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Wang SY, Gao JC, Wu SD. Artificial intelligence for reducing missed detection of adenomas and polyps in colonoscopy: A systematic review and meta-analysis. World J Gastroenterol 2025; 31:105753. [DOI: 10.3748/wjg.v31.i21.105753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2025] [Revised: 03/21/2025] [Accepted: 05/19/2025] [Indexed: 06/06/2025] Open
Abstract
BACKGROUND Colorectal cancer has a high incidence and mortality rate, and the effectiveness of routine colonoscopy largely depends on the endoscopist’s expertise. In recent years, computer-aided detection (CADe) systems have been increasingly integrated into colonoscopy to improve detection accuracy. However, while most studies have focused on adenoma detection rate (ADR) as the primary outcome, the more sensitive adenoma miss rate (AMR) has been less frequently analyzed.
AIM To evaluate the effectiveness of CADe in colonoscopy and assess the advantages of AMR over ADR.
METHODS A comprehensive literature search was conducted in PubMed, Embase, and the Cochrane Central Register of Controlled Trials using predefined search strategies to identify relevant studies published up to August 2, 2024. Statistical analyses were performed to compare outcomes between groups, and potential publication bias was assessed using funnel plots. The quality of the included studies was evaluated using the Cochrane Risk of Bias tool and the Grading of Recommendations, Assessment, Development, and Evaluation approach.
RESULTS Five studies comprising 1624 patients met the inclusion criteria. AMR was significantly lower in the CADe-assisted group than in the routine colonoscopy group (147/927, 15.9% vs 345/960, 35.9%; P < 0.01). However, CADe did not provide a significant advantage in detecting advanced adenomas or lesions measuring 6-9 mm or ≥ 10 mm. The polyp miss rate (PMR) was also lower in the CADe-assisted group [odds ratio (OR), 0.35; 95% confidence interval (CI): 0.23-0.52; P < 0.01]. While the overall ADR did not differ significantly between groups, the ADR during the first-pass examination was higher in the CADe-assisted group (OR, 1.37; 95%CI: 1.10-1.69; P = 0.004). The level of evidence for the included randomized controlled trials was graded as moderate.
CONCLUSION CADe can significantly reduce AMR and PMR while improving ADR during initial detection, demonstrating its potential to enhance colonoscopy performance. These findings highlight the value of CADe in improving the detection of colorectal neoplasms, particularly small and histologically distinct adenomas.
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Affiliation(s)
- Sheng-Yu Wang
- The Second Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang 110004, Liaoning Province, China
| | - Jia-Cheng Gao
- Department of Orthopedic Surgery, The First Hospital of China Medical University, Shenyang 110001, Liaoning Province, China
| | - Shuo-Dong Wu
- Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang 110004, Liaoning Province, China
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Spadaccini M, Hassan C, Mori Y, Halvorsen N, Gimeno-García AZ, Nakashima H, Facciorusso A, Patel HK, Antonelli G, Khalaf K, Rizkala T, Ramai D, Rondonotti E, Kamba S, Maselli R, Correale L, Bretthauer M, Bhandari P, Sharma P, Rex DK, Repici A. Artificial intelligence and colorectal neoplasia detection performances in patients with positive fecal immunochemical test: Meta-analysis and systematic review. Dig Endosc 2025. [PMID: 40328639 DOI: 10.1111/den.15034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2024] [Accepted: 03/17/2025] [Indexed: 05/08/2025]
Abstract
OBJECTIVES The combination of fecal immunochemical test (FIT) followed by colonoscopy has established itself as one of the preferred population-based screening strategies. Despite extensive exploration of various techniques and technologies, their impact on adenoma detection rate has shown inconsistency across studies in this specific setting "FIT+ population." We aimed to assess the impact of the computer-aided detection (CADe) system in all randomized trials focused on this subpopulation. METHODS We searched MEDLINE, EMBASE, and Scopus databases until September 2023 for randomized controlled trials reporting diagnostic accuracy of CADe systems for detection of colorectal neoplasia. The primary outcome was pooled adenoma detection rate, and secondary outcomes were adenoma per colonoscopy, advanced adenoma per colonoscopy, serrated lesions, and nonneoplastic per colonoscopy. RESULTS Ten randomized trials on 5421 patients were included. Adenoma detection rate was higher in the CADe group than in the standard colonoscopy group (0.62 vs. 0.52; relative risk 1.19; 95% confidence interval 1.08-1.31). CADe also resulted in higher detection performances of both adenomas (incidence rate ratio 1.16; 95% confidence interval 1.09-1.24) and serrated lesions (incidence rate ratio, 1.20; 95% confidence interval 1.05-1.38) at per-polyp analysis. No differences were found for advanced adenomas between the groups. On the other hand, more nonneoplastic polyps were removed in the CADe than the standard group (0.45 vs. 0.34; mean difference 0.06; P = 0.026) in a comparable inspection time. CONCLUSIONS The use of CADe during colonoscopy results in an increased detection of adenomas, and serrated lesions, in a FIT+ setting. The impact on advanced adenomas was not significant. Higher rates of unnecessary removal of nonneoplastic polyps were also reported.
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Affiliation(s)
- Marco Spadaccini
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Endoscopy Unit, Humanitas Clinical Center - IRCCS, Rozzano, Italy
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Endoscopy Unit, Humanitas Clinical Center - IRCCS, Rozzano, Italy
| | - Yuichi Mori
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Natalie Halvorsen
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway
| | - Antonio Z Gimeno-García
- Servicio de Gastroenterología, Hospital Universitario de Canarias, Instituto Universitario de Tecnologías Biomédicas (ITB), Tenerife, Spain
- Departamento de Medicina Interna, Centro de Investigación Biomédica de Canarias (CIBICAN), Universidad de La Laguna, Tenerife, Spain
| | - Hirotaka Nakashima
- Department of Gastroenterology, Foundation for Detection of Early Gastric Carcinoma, Tokyo, Japan
| | - Antonio Facciorusso
- Department of Medical Sciences, Section of Gastroenterology, University of Foggia, Foggia, Italy
| | - Harsh K Patel
- Department of Gastroenterology and Hepatology, Kansas City VA Medical Center, Kansas City, Kansas, USA
| | - Giulio Antonelli
- Gastroenterology and Digestive Endoscopy Unit, Ospedale dei Castelli, Rome, Italy
- Department of Anatomical, Histological, Forensic Medicine and Orthopedics Sciences, "Sapienza" University of Rome, Rome, Italy
| | - Kareem Khalaf
- The Center for Therapeutic Endoscopy and Endoscopic Oncology, St. Michael's Hospital, University of Toronto, Toronto, Canada
| | - Tommy Rizkala
- Endoscopy Unit, Humanitas Clinical Center - IRCCS, Rozzano, Italy
| | - Daryl Ramai
- Department of Gastroenterology and Hepatology, University of Utah Health, Salt Lake City, Utah, USA
| | | | - Shunsuke Kamba
- Department of Endoscopy, The Jikei University School of Medicine, Tokyo, Japan
| | - Roberta Maselli
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Endoscopy Unit, Humanitas Clinical Center - IRCCS, Rozzano, Italy
| | | | | | - Pradeep Bhandari
- Department of Gastroenterology, Queen Alexandra Hospital, Portsmouth, UK
| | - Prateek Sharma
- Department of Gastroenterology and Hepatology, Kansas City VA Medical Center, Kansas City, Kansas, USA
| | - Douglas K Rex
- Division of Gastroenterology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Alessandro Repici
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Endoscopy Unit, Humanitas Clinical Center - IRCCS, Rozzano, Italy
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Jahn B, Bundo M, Arvandi M, Schaffner M, Todorovic J, Sroczynski G, Knudsen A, Fischer T, Schiller-Fruehwirth I, Öfner D, Renner F, Jonas M, Kuchin I, Kruse J, Santamaria J, Ferlitsch M, Siebert U. One in three adenomas could be missed by white-light colonoscopy - findings from a systematic review and meta-analysis. BMC Gastroenterol 2025; 25:170. [PMID: 40082770 PMCID: PMC11908064 DOI: 10.1186/s12876-025-03679-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2024] [Accepted: 02/11/2025] [Indexed: 03/16/2025] Open
Abstract
BACKGROUND White light (conventional) colonoscopy (WLC) is widely used for colorectal cancer screening, diagnosis and surveillance but endoscopists may fail to detect adenomas. Our goal was to assess and synthesize overall and subgroup-specific adenoma miss rates (AMR) of WLC in daily practice. METHODS We conducted a systematic review in MEDLINE, EMBASE, Cochrane Library, and grey literature on studies evaluating diagnostic WLC accuracy in tandem studies with novel-colonoscopic technologies (NCT) in subjects undergoing screening, diagnostic or surveillance colonoscopy. Information on study design, AMR overall and specific for adenoma size, histology, location, morphology and further outcomes were extracted and reported in standardized evidence tables. Study quality was assessed using the QUADAS-2 tool. Random-effects meta-analyses and meta-regression were performed to estimate pooled estimates for AMR with 95% confidence intervals (95% CI) and to explain heterogeneity. RESULTS Out of 5,963 identified studies, we included sixteen studies with 4,101 individuals in our meta-analysis. One in three adenomas (34%; 95% CI: 30-38%) was missed by WLC in daily practice individuals. Subgroup analyses showed significant AMR differences by size (36%, adenomas 1-5 mm; 27%, adenomas 6-9 mm; 12%, adenomas ≥ 10 mm), histology (non-advanced: 42%, advanced: 21%), morphology (flat: 50%, polypoid: 27%), but not by location (distal: 36%, proximal: 36%). CONCLUSIONS Based on our meta-analysis, one in three adenomas could be missed by WLC. This may significantly contribute to interval cancers. Our results should be considered in health technology assessment when interpreting sensitivity of fecal occult blood or other screening tests derived from studies using WLC as "gold standard".
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Affiliation(s)
- Beate Jahn
- Department of Public Health, Health Services Research and Health Technology Assessment, UMIT TIROL - University for Health Sciences and Technology, Hall in Tirol, Austria
| | - Marvin Bundo
- Department of Public Health, Health Services Research and Health Technology Assessment, UMIT TIROL - University for Health Sciences and Technology, Hall in Tirol, Austria
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Oeschger Center for Climate Change Research, University of Bern, Bern, Switzerland
| | - Marjan Arvandi
- Department of Public Health, Health Services Research and Health Technology Assessment, UMIT TIROL - University for Health Sciences and Technology, Hall in Tirol, Austria
| | - Monika Schaffner
- Department of Public Health, Health Services Research and Health Technology Assessment, UMIT TIROL - University for Health Sciences and Technology, Hall in Tirol, Austria
| | - Jovan Todorovic
- Department of Public Health, Health Services Research and Health Technology Assessment, UMIT TIROL - University for Health Sciences and Technology, Hall in Tirol, Austria
| | - Gaby Sroczynski
- Department of Public Health, Health Services Research and Health Technology Assessment, UMIT TIROL - University for Health Sciences and Technology, Hall in Tirol, Austria
| | - Amy Knudsen
- Institute for Technology Assessment, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Timo Fischer
- Main Association of Austrian Social Security Institutions, Vienna, Austria
| | | | - Dietmar Öfner
- Department of Visceral, Transplant and Thoracic Surgery, Medical University of Innsbruck, Innsbruck, Austria
| | | | - Michael Jonas
- Medical Association of Vorarlberg, Dornbirn, Austria
| | - Igor Kuchin
- Department of Public Health, Health Services Research and Health Technology Assessment, UMIT TIROL - University for Health Sciences and Technology, Hall in Tirol, Austria
| | - Julia Kruse
- Department of Public Health, Health Services Research and Health Technology Assessment, UMIT TIROL - University for Health Sciences and Technology, Hall in Tirol, Austria
| | - Júlia Santamaria
- Department of Public Health, Health Services Research and Health Technology Assessment, UMIT TIROL - University for Health Sciences and Technology, Hall in Tirol, Austria
| | - Monika Ferlitsch
- Department of Internal Medicine III, Division of Gastroenterology and Hepatology, Medical University of Vienna, Vienna, Austria
| | - Uwe Siebert
- Department of Public Health, Health Services Research and Health Technology Assessment, UMIT TIROL - University for Health Sciences and Technology, Hall in Tirol, Austria.
- Institute for Technology Assessment, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Division of Health Technology Assessment and Bioinformatics, ONCOTYROL - Center for Personalized Cancer Medicine, Innsbruck, Austria.
- Center for Health Decision Science, Departments of Epidemiology and Health Policy & Management, Harvard T. H. Chan School of Public Health, Boston, USA.
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Nakao E, Yoshio T, Kato Y, Namikawa K, Tokai Y, Yoshimizu S, Horiuchi Y, Ishiyama A, Hirasawa T, Kurihara N, Ishizuka N, Ishihara R, Tada T, Fujisaki J. Randomized controlled trial of an artificial intelligence diagnostic system for the detection of esophageal squamous cell carcinoma in clinical practice. Endoscopy 2025; 57:210-217. [PMID: 39317205 DOI: 10.1055/a-2421-3194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/26/2024]
Abstract
BACKGROUND Artificial intelligence (AI) has made remarkable progress in image recognition using deep learning systems. It has been used to detect esophageal squamous cell carcinoma (ESCC); however, none of the previous reports were investigations in a clinical setting, being retrospective in design. We therefore conducted this trial to determine how AI can help endoscopists detect ESCC in a clinical setting. METHODS This was a prospective, single-center, exploratory, and randomized controlled trial. High risk patients with ESCC undergoing screening or surveillance esophagogastroduodenoscopy were enrolled and randomly assigned to either the AI or control groups. In the AI group, the endoscopists watched both the AI monitor that detected ESCC with annotation and the normal monitor simultaneously; in the control group, the endoscopists watched only the normal monitor. In both groups, the endoscopists observed the esophagus using white-light imaging (WLI), followed by narrow-band imaging (NBI), then iodine staining. The primary end point was the enhanced detection rate of ESCC by nonexperts using AI. The detection rate was defined as the ratio of WLI/NBI-detected ESCCs to all ESCCs detected by iodine staining. RESULTS 320 patients were included in the analysis. The detection rate of ESCC among nonexperts was 47% in the AI group and 45% in the control group (P = 0.93), with no significant difference, which was similarly found for experts (87% vs. 57%; P = 0.20) and all endoscopists (57% vs. 50%; P = 0.70). CONCLUSIONS This study could not demonstrate an improvement in the esophageal cancer detection rate using the AI diagnostic support system for ESCC.
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Affiliation(s)
- Eisuke Nakao
- Gastroenterology, Cancer Institute Hospital, Japanese Foundation For Cancer Research, Tokyo, Japan
| | - Toshiyuki Yoshio
- Gastroenterology, Cancer Institute Hospital, Japanese Foundation For Cancer Research, Tokyo, Japan
| | - Yusuke Kato
- AI Medical Service, AI Medical Service Inc., Tokyo, Japan
| | - Ken Namikawa
- Gastroenterology, Cancer Institute Hospital, Japanese Foundation For Cancer Research, Tokyo, Japan
| | - Yoshitaka Tokai
- Gastroenterology, Cancer Institute Hospital, Japanese Foundation For Cancer Research, Tokyo, Japan
| | - Shoichi Yoshimizu
- Gastroenterology, Cancer Institute Hospital, Japanese Foundation For Cancer Research, Tokyo, Japan
| | - Yusuke Horiuchi
- Gastroenterology, Cancer Institute Hospital, Japanese Foundation For Cancer Research, Tokyo, Japan
| | - Akiyoshi Ishiyama
- Gastroenterology, Cancer Institute Hospital, Japanese Foundation For Cancer Research, Tokyo, Japan
| | - Toshiaki Hirasawa
- Gastroenterology, Cancer Institute Hospital, Japanese Foundation For Cancer Research, Tokyo, Japan
| | - Nozomi Kurihara
- Clinical Planning and Strategy, Cancer Institute Hospital, Japanese Foundation For Cancer Research, Tokyo, Japan
| | - Naoki Ishizuka
- Clinical Planning and Strategy, Cancer Institute Hospital, Japanese Foundation For Cancer Research, Tokyo, Japan
- Center for Digital Transformation for Health, Kyoto University School of Medicine, Kyoto, Japan
| | - Ryu Ishihara
- Gastrointestinal Oncology, Osaka International Cancer Institute., Osaka, Japan
| | - Tomohiro Tada
- AI Medical Service, AI Medical Service Inc., Tokyo, Japan
- Gastroenterology and Proctology, Tada Tomohiro Institute of Gastroenterology and Proctology, Saitama, Japan
- Surgical Oncology, University of Tokyo Graduate School of Medicine, Tokyo, Japan
| | - Junko Fujisaki
- Gastroenterology, Cancer Institute Hospital, Japanese Foundation For Cancer Research, Tokyo, Japan
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Lagström RMB, Bräuner KB, Bielik J, Rosen AW, Crone JG, Gögenur I, Bulut M. Improvement in adenoma detection rate by artificial intelligence-assisted colonoscopy: Multicenter quasi-randomized controlled trial. Endosc Int Open 2025; 13:a25215169. [PMID: 40018072 PMCID: PMC11866038 DOI: 10.1055/a-2521-5169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2024] [Accepted: 01/15/2025] [Indexed: 03/01/2025] Open
Abstract
Background and study aims Adenoma detection rate (ADR) is a key performance measure with variability among endoscopists. Artificial intelligence (AI) in colonoscopy could reduce this variability and has shown to improve ADR. This study assessed the impact of AI on ADR among Danish endoscopists of varying experience levels. Patients and methods We conducted a prospective, quasi-randomized, controlled, multicenter trial involving patients aged 18 and older undergoing screening, surveillance, and diagnostic colonoscopy at four centers. Participants were assigned to AI-assisted colonoscopy (GI Genius, Medtronic) or conventional colonoscopy. Endoscopists were classified as experts (> 1000 colonoscopies) or non-experts (≤ 1000 colonoscopies). The primary outcome was ADR. We performed a subgroup analysis stratified on endoscopist experience and a subset analysis of the screening population. Results A total of 795 patients were analyzed: 400 in the AI group and 395 in the control group. The AI group demonstrated a significantly higher ADR than the control group (59.1% vs. 46.6%, P < 0.001). The increase was significant among experts (59.9% vs. 47.3%, P < 0.002) but not among non-experts. AI assistance significantly improved ADR (74.4% vs. 58.1%, P = 0.003) in screening colonoscopies. Polyp detection rate (PDR) was also higher in the AI group (69.8% vs. 56.2%, P < 0.001). There was no significant difference in the non-neoplastic resection rate (NNRR) (15.1% vs. 17.1%, P = 0.542). Conclusions AI-assisted colonoscopy significantly increased ADR by 12.5% overall, with a notable 16.3% increase in the screening population. The unchanged NNRR indicates that the higher PDR was due to increased ADR, not unnecessary resections.
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Affiliation(s)
| | - Karoline Bendix Bräuner
- Department of Surgery, Zealand University Hospital Koge, Køge, Denmark
- Department of Surgery, Slagelse Hospital, Slagelse, Denmark
| | - Julia Bielik
- Department of Surgery, Holbæk Sygehus, Holbæk, Denmark
| | | | | | - Ismail Gögenur
- Department of Surgery, Zealand University Hospital Koge, Køge, Denmark
- Department of Clinical Medicine, University of Copenhagen Faculty of Health and Medical Sciences, Copenhagen, Denmark
| | - Mustafa Bulut
- Department of Surgery, Zealand University Hospital Koge, Køge, Denmark
- Department of Clinical Medicine, University of Copenhagen Faculty of Health and Medical Sciences, Copenhagen, Denmark
- Copenhagen Academy for Medical Education and Simulation (CAMES), Copenhagen, Denmark
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Spadaccini M, Hassan C, Mori Y, Massimi D, Correale L, Facciorusso A, Patel HK, Rizkala T, Khalaf K, Ramai D, Rondonotti E, Maselli R, Rex DK, Bhandari P, Sharma P, Repici A. Variability in computer-aided detection effect on adenoma detection rate in randomized controlled trials: A meta-regression analysis. Dig Liver Dis 2025:S1590-8658(25)00205-1. [PMID: 39924430 DOI: 10.1016/j.dld.2025.01.192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2024] [Revised: 12/16/2024] [Accepted: 01/21/2025] [Indexed: 02/11/2025]
Abstract
BACKGROUND Computer-aided detection (CADe) systems may increase adenoma detection rate (ADR) during colonoscopy. However, the variable results of CADe effects in different RCTs warrant investigation into factors influencing these results. AIMS Investigate the different variables possibly affecting the impact of CADe-assisted colonoscopy and its effect on ADR. METHODS We searched MEDLINE, EMBASE, and Scopus databases until July 2023 for RCTs reporting performance of CADe systems in the detection of colorectal neoplasia. The main outcome was pooled ADR. A random-effects meta-analysis was performed to obtain the pooled risk ratios (RR) with 95 % confidence intervals (CI)). To explore sources of heterogeneity, we conducted a meta-regression analysis using both univariable and multivariable mixed-effects models. Potential explanatory variables included factors influencing adenoma prevalence, such as patient gender, age, and colonoscopy indication. We also included both key (ADR), and minor (Withdrawal time) performance measures considered as quality indicators for colonoscopy. RESULTS Twenty-three randomized controlled trials (RCTs) on 19,077 patients were include. ADR was higher in the CADe group (46 % [95 % CI 39-52]) than in the standard colonoscopy group (38 % [95 % CI 31-46]) with a risk ratio of 1.22 [95 % CI 1.14-1.29]); and a substantial level of heterogeneity (I2 = 67.69 %). In the univariable meta-regression analysis, patient age, ADR in control arms, and withdrawal time were the strongest predictors of CADe effect on ADR (P < .001). In multivariable meta-regression, ADR in control arms, and withdrawal time were simultaneous significant predictors of the proportion of the CADe effect on ADR. CONCLUSION The substantial level of heterogeneity found appeared to be associated with variability in colonoscopy quality performances across the studies, namely ADR in control arm, and withdrawal time.
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Affiliation(s)
- Marco Spadaccini
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy; Humanitas Clinical and Research Center -IRCCS-, Endoscopy Unit, Rozzano, Italy.
| | - Cesare Hassan
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy; Humanitas Clinical and Research Center -IRCCS-, Endoscopy Unit, Rozzano, Italy
| | - Yuichi Mori
- University of Oslo, Clinical Effectiveness Research Group, Oslo, Norway; Showa University Northern Yokohama Hospital, Digestive Disease Center, Yokohama, Japan
| | - Davide Massimi
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy; Humanitas Clinical and Research Center -IRCCS-, Endoscopy Unit, Rozzano, Italy
| | - Loredana Correale
- Humanitas Clinical and Research Center -IRCCS-, Endoscopy Unit, Rozzano, Italy
| | - Antonio Facciorusso
- University of Oslo, Clinical Effectiveness Research Group, Oslo, Norway; University of Salento, Gastroenterology Unit, Department of Experimental Medicine, Lecce, Italy
| | - Harsh K Patel
- Kansas City VA Medical Center, Gastroenterology and Hepatology, Kansas City, United States
| | - Tommy Rizkala
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy
| | - Kareem Khalaf
- St. Michael's Hospital, University of Toronto, Division of Gastroenterology, Toronto, Ontario, Canada
| | - Daryl Ramai
- University of Utah Health, Gastroenterology and Hepatology, Salt Lake City, UT, USA
| | | | - Roberta Maselli
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy; Humanitas Clinical and Research Center -IRCCS-, Endoscopy Unit, Rozzano, Italy
| | - Douglas K Rex
- Indiana University School of Medicine, Division of Gastroenterology, Indianapolis, Indiana, USA
| | - Pradeep Bhandari
- Queen Alexandra Hospital, Department of Gastroenterology, Portsmouth, UK
| | - Prateek Sharma
- Kansas City VA Medical Center, Gastroenterology and Hepatology, Kansas City, United States
| | - Alessandro Repici
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy; Humanitas Clinical and Research Center -IRCCS-, Endoscopy Unit, Rozzano, Italy
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7
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Duan C, Sheng J, Ma X. Innovative approaches in colorectal cancer screening: advances in detection methods and the role of artificial intelligence. Therap Adv Gastroenterol 2025; 18:17562848251314829. [PMID: 39898356 PMCID: PMC11783499 DOI: 10.1177/17562848251314829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2024] [Accepted: 01/06/2025] [Indexed: 02/04/2025] Open
Abstract
Colorectal cancer (CRC) is the third most prevalent cancer globally and poses a significant health threat, making early detection crucial. This review paper explored emerging detection methods for early screening of CRC, including gut microbiota, metabolites, genetic markers, and artificial intelligence (AI)-based technologies. Current screening methods have their respective advantages and limitations, particularly in detecting precursors. First, the importance of the gut microbiome in CRC progression is discussed, highlighting how specific microbial alterations can serve as biomarkers for early detection, potentially enhancing diagnostic accuracy when combined with traditional screening methods. Next, research on metabolic reprogramming illustrates the relationship between metabolic changes and CRC, with studies developing metabolite-based detection models that show good sensitivity for early diagnosis. In terms of genetic markers, methylated DNA markers like SEPTIN9 have demonstrated high sensitivity, although further validation across diverse populations is necessary. Lastly, AI technology has shown immense potential in improving adenoma detection rates, significantly enhancing the quality of colonoscopic examinations through image recognition techniques. This review aims to provide a comprehensive perspective on new strategies for CRC screening, emphasizing the potential of noninvasive detection technologies and the prospects of AI and genomics in clinical applications. Despite several challenges, this review advocates for future large-scale prospective studies to validate the effectiveness and cost-effectiveness of these new screening methods while promoting the implementation of screening protocols tailored to individual characteristics.
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Affiliation(s)
- Changwei Duan
- Medical School of Chinese PLA, Beijing, China Senior Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, China
- Department of Gastroenterology, The Seventh Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Jianqiu Sheng
- Medical School of Chinese PLA, Beijing 100853, China Senior Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing 100853, China
- Department of Gastroenterology, The Seventh Medical Center of Chinese PLA General Hospital, No. 5 Nanmencang, Beijing 100700, China
| | - Xianzong Ma
- Senior Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing 100853, China
- Department of Gastroenterology, The Seventh Medical Center of Chinese PLA General Hospital, No. 28 Fuxing Road, Beijing 100700, China
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Liu J, Zhou R, Liu C, Liu H, Cui Z, Guo Z, Zhao W, Zhong X, Zhang X, Li J, Wang S, Xing L, Zhao Y, Ma R, Ni J, Li Z, Li Y, Zuo X. Automatic Quality Control System and Adenoma Detection Rates During Routine Colonoscopy: A Randomized Clinical Trial. JAMA Netw Open 2025; 8:e2457241. [PMID: 39883463 PMCID: PMC11783196 DOI: 10.1001/jamanetworkopen.2024.57241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2024] [Accepted: 11/25/2024] [Indexed: 01/31/2025] Open
Abstract
Importance High-quality colonoscopy reduces the risks of colorectal cancer by increasing the adenoma detection rate. Routine use of an automatic quality control system (AQCS) to assist in colorectal adenoma detection should be considered. Objective To evaluate the effect of an AQCS on the adenoma detection rate among colonoscopists who were moderate- and low-level detectors during routine colonoscopy. Design, Setting, and Participants This multicenter, single-blind, randomized clinical trial was conducted at 6 centers in China from August 1, 2021, to September 30, 2022. Data were analyzed from March 1 to June 30, 2023. Individuals aged 18 to 80 years were enrolled. Exclusion criteria were a history of inflammatory bowel disease, advanced colorectal cancer, and polyposis syndromes; known colorectal polyps without complete removal previously; a history of colorectal surgery; known stenosis or obstruction with contraindication for biopsy or prior failed colonoscopy; pregnancy or lactation; and refusal to participate. Intention-to-treat and per-protocol analysis was used. Interventions Standard colonoscopy or AQCS-assisted colonoscopy. Main Outcomes and Measures Adenoma detection rate. Results A total of 1254 participants (mean [SD] age, 51.21 [12.10] years; 674 [53.7%] male) were randomized (627 standard colonoscopy, 627 AQCS-assisted colonoscopy). Intention-to-treat analysis showed a significantly higher adenoma detection rate in the AQCS-assisted group vs standard colonoscopy group (32.7% vs 22.6%; relative risk [RR], 1.60; 95% CI, 1.23-2.09; P < .001). The adenoma detection rates were significantly higher in the AQCS group when considering pathology (nonadvanced adenomas, 30.1% vs 21.2%; RR, 1.52; 95% CI, 1.16-1.99; P = .002), and morphology (flat or sessile, 29.3% vs 20.4%, RR, 1.52; 95% CI, 1.16-2.00; P = .003). Use of AQCS significantly increased the adenoma detection rate of both the lower-level detectors (30.0% vs 20.0%; RR, 1.71; 95% CI, 1.24-2.35; P = .001) and the medium-level detectors (38.1% vs 27.7%; RR, 1.61; 95% CI, 1.07-2.43; P = .02). Similar increases were found for adenoma detection rates in the academic and nonacademic centers (academic: 29.3% vs 20.8%; RR, 1.58; 95% CI, 1.10-2.29; P = .01; nonacademic: 36.1% vs 24.5%; RR, 1.74; 95% CI, 1.23-2.46; P = .002). The number of adenomas per colonoscopy was significantly higher in the AQCS-assisted group (0.86 vs 0.48; RR, 1.50; 95% CI, 1.17-1.91; P = .001). The mean withdrawal time without intervention was slightly increased with AQCS assistance (6.78 vs 6.46 minutes; RR, 1.38; 95% CI, 1.26-1.52; P < .001). No serious adverse events were reported. Conclusions and Relevance In this randomized clinical trial, AQCS assistance during routine colonoscopy increased adenoma detection rates and several related polyp parameters compared with standard colonoscopy in the lower- and medium-level detectors in academic and nonacademic settings. Routine use of AQCS to assist in colorectal adenoma detection and quality improvement should be considered. Trial Registration ClinicalTrials.gov Identifier: NCT04901130.
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Affiliation(s)
- Jing Liu
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China
- Shandong Provincial Clinical Research Center for Digestive Disease, Jinan, Shandong, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China
- Department of Gastroenterology, Qilu Hospital of Shandong University, Qingdao, Shandong, China
| | - Ruchen Zhou
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China
- Shandong Provincial Clinical Research Center for Digestive Disease, Jinan, Shandong, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Chengxia Liu
- Department of Gastroenterology, Binzhou Medical University Hospital, Binzhou, Shandong, China
| | - Haiyan Liu
- Department of Gastroenterology, Binzhou Medical University Hospital, Binzhou, Shandong, China
- Department of Gastroenterology, The First School of Clinical Medicine of Binzhou Medical University, Binzhou, Shandong, China
| | - Zhenqin Cui
- Department of Gastroenterology, Central Hospital of Shengli Oilfield, Dongying, Shandong, China
| | - Zhuang Guo
- Department of Gastroenterology, Central Hospital of Shengli Oilfield, Dongying, Shandong, China
| | - Weidong Zhao
- Department of Gastroenterology, Zibo Municipal Hospital, Zibo, Shandong, China
| | - Xiaoqin Zhong
- Department of Gastroenterology, Zibo Municipal Hospital, Zibo, Shandong, China
| | - Xiaodong Zhang
- Department of Gastroenterology, Linyi People’s Hospital, Dezhou, Shandong, China
| | - Jing Li
- Department of Gastroenterology, Linyi People’s Hospital, Dezhou, Shandong, China
| | - Shihuan Wang
- Department of Gastroenterology, The People’s Hospital of Zhaoyuan City, Yantai, Shandong, China
| | - Li Xing
- Department of Gastroenterology, The People’s Hospital of Zhaoyuan City, Yantai, Shandong, China
| | - Yusha Zhao
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China
- Shandong Provincial Clinical Research Center for Digestive Disease, Jinan, Shandong, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Ruiguang Ma
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China
- Shandong Provincial Clinical Research Center for Digestive Disease, Jinan, Shandong, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Jiekun Ni
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China
- Shandong Provincial Clinical Research Center for Digestive Disease, Jinan, Shandong, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Zhen Li
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China
- Shandong Provincial Clinical Research Center for Digestive Disease, Jinan, Shandong, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Yanqing Li
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China
- Shandong Provincial Clinical Research Center for Digestive Disease, Jinan, Shandong, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Xiuli Zuo
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China
- Shandong Provincial Clinical Research Center for Digestive Disease, Jinan, Shandong, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China
- Department of Gastroenterology, Qilu Hospital of Shandong University, Qingdao, Shandong, China
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9
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Maida M, Marasco G, Maas MHJ, Ramai D, Spadaccini M, Sinagra E, Facciorusso A, Siersema PD, Hassan C. Effectiveness of artificial intelligence assisted colonoscopy on adenoma and polyp miss rate: A meta-analysis of tandem RCTs. Dig Liver Dis 2025; 57:169-175. [PMID: 39322447 DOI: 10.1016/j.dld.2024.09.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2024] [Revised: 08/20/2024] [Accepted: 09/01/2024] [Indexed: 09/27/2024]
Abstract
BACKGROUND AND AIMS One-fourth of colorectal neoplasia is missed at screening colonoscopy, representing the leading cause of interval colorectal cancer (I-CRC). This systematic review and meta-analysis summarizes the efficacy of computer-aided colonoscopy (CAC) compared to white-light colonoscopy (WLC) in reducing lesion miss rates. METHODS Major databases were systematically searched through May 2024 for tandem-design RCTs comparing lesion miss rates in CAC-first followed by WLC vs WLC-first followed by CAC. The primary outcomes were adenoma miss rate (AMR) and polyp miss rate (PMR). The secondary outcomes were advanced AMR (aAMR) and sessile serrated lesion miss rate (SMR). RESULTS Six RCTs (1718 patients) were included. AMR was significantly lower for CAC compared to WLC (RR = 0.46; 95 %CI [0.38-0.55]; P < 0.001). PMR was also lower for CAC compared to WLC (RR = 0.44; 95 %CI [0.33-0.60]; P < 0.001). No significant difference in aAMR (RR = 1.28; 95 %CI [0.34-4.83]; P = 0.71) and SMR (RR = 0.44; 95 %CI [0.15-1.28]; P = 0.13) were observed. Sensitivity analysis including only RCTs performed in CRC screening and surveillance setting confirmed lower AMR (RR = 0.48; 95 %CI [0.39-0.58]; P < 0.001) and PMR (RR = 0.50; 95 %CI [0.37-0.66]; P < 0.001), also showing significantly lower SMR (RR = 0.28; 95 %CI [0.11-0.70]; P = 0.007) for CAC compared to WLC. CONCLUSIONS CAC results in significantly lower AMR and PMR compared to WLC overall, and significantly lower AMR, PMR and SMR in the screening/surveillance setting, potentially reducing the incidence of I-CRC.
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Affiliation(s)
- M Maida
- Department of Medicine and Surgery, University of Enna 'Kore', Enna, Italy; Gastroenterology Unit, Umberto I Hospital, Enna, Italy.
| | - G Marasco
- Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy; IRCCS Azienda Ospedaliera Universitaria di Bologna, Bologna, Italy
| | - M H J Maas
- Department of Gastroenterology & Hepatology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - D Ramai
- Division of Gastroenterology, Hepatology and Endoscopy, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - M Spadaccini
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy; Endoscopy Unit, Humanitas Clinical and Research Hospital, IRCCS, Rozzano, Italy
| | - E Sinagra
- Gastroenterology Unit, Fondazione Istituto San Raffaele Giglio, Cefalù, Italy
| | - A Facciorusso
- Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy
| | - P D Siersema
- Depatment of Gastroenterology and Hepatology, Erasmus MC - University Medical Center, Rotterdam, the Netherlands
| | - C Hassan
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy; Endoscopy Unit, Humanitas Clinical and Research Hospital, IRCCS, Rozzano, Italy
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10
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Makar J, Abdelmalak J, Con D, Hafeez B, Garg M. Use of artificial intelligence improves colonoscopy performance in adenoma detection: a systematic review and meta-analysis. Gastrointest Endosc 2025; 101:68-81.e8. [PMID: 39216648 DOI: 10.1016/j.gie.2024.08.033] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Revised: 08/17/2024] [Accepted: 08/27/2024] [Indexed: 09/04/2024]
Abstract
BACKGROUND AND AIMS Artificial intelligence (AI) is increasingly used to improve adenoma detection during colonoscopy. This meta-analysis aimed to provide an updated evaluation of computer-aided detection (CADe) systems and their impact on key colonoscopy quality indicators. METHODS We searched the EMBASE, PubMed, and MEDLINE databases from inception until February 15, 2024, for randomized control trials (RCTs) comparing the performance of CADe systems with routine unassisted colonoscopy in the detection of colorectal adenomas. RESULTS Twenty-eight RCTs were selected for inclusion involving 23,861 participants. Random-effects meta-analysis demonstrated a 20% increase in adenoma detection rate (risk ratio [RR], 1.20; 95% confidence interval [CI], 1.14-1.27; P < .01) and 55% decrease in adenoma miss rate (RR, 0.45; 95% CI, 0.37-0.54; P < .01) with AI-assisted colonoscopy. Subgroup analyses involving only expert endoscopists demonstrated a similar effect size (RR, 1.19; 95% CI, 1.11-1.27; P < .001), with similar findings seen in analysis of differing CADe systems and healthcare settings. CADe use also significantly increased adenomas per colonoscopy (weighted mean difference, 0.21; 95% CI, 0.14-0.29; P < .01), primarily because of increased diminutive lesion detection, with no significant difference seen in detection of advanced adenomas. Sessile serrated lesion detection (RR, 1.10; 95% CI, 0.93-1.30; P = .27) and miss rates (RR, 0.44; 95% CI, 0.16-1.19; P = .11) were similar. There was an average 0.15-minute prolongation of withdrawal time with AI-assisted colonoscopy (weighted mean difference, 0.15; 95% CI, 0.04-0.25; P = .01) and a 39% increase in the rate of non-neoplastic resection (RR, 1.39; 95% CI, 1.23-1.57; P < .001). CONCLUSIONS AI-assisted colonoscopy significantly improved adenoma detection but not sessile serrated lesion detection irrespective of endoscopist experience, system type, or healthcare setting.
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Affiliation(s)
- Jonathan Makar
- Department of Medicine, The University of Melbourne, Melbourne, Victoria, Australia
| | - Jonathan Abdelmalak
- Department of Gastroenterology, Austin Hospital, Heidelberg, Victoria, Australia; Department of Gastroenterology, Alfred Hospital, Melbourne, Victoria, Australia; Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Danny Con
- Department of Medicine, The University of Melbourne, Melbourne, Victoria, Australia; Department of Gastroenterology, Austin Hospital, Heidelberg, Victoria, Australia
| | - Bilal Hafeez
- Department of Medicine, The University of Melbourne, Melbourne, Victoria, Australia
| | - Mayur Garg
- Department of Medicine, The University of Melbourne, Melbourne, Victoria, Australia; Department of Gastroenterology, Northern Health, Epping, Victoria, Australia
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11
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Misawa M, Kudo SE. Current Status of Artificial Intelligence Use in Colonoscopy. Digestion 2024; 106:138-145. [PMID: 39724867 DOI: 10.1159/000543345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2024] [Accepted: 12/24/2024] [Indexed: 12/28/2024]
Abstract
BACKGROUND Artificial intelligence (AI) has significantly impacted medical imaging, particularly in gastrointestinal endoscopy. Computer-aided detection and diagnosis systems (CADe and CADx) are thought to enhance the quality of colonoscopy procedures. SUMMARY Colonoscopy is essential for colorectal cancer screening but often misses a significant percentage of adenomas. AI-assisted systems employing deep learning offer improved detection and differentiation of colorectal polyps, potentially increasing adenoma detection rates by 8%-10%. The main benefit of CADe is in detecting small adenomas, whereas it has a limited impact on advanced neoplasm detection. Recent advancements include real-time CADe systems and CADx for histopathological predictions, aiding in the differentiation of neoplastic and nonneoplastic lesions. Biases such as the Hawthorne effect and potential overdiagnosis necessitate large-scale clinical trials to validate the long-term benefits of AI. Additionally, novel concepts such as computer-aided quality improvement systems are emerging to address limitations facing current CADe systems. KEY MESSAGES Despite the potential of AI for enhancing colonoscopy outcomes, its effectiveness in reducing colorectal cancer incidence and mortality remains unproven. Further prospective studies are essential to establish the overall utility and clinical benefits of AI in colonoscopy.
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Affiliation(s)
- Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Tsuzuki, Yokohama, Japan
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Tsuzuki, Yokohama, Japan
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12
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Li S, Xu M, Meng Y, Sun H, Zhang T, Yang H, Li Y, Ma X. The application of the combination between artificial intelligence and endoscopy in gastrointestinal tumors. MEDCOMM – ONCOLOGY 2024; 3. [DOI: 10.1002/mog2.91] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 09/03/2024] [Indexed: 01/04/2025]
Abstract
AbstractGastrointestinal (GI) tumors have always been a major type of malignant tumor and a leading cause of tumor‐related deaths worldwide. The main principles of modern medicine for GI tumors are early prevention, early diagnosis, and early treatment, with early diagnosis being the most effective measure. Endoscopy, due to its ability to visualize lesions, has been one of the primary modalities for screening, diagnosing, and treating GI tumors. However, a qualified endoscopist often requires long training and extensive experience, which to some extent limits the wider use of endoscopy. With advances in data science, artificial intelligence (AI) has brought a new development direction for the endoscopy of GI tumors. AI can quickly process large quantities of data and images and improve diagnostic accuracy with some training, greatly reducing the workload of endoscopists and assisting them in early diagnosis. Therefore, this review focuses on the combined application of endoscopy and AI in GI tumors in recent years, describing the latest research progress on the main types of tumors and their performance in clinical trials, the application of multimodal AI in endoscopy, the development of endoscopy, and the potential applications of AI within it, with the aim of providing a reference for subsequent research.
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Affiliation(s)
- Shen Li
- Department of Biotherapy Cancer Center, West China Hospital, West China Medical School Sichuan University Chengdu China
| | - Maosen Xu
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy, West China Hospital, National Clinical Research, Sichuan University Chengdu Sichuan China
| | - Yuanling Meng
- West China School of Stomatology Sichuan University Chengdu Sichuan China
| | - Haozhen Sun
- College of Life Sciences Sichuan University Chengdu Sichuan China
| | - Tao Zhang
- Department of Biotherapy Cancer Center, West China Hospital, West China Medical School Sichuan University Chengdu China
| | - Hanle Yang
- Department of Biotherapy Cancer Center, West China Hospital, West China Medical School Sichuan University Chengdu China
| | - Yueyi Li
- Department of Biotherapy Cancer Center, West China Hospital, West China Medical School Sichuan University Chengdu China
| | - Xuelei Ma
- Department of Biotherapy Cancer Center, West China Hospital, West China Medical School Sichuan University Chengdu China
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Soleymanjahi S, Huebner J, Elmansy L, Rajashekar N, Lüdtke N, Paracha R, Thompson R, Grimshaw AA, Foroutan F, Sultan S, Shung DL. Artificial Intelligence-Assisted Colonoscopy for Polyp Detection : A Systematic Review and Meta-analysis. Ann Intern Med 2024; 177:1652-1663. [PMID: 39531400 DOI: 10.7326/annals-24-00981] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2024] Open
Abstract
BACKGROUND Randomized clinical trials (RCTs) of computer-aided detection (CADe) system-enhanced colonoscopy compared with conventional colonoscopy suggest increased adenoma detection rate (ADR) and decreased adenoma miss rate (AMR), but the effect on detection of advanced colorectal neoplasia (ACN) is unclear. PURPOSE To conduct a systematic review to compare performance of CADe-enhanced and conventional colonoscopy. DATA SOURCES Cochrane Library, Google Scholar, Ovid EMBASE, Ovid MEDLINE, PubMed, Scopus, and Web of Science Core Collection databases were searched through February 2024. STUDY SELECTION Published RCTs comparing CADe-enhanced and conventional colonoscopy. DATA EXTRACTION Average adenoma per colonoscopy (APC) and ACN per colonoscopy were primary outcomes. Adenoma detection rate, AMR, and ACN detection rate (ACN DR) were secondary outcomes. Balancing outcomes included withdrawal time and resection of nonneoplastic polyps (NNPs). Subgroup analyses were done by neural network architecture. DATA SYNTHESIS Forty-four RCTs with 36 201 cases were included. Computer-aided detection-enhanced colonoscopies have higher average APC (12 090 of 12 279 [0.98] vs. 9690 of 12 292 [0.78], incidence rate difference [IRD] = 0.22 [95% CI, 0.16 to 0.28]) and higher ADR (7098 of 16 253 [44.7%] vs. 5825 of 15 855 [36.7%], rate ratio [RR] = 1.21 [CI, 1.15 to 1.28]). Average ACN per colonoscopy was similar (1512 of 9296 [0.16] vs. 1392 of 9121 [0.15], IRD = 0.01 [CI, -0.01 to 0.02]), but ACN DR was higher with CADe system use (1260 of 9899 [12.7%] vs. 1119 of 9746 [11.5%], RR = 1.16 [CI, 1.02 to 1.32]). Using CADe systems resulted in resection of almost 2 extra NNPs per 10 colonoscopies and longer total withdrawal time (0.53 minutes [CI, 0.30 to 0.77]). LIMITATION Statistically significant heterogeneity in quality and sample size and inability to blind endoscopists to the intervention in included studies may affect the performance estimates. CONCLUSION Computer-aided detection-enhanced colonoscopies have increased APC and detection rate but no difference in ACN per colonoscopy and a small increase in ACN DR. There is minimal increase in procedure time and no difference in performance across neural network architectures. PRIMARY FUNDING SOURCE None. (PROSPERO: CRD42023422835).
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Affiliation(s)
- Saeed Soleymanjahi
- Division of Gastroenterology, Mass General Brigham, Harvard School of Medicine, Boston, Massachusetts (S.Soleymanjahi)
| | - Jack Huebner
- Department of Medicine, Yale School of Medicine, New Haven, Connecticut (J.H., L.E., N.R., R.P., R.T.)
| | - Lina Elmansy
- Department of Medicine, Yale School of Medicine, New Haven, Connecticut (J.H., L.E., N.R., R.P., R.T.)
| | - Niroop Rajashekar
- Department of Medicine, Yale School of Medicine, New Haven, Connecticut (J.H., L.E., N.R., R.P., R.T.)
| | - Nando Lüdtke
- Section of Digestive Diseases, Department of Medicine, Yale School of Medicine, New Haven, Connecticut (N.L.)
| | - Rumzah Paracha
- Department of Medicine, Yale School of Medicine, New Haven, Connecticut (J.H., L.E., N.R., R.P., R.T.)
| | - Rachel Thompson
- Department of Medicine, Yale School of Medicine, New Haven, Connecticut (J.H., L.E., N.R., R.P., R.T.)
| | - Alyssa A Grimshaw
- Cushing/Whitney Medical Library, Yale University, New Haven, Connecticut (A.A.G.)
| | | | - Shahnaz Sultan
- Division of Gastroenterology, Hepatology and Nutrition, University of Minnesota, Minneapolis, Minnesota (S.Sultan)
| | - Dennis L Shung
- Section of Digestive Diseases, Clinical and Translational Research Accelerator, and Department of Biomedical Informatics and Data Science, Department of Medicine, Yale School of Medicine, New Haven, Connecticut (D.L.S.)
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Maas MHJ, Rath T, Spada C, Soons E, Forbes N, Kashin S, Cesaro P, Eickhoff A, Vanbiervliet G, Salvi D, Belletrutti PJ, Siersema PD, for the Discovery study team . A computer-aided detection system in the everyday setting of diagnostic, screening, and surveillance colonoscopy: an international, randomized trial. Endoscopy 2024; 56:843-850. [PMID: 38749482 PMCID: PMC11524745 DOI: 10.1055/a-2328-2844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Accepted: 05/15/2024] [Indexed: 06/29/2024]
Abstract
BACKGROUND Computer-aided detection (CADe) has been developed to improve detection during colonoscopy. After initial reports of high efficacy, there has been an increasing recognition of variability in the effectiveness of CADe systems. The aim of this study was to evaluate a CADe system in a varied colonoscopy population. METHODS A multicenter, randomized trial was conducted at seven hospitals (both university and non-university) in Europe and Canada. Participants referred for diagnostic, non-immunochemical fecal occult blood test (iFOBT) screening, or surveillance colonoscopy were randomized (1:1) to undergo CADe-assisted or conventional colonoscopy by experienced endoscopists. Participants with insufficient bowel preparation were excluded from the analysis. The primary outcome was adenoma detection rate (ADR). Secondary outcomes included adenomas per colonoscopy (APC) and sessile serrated lesions (SSLs) per colonoscopy. RESULTS 581 participants were enrolled, of whom 497 were included in the final analysis: 250 in the CADe arm and 247 in the conventional colonoscopy arm. The indication was surveillance in 202/497 colonoscopies (40.6 %), diagnostic in 199/497 (40.0 %), and non-iFOBT screening in 96/497 (19.3 %). Overall, ADR (38.4 % vs. 37.7 %; P = 0.43) and APC (0.66 vs. 0.66; P = 0.97) were similar between CADe and conventional colonoscopy. SSLs per colonoscopy was increased (0.30 vs. 0.19; P = 0.049) in the CADe arm vs. the conventional colonoscopy arm. CONCLUSIONS In this study conducted by experienced endoscopists, CADe did not result in a statistically significant increase in ADR. However, the ADR of our control group substantially surpassed our sample size assumptions, increasing the risk of an underpowered trial.
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Affiliation(s)
- Michiel H. J. Maas
- Gastroenterology and Hepatology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Timo Rath
- Department of Medicine I, Division of Gastroenterology, Universitätsklinikum Erlangen, Erlangen, Germany
| | - Cristiano Spada
- Department of Gastroenterology and Endoscopy, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy
- Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Elsa Soons
- Gastroenterology and Hepatology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Nauzer Forbes
- Department of Medicine, University of Calgary, Calgary, Canada
| | - Sergey Kashin
- Department of Endoscopy, Yaroslavl Regional Cancer Hospital, Yaroslavl, Russia
| | - Paola Cesaro
- Department of Gastroenterology and Endoscopy, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy
| | - Axel Eickhoff
- Gastroenterology, Diabetology, Infectiology, Klinikum Hanau, Hanau, Germany
| | | | - Daniele Salvi
- Department of Gastroenterology and Endoscopy, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy
- Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | | | - Peter D. Siersema
- Gastroenterology and Hepatology, Radboud University Medical Center, Nijmegen, the Netherlands
- ErasmusMC – University Medical Center, Rotterdam, the Netherlands
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Cheng CL, Tang JH, Hsieh YH, Kuo YL, Fang KC, Tseng CW, Su IC, Chang CC, Tsui YN, Lee BP, Zou KY, Lee YS, Leung FW. Comparing Right-Sided Colon Adenoma and Serrated Polyp Miss Rates With Water Exchange and CO 2 Insufflation: A Randomized Controlled Trial. Am J Gastroenterol 2024:00000434-990000000-01419. [PMID: 39471473 DOI: 10.14309/ajg.0000000000003168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Accepted: 10/25/2024] [Indexed: 11/01/2024]
Abstract
INTRODUCTION Postcolonoscopy colorectal cancers primarily occur in the right-sided colon because of missed adenomas and serrated polyps (SPs). Water exchange (WE) improves cleanliness and visibility of the right-sided colon. We hypothesized that WE could reduce the right-sided colon adenoma (rAMR) and SP miss rate (rSPMR) compared with standard colonoscopy. METHODS We randomly assigned 386 colonoscopy patients to insertion with either WE or CO 2 insufflation. During the first withdrawal, polypectomies were performed up to the hepatic flexure. A second endoscopist, blinded to the insertion technique, re-examined the right-sided colon. The miss rate was determined by dividing the number of additional adenomas or SPs by the total number detected in both examinations. The primary outcome was the combined rAMR and rSPMR. RESULTS WE significantly decreased the combined rAMR and rSPMR (22.2% vs 32.2%, P < 0.001) and rSPMR alone (22.5% vs 37.1%, P = 0.002) compared with CO 2 insufflation, but not rAMR (21.8% vs 29.8%, P = 0.079). In addition, WE significantly increased the detection of SP per colonoscopy (SP per colonoscopy) in the right-sided colon (0.95 ± 1.56 vs 0.50 ± 0.79, P < 0.001). Multivariate logistic regression analysis showed that ≥2 SPs in the right-sided colon were an independent predictor of rSPMR (odds ratio, 3.47; 95% confidence interval, 1.89─6.38), along with a higher right-sided colon Boston Bowel Preparation Scale score (odds ratio, 0.55; 95% confidence interval, 0.32─0.94). DISCUSSION The significant reduction in rSPMR and increase in right-sided colon SP per colonoscopy suggest that colonoscopy insertion using WE is a valid alternative to CO 2 insufflation (clinical trial registration number: NCT04124393).
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Affiliation(s)
- Chi-Liang Cheng
- Division of Gastroenterology, Evergreen General Hospital, Taoyuan, Taiwan
| | - Jui-Hsiang Tang
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, New Taipei Municipal Tucheng Hospital, New Taipei City, Taiwan
| | - Yu-Hsi Hsieh
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan
- School of Medicine , Tzu Chi University , Hualien , Taiwan
| | - Yen-Lin Kuo
- Division of Gastroenterology, Evergreen General Hospital, Taoyuan, Taiwan
| | - Kuan-Chieh Fang
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Chih-Wei Tseng
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan
- School of Medicine , Tzu Chi University , Hualien , Taiwan
| | - I-Chia Su
- Division of Gastroenterology, Evergreen General Hospital, Taoyuan, Taiwan
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Chun-Chao Chang
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Yi-Ning Tsui
- Division of Gastroenterology, Evergreen General Hospital, Taoyuan, Taiwan
| | - Bai-Ping Lee
- Division of Gastroenterology, Evergreen General Hospital, Taoyuan, Taiwan
| | - Ke-Yun Zou
- Department of Biotechnology, School of Health Technology, Ming Chuan University, Taoyuan, Taiwan
| | - Yun-Shien Lee
- Department of Biotechnology, School of Health Technology, Ming Chuan University, Taoyuan, Taiwan
| | - Felix W Leung
- Sepulveda Ambulatory Care Center, Veterans Affairs Greater Los Angeles Healthcare System, North Hills, California, USA
- David Geffen School of Medicine at UCLA, Los Angeles, California, USA
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Park DK, Kim EJ, Im JP, Lim H, Lim YJ, Byeon JS, Kim KO, Chung JW, Kim YJ. A prospective multicenter randomized controlled trial on artificial intelligence assisted colonoscopy for enhanced polyp detection. Sci Rep 2024; 14:25453. [PMID: 39455850 PMCID: PMC11512038 DOI: 10.1038/s41598-024-77079-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 10/18/2024] [Indexed: 10/28/2024] Open
Abstract
Colon polyp detection and removal via colonoscopy are essential for colorectal cancer screening and prevention. This study aimed to develop a colon polyp detection program based on the RetinaNet algorithm and verify its clinical utility. To develop the AI-assisted program, the dataset was fully anonymized and divided into 10 folds for 10-fold cross-validation. Each fold consisted of 9,639 training images and 1,070 validation images. Video data from 56 patients were used for model training, and transfer learning was performed using the developed still image-based model. The final model was developed as a real-time polyp-detection program for endoscopy. To evaluate the model's performance, a prospective randomized controlled trial was conducted at six institutions to compare the polyp detection rates (PDR). A total of 805 patients were included. The group that utilized the AI model showed significantly higher PDR and adenoma detection rate (ADR) than the group that underwent colonoscopy without AI assistance. Multivariate analysis revealed an OR of 1.50 for cases where polyps were detected. The AI-assisted polyp-detection program is clinically beneficial for detecting polyps during colonoscopy. By utilizing this AI-assisted program, clinicians can improve adenoma detection rates, ultimately leading to enhanced cancer prevention.
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Affiliation(s)
- Dong Kyun Park
- Division of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center, Gachon University College of Medicine, 21, Namdong-daero 774 beon-gil, Namdong-gu, Incheon, 21565, Republic of Korea
- Health IT Research Center, Gachon University Gil Medical Center, Incheon, Republic of Korea
| | - Eui Joo Kim
- Division of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center, Gachon University College of Medicine, 21, Namdong-daero 774 beon-gil, Namdong-gu, Incheon, 21565, Republic of Korea
| | - Jong Pil Im
- Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Hyun Lim
- Department of Internal Medicine, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, Republic of Korea
| | - Yun Jeong Lim
- Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang, Republic of Korea
| | - Jeong-Sik Byeon
- Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Kyoung Oh Kim
- Division of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center, Gachon University College of Medicine, 21, Namdong-daero 774 beon-gil, Namdong-gu, Incheon, 21565, Republic of Korea
| | - Jun-Won Chung
- Division of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center, Gachon University College of Medicine, 21, Namdong-daero 774 beon-gil, Namdong-gu, Incheon, 21565, Republic of Korea
| | - Yoon Jae Kim
- Division of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center, Gachon University College of Medicine, 21, Namdong-daero 774 beon-gil, Namdong-gu, Incheon, 21565, Republic of Korea.
- Health IT Research Center, Gachon University Gil Medical Center, Incheon, Republic of Korea.
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Mota J, Almeida MJ, Martins M, Mendes F, Cardoso P, Afonso J, Ribeiro T, Ferreira J, Fonseca F, Limbert M, Lopes S, Macedo G, Castro Poças F, Mascarenhas M. Artificial Intelligence in Coloproctology: A Review of Emerging Technologies and Clinical Applications. J Clin Med 2024; 13:5842. [PMID: 39407902 PMCID: PMC11477032 DOI: 10.3390/jcm13195842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2024] [Revised: 09/21/2024] [Accepted: 09/22/2024] [Indexed: 10/20/2024] Open
Abstract
Artificial intelligence (AI) has emerged as a transformative tool across several specialties, namely gastroenterology, where it has the potential to optimize both diagnosis and treatment as well as enhance patient care. Coloproctology, due to its highly prevalent pathologies and tremendous potential to cause significant mortality and morbidity, has drawn a lot of attention regarding AI applications. In fact, its application has yielded impressive outcomes in various domains, colonoscopy being one prominent example, where it aids in the detection of polyps and early signs of colorectal cancer with high accuracy and efficiency. With a less explored path but equivalent promise, AI-powered capsule endoscopy ensures accurate and time-efficient video readings, already detecting a wide spectrum of anomalies. High-resolution anoscopy is an area that has been growing in interest in recent years, with efforts being made to integrate AI. There are other areas, such as functional studies, that are currently in the early stages, but evidence is expected to emerge soon. According to the current state of research, AI is anticipated to empower gastroenterologists in the decision-making process, paving the way for a more precise approach to diagnosing and treating patients. This review aims to provide the state-of-the-art use of AI in coloproctology while also reflecting on future directions and perspectives.
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Affiliation(s)
- Joana Mota
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - Maria João Almeida
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - Miguel Martins
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - Francisco Mendes
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - Pedro Cardoso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - João Afonso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - Tiago Ribeiro
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - João Ferreira
- Department of Mechanical Engineering, Faculty of Engineering, University of Porto, 4200-065 Porto, Portugal;
- DigestAID—Digestive Artificial Intelligence Development, Rua Alfredo Allen n.° 455/461, 4200-135 Porto, Portugal
| | - Filipa Fonseca
- Instituto Português de Oncologia de Lisboa Francisco Gentil (IPO Lisboa), 1099-023 Lisboa, Portugal; (F.F.); (M.L.)
| | - Manuel Limbert
- Instituto Português de Oncologia de Lisboa Francisco Gentil (IPO Lisboa), 1099-023 Lisboa, Portugal; (F.F.); (M.L.)
- Artificial Intelligence Group of the Portuguese Society of Coloproctology, 1050-117 Lisboa, Portugal;
| | - Susana Lopes
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
- Artificial Intelligence Group of the Portuguese Society of Coloproctology, 1050-117 Lisboa, Portugal;
- Faculty of Medicine, University of Porto, 4200-047 Porto, Portugal
| | - Guilherme Macedo
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-047 Porto, Portugal
| | - Fernando Castro Poças
- Artificial Intelligence Group of the Portuguese Society of Coloproctology, 1050-117 Lisboa, Portugal;
- Department of Gastroenterology, Santo António University Hospital, 4099-001 Porto, Portugal
- Abel Salazar Biomedical Sciences Institute (ICBAS), 4050-313 Porto, Portugal
| | - Miguel Mascarenhas
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
- Artificial Intelligence Group of the Portuguese Society of Coloproctology, 1050-117 Lisboa, Portugal;
- Faculty of Medicine, University of Porto, 4200-047 Porto, Portugal
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18
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Scott IA, Miller T, Crock C. Using conversant artificial intelligence to improve diagnostic reasoning: ready for prime time? Med J Aust 2024; 221:240-243. [PMID: 39086025 DOI: 10.5694/mja2.52401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Accepted: 04/22/2024] [Indexed: 08/02/2024]
Affiliation(s)
- Ian A Scott
- University of Queensland, Brisbane, QLD
- Princess Alexandra Hospital, Brisbane, QLD
| | | | - Carmel Crock
- Royal Victorian Eye and Ear Hospital, Melbourne, VIC
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Xu Q, He Z. Effect of different working periods on missed diagnosis of colorectal polyps in colonoscopy. BMC Gastroenterol 2024; 24:286. [PMID: 39187774 PMCID: PMC11346284 DOI: 10.1186/s12876-024-03365-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2024] [Accepted: 08/08/2024] [Indexed: 08/28/2024] Open
Abstract
BACKGROUND To investigate the effect of different working periods on missed diagnoses in patients with colorectal polyps in colonoscopy. METHODS We conducted a retrospective analysis of patients who were diagnosed with colorectal polyps during colonoscopy in an outpatient department between July and December 2022. These patients were subsequently hospitalized for resection during this period. Patients with missed diagnoses were those who had newly discovered polyps in a second colonoscopy. The working periods were categorized as work, near the end of work, and delayed work, respectively, in the morning and afternoon. RESULTS A total of 482 patients were included, and the miss rate of diagnosis was 48.1% (232/482), mainly in the transverse colon (25%), and the ascending colon (23%). Patient age was a risk factor for the miss rate of diagnosis (OR = 1.025, 95%CI: 1.009-1.042, P = 0.003) and was also associated with the number of polyps detected for the first colonoscopy (χ2 = 18.196, P = 0.001). The different working periods had no statistical effect on the missed rate of diagnosis (χ2 = 1.998, P = 0.849). However, there was an increasing trend in miss rates towards the end of work and delayed work periods, both in the morning and afternoon. The highest miss rate (60.0%) was observed during delayed work in the afternoon. Additionally, poor bowel preparation was significantly more common during delayed work in the afternoon. CONCLUSIONS The increasing trend in miss rates towards the end of work and delayed work periods deserves clinical attention. Endoscopists cannot always stay in good condition under heavy workloads.
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Affiliation(s)
- Qing Xu
- Department of Gastroenterology, The First Affiliated Hospital of Nanjing Medical University, No.300 Guangzhou Road, Nanjing, 210029, Jiangsu Province, China
| | - Zhi He
- Department of Gastroenterology, The First Affiliated Hospital of Nanjing Medical University, No.300 Guangzhou Road, Nanjing, 210029, Jiangsu Province, China.
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Kikuchi R, Okamoto K, Ozawa T, Shibata J, Ishihara S, Tada T. Endoscopic Artificial Intelligence for Image Analysis in Gastrointestinal Neoplasms. Digestion 2024; 105:419-435. [PMID: 39068926 DOI: 10.1159/000540251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 07/02/2024] [Indexed: 07/30/2024]
Abstract
BACKGROUND Artificial intelligence (AI) using deep learning systems has recently been utilized in various medical fields. In the field of gastroenterology, AI is primarily implemented in image recognition and utilized in the realm of gastrointestinal (GI) endoscopy. In GI endoscopy, computer-aided detection/diagnosis (CAD) systems assist endoscopists in GI neoplasm detection or differentiation of cancerous or noncancerous lesions. Several AI systems for colorectal polyps have already been applied in colonoscopy clinical practices. In esophagogastroduodenoscopy, a few CAD systems for upper GI neoplasms have been launched in Asian countries. The usefulness of these CAD systems in GI endoscopy has been gradually elucidated. SUMMARY In this review, we outline recent articles on several studies of endoscopic AI systems for GI neoplasms, focusing on esophageal squamous cell carcinoma (ESCC), esophageal adenocarcinoma (EAC), gastric cancer (GC), and colorectal polyps. In ESCC and EAC, computer-aided detection (CADe) systems were mainly developed, and a recent meta-analysis study showed sensitivities of 91.2% and 93.1% and specificities of 80% and 86.9%, respectively. In GC, a recent meta-analysis study on CADe systems demonstrated that their sensitivity and specificity were as high as 90%. A randomized controlled trial (RCT) also showed that the use of the CADe system reduced the miss rate. Regarding computer-aided diagnosis (CADx) systems for GC, although RCTs have not yet been conducted, most studies have demonstrated expert-level performance. In colorectal polyps, multiple RCTs have shown the usefulness of the CADe system for improving the polyp detection rate, and several CADx systems have been shown to have high accuracy in colorectal polyp differentiation. KEY MESSAGES Most analyses of endoscopic AI systems suggested that their performance was better than that of nonexpert endoscopists and equivalent to that of expert endoscopists. Thus, endoscopic AI systems may be useful for reducing the risk of overlooking lesions and improving the diagnostic ability of endoscopists.
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Affiliation(s)
- Ryosuke Kikuchi
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Kazuaki Okamoto
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Tsuyoshi Ozawa
- Tomohiro Tada the Institute of Gastroenterology and Proctology, Saitama, Japan
- AI Medical Service Inc., Tokyo, Japan
| | - Junichi Shibata
- Tomohiro Tada the Institute of Gastroenterology and Proctology, Saitama, Japan
- AI Medical Service Inc., Tokyo, Japan
| | - Soichiro Ishihara
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Tomohiro Tada
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
- Tomohiro Tada the Institute of Gastroenterology and Proctology, Saitama, Japan
- AI Medical Service Inc., Tokyo, Japan
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Introzzi L, Zonca J, Cabitza F, Cherubini P, Reverberi C. Enhancing human-AI collaboration: The case of colonoscopy. Dig Liver Dis 2024; 56:1131-1139. [PMID: 37940501 DOI: 10.1016/j.dld.2023.10.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 10/19/2023] [Accepted: 10/23/2023] [Indexed: 11/10/2023]
Abstract
Diagnostic errors impact patient health and healthcare costs. Artificial Intelligence (AI) shows promise in mitigating this burden by supporting Medical Doctors in decision-making. However, the mere display of excellent or even superhuman performance by AI in specific tasks does not guarantee a positive impact on medical practice. Effective AI assistance should target the primary causes of human errors and foster effective collaborative decision-making with human experts who remain the ultimate decision-makers. In this narrative review, we apply these principles to the specific scenario of AI assistance during colonoscopy. By unraveling the neurocognitive foundations of the colonoscopy procedure, we identify multiple bottlenecks in perception, attention, and decision-making that contribute to diagnostic errors, shedding light on potential interventions to mitigate them. Furthermore, we explored how existing AI devices fare in clinical practice and whether they achieved an optimal integration with the human decision-maker. We argue that to foster optimal Human-AI collaboration, future research should expand our knowledge of factors influencing AI's impact, establish evidence-based cognitive models, and develop training programs based on them. These efforts will enhance human-AI collaboration, ultimately improving diagnostic accuracy and patient outcomes. The principles illuminated in this review hold more general value, extending their relevance to a wide array of medical procedures and beyond.
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Affiliation(s)
- Luca Introzzi
- Department of Psychology, Università Milano - Bicocca, Milano, Italy
| | - Joshua Zonca
- Department of Psychology, Università Milano - Bicocca, Milano, Italy; Milan Center for Neuroscience, Università Milano - Bicocca, Milano, Italy
| | - Federico Cabitza
- Department of Informatics, Systems and Communication, Università Milano - Bicocca, Milano, Italy; IRCCS Istituto Ortopedico Galeazzi, Milano, Italy
| | - Paolo Cherubini
- Department of Brain and Behavioral Sciences, Università Statale di Pavia, Pavia, Italy
| | - Carlo Reverberi
- Department of Psychology, Università Milano - Bicocca, Milano, Italy; Milan Center for Neuroscience, Università Milano - Bicocca, Milano, Italy.
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Spadaccini M, Troya J, Khalaf K, Facciorusso A, Maselli R, Hann A, Repici A. Artificial Intelligence-assisted colonoscopy and colorectal cancer screening: Where are we going? Dig Liver Dis 2024; 56:1148-1155. [PMID: 38458884 DOI: 10.1016/j.dld.2024.01.203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 01/22/2024] [Accepted: 01/23/2024] [Indexed: 03/10/2024]
Abstract
Colorectal cancer is a significant global health concern, necessitating effective screening strategies to reduce its incidence and mortality rates. Colonoscopy plays a crucial role in the detection and removal of colorectal neoplastic precursors. However, there are limitations and variations in the performance of endoscopists, leading to missed lesions and suboptimal outcomes. The emergence of artificial intelligence (AI) in endoscopy offers promising opportunities to improve the quality and efficacy of screening colonoscopies. In particular, AI applications, including computer-aided detection (CADe) and computer-aided characterization (CADx), have demonstrated the potential to enhance adenoma detection and optical diagnosis accuracy. Additionally, AI-assisted quality control systems aim to standardize the endoscopic examination process. This narrative review provides an overview of AI principles and discusses the current knowledge on AI-assisted endoscopy in the context of screening colonoscopies. It highlights the significant role of AI in improving lesion detection, characterization, and quality assurance during colonoscopy. However, further well-designed studies are needed to validate the clinical impact and cost-effectiveness of AI-assisted colonoscopy before its widespread implementation.
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Affiliation(s)
- Marco Spadaccini
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; Department of Biomedical Sciences, Humanitas University, 20089 Rozzano, Italy.
| | - Joel Troya
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Kareem Khalaf
- Division of Gastroenterology, St. Michael's Hospital, University of Toronto, Toronto, Canada
| | - Antonio Facciorusso
- Gastroenterology Unit, Department of Surgical and Medical Sciences, University of Foggia, Foggia, Italy
| | - Roberta Maselli
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; Department of Biomedical Sciences, Humanitas University, 20089 Rozzano, Italy
| | - Alexander Hann
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Alessandro Repici
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; Department of Biomedical Sciences, Humanitas University, 20089 Rozzano, Italy
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Jin XF, Ma HY, Shi JW, Cai JT. Efficacy of artificial intelligence in reducing miss rates of GI adenomas, polyps, and sessile serrated lesions: a meta-analysis of randomized controlled trials. Gastrointest Endosc 2024; 99:667-675.e1. [PMID: 38184117 DOI: 10.1016/j.gie.2024.01.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 11/20/2023] [Accepted: 01/01/2024] [Indexed: 01/08/2024]
Abstract
BACKGROUND AND AIMS The aim of this study was to determine if utilization of artificial intelligence (AI) in the course of endoscopic procedures can significantly diminish both the adenoma miss rate (AMR) and the polyp miss rate (PMR) compared with standard endoscopy. METHODS We performed an extensive search of various databases, encompassing PubMed, Embase, Cochrane Library, Web of Science, and Scopus, until June 2023. The search terms used were artificial intelligence, machine learning, deep learning, transfer machine learning, computer-assisted diagnosis, convolutional neural networks, gastrointestinal (GI) endoscopy, endoscopic image analysis, polyp, adenoma, and neoplasms. The main study aim was to explore the impact of AI on the AMR, PMR, and sessile serrated lesion miss rate. RESULTS A total of 7 randomized controlled trials were included in this meta-analysis. Pooled AMR was markedly lower in the AI group versus the non-AI group (pooled relative risk [RR], .46; 95% confidence interval [CI], .36-.59; P < .001). PMR was also reduced in the AI group in contrast with the non-AI control (pooled RR, .43; 95% CI, .27-.69; P < .001). The results showed that AI decreased the miss rate of sessile serrated lesions (pooled RR, .43; 95% CI, .20 to .92; P < .05) and diminutive adenomas (pooled RR, .49; 95% CI, .26-.93) during endoscopy, but no significant effect was observed for advanced adenomas (pooled RR, .48; 95% CI, .17-1.37; P = .17). The average number of polyps (Hedges' g = -.486; 95% CI, -.697 to -.274; P = .000) and adenomas (Hedges' g = -.312; 95% CI, -.551 to -.074; P = .01) detected during the second procedure also favored AI. However, AI implementation did not lead to a prolonged withdrawal time (P > .05). CONCLUSIONS This meta-analysis suggests that AI technology leads to significant reduction of miss rates for GI adenomas, polyps, and sessile serrated lesions during endoscopic surveillance. These results underscore the potential of AI to improve the accuracy and efficiency of GI endoscopic procedures.
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Affiliation(s)
- Xi-Feng Jin
- Department of Gastroenterology, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China.
| | - Hong-Yan Ma
- Tengzhou Central People's Hospital, Shandong Province, Zaozhuang, China
| | - Jun-Wen Shi
- Tengzhou Central People's Hospital, Shandong Province, Zaozhuang, China
| | - Jian-Ting Cai
- Department of Gastroenterology, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
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24
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Okumura T, Imai K, Misawa M, Kudo SE, Hotta K, Ito S, Kishida Y, Takada K, Kawata N, Maeda Y, Yoshida M, Yamamoto Y, Minamide T, Ishiwatari H, Sato J, Matsubayashi H, Ono H. Evaluating false-positive detection in a computer-aided detection system for colonoscopy. J Gastroenterol Hepatol 2024; 39:927-934. [PMID: 38273460 DOI: 10.1111/jgh.16491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 12/21/2023] [Accepted: 01/03/2024] [Indexed: 01/27/2024]
Abstract
BACKGROUND AND AIM Computer-aided detection (CADe) systems can efficiently detect polyps during colonoscopy. However, false-positive (FP) activation is a major limitation of CADe. We aimed to compare the rate and causes of FP using CADe before and after an update designed to reduce FP. METHODS We analyzed CADe-assisted colonoscopy videos recorded between July 2022 and October 2022. The number and causes of FPs and excessive time spent by the endoscopist on FP (ET) were compared pre- and post-update using 1:1 propensity score matching. RESULTS During the study period, 191 colonoscopy videos (94 and 97 in the pre- and post-update groups, respectively) were recorded. Propensity score matching resulted in 146 videos (73 in each group). The mean number of FPs and median ET per colonoscopy were significantly lower in the post-update group than those in the pre-update group (4.2 ± 3.7 vs 18.1 ± 11.1; P < 0.001 and 0 vs 16 s; P < 0.001, respectively). Mucosal tags, bubbles, and folds had the strongest association with decreased FP post-update (pre-update vs post-update: 4.3 ± 3.6 vs 0.4 ± 0.8, 0.32 ± 0.70 vs 0.04 ± 0.20, and 8.6 ± 6.7 vs 1.6 ± 1.7, respectively). There was no significant decrease in the true positive rate (post-update vs pre-update: 95.0% vs 99.2%; P = 0.09) or the adenoma detection rate (post-update vs pre-update: 52.1% vs 49.3%; P = 0.87). CONCLUSIONS The updated CADe can reduce FP without impairing polyp detection. A reduction in FP may help relieve the burden on endoscopists.
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Affiliation(s)
- Taishi Okumura
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | - Kenichiro Imai
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | - Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Kinichi Hotta
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | - Sayo Ito
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | | | - Kazunori Takada
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | - Noboru Kawata
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | - Yuki Maeda
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | - Masao Yoshida
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | - Yoichi Yamamoto
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | | | | | - Junya Sato
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | | | - Hiroyuki Ono
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
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25
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Lee MCM, Parker CH, Liu LWC, Farahvash A, Jeyalingam T. Impact of study design on adenoma detection in the evaluation of artificial intelligence-aided colonoscopy: a systematic review and meta-analysis. Gastrointest Endosc 2024; 99:676-687.e16. [PMID: 38272274 DOI: 10.1016/j.gie.2024.01.021] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 12/19/2023] [Accepted: 01/10/2024] [Indexed: 01/27/2024]
Abstract
BACKGROUND AND AIMS Randomized controlled trials (RCTs) have reported that artificial intelligence (AI) improves endoscopic polyp detection. Different methodologies-namely, parallel and tandem designs-have been used to evaluate the efficacy of AI-assisted colonoscopy in RCTs. Systematic reviews and meta-analyses have reported a pooled effect that includes both study designs. However, it is unclear whether there are inconsistencies in the reported results of these 2 designs. Here, we aimed to determine whether study characteristics moderate between-trial differences in outcomes when evaluating the effectiveness of AI-assisted polyp detection. METHODS A systematic search of Ovid MEDLINE, Embase, Cochrane Central, Web of Science, and IEEE Xplore was performed through March 1, 2023, for RCTs comparing AI-assisted colonoscopy with routine high-definition colonoscopy in polyp detection. The primary outcome of interest was the impact of study type on the adenoma detection rate (ADR). Secondary outcomes included the impact of the study type on adenomas per colonoscopy and withdrawal time, as well as the impact of geographic location, AI system, and endoscopist experience on ADR. Pooled event analysis was performed using a random-effects model. RESULTS Twenty-four RCTs involving 17,413 colonoscopies (AI assisted: 8680; non-AI assisted: 8733) were included. AI-assisted colonoscopy improved overall ADR (risk ratio [RR], 1.24; 95% confidence interval [CI], 1.17-1.31; I2 = 53%; P < .001). Tandem studies collectively demonstrated improved ADR in AI-aided colonoscopies (RR, 1.18; 95% CI, 1.08-1.30; I2 = 0%; P < .001), as did parallel studies (RR, 1.26; 95% CI, 1.17-1.35; I2 = 62%; P < .001), with no statistical subgroup difference between study design. Both tandem and parallel study designs revealed improvement in adenomas per colonoscopy in AI-aided colonoscopies, but this improvement was more marked among tandem studies (P < .001). AI assistance significantly increased withdrawal times for parallel (P = .002), but not tandem, studies. ADR improvement was more marked among studies conducted in Asia compared to Europe and North America in a subgroup analysis (P = .007). Type of AI system used or endoscopist experience did not affect overall improvement in ADR. CONCLUSIONS Either parallel or tandem study design can capture the improvement in ADR resulting from the use of AI-assisted polyp detection systems. Tandem studies powered to detect differences in endoscopic performance through paired comparison may be a resource-efficient method of evaluating new AI-assisted technologies.
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Affiliation(s)
- Michelle C M Lee
- Division of Gastroenterology and Hepatology, Department of Medicine, University Health Network, University of Toronto, Toronto, Ontario, Canada; Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Colleen H Parker
- Division of Gastroenterology and Hepatology, Department of Medicine, University Health Network, University of Toronto, Toronto, Ontario, Canada; Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Louis W C Liu
- Division of Gastroenterology and Hepatology, Department of Medicine, University Health Network, University of Toronto, Toronto, Ontario, Canada; Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Armin Farahvash
- Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Thurarshen Jeyalingam
- Division of Gastroenterology and Hepatology, Department of Medicine, University Health Network, University of Toronto, Toronto, Ontario, Canada; Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
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26
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Gangwani MK, Haghbin H, Ishtiaq R, Hasan F, Dillard J, Jaber F, Dahiya DS, Ali H, Salim S, Lee-Smith W, Sohail AH, Inamdar S, Aziz M, Hart B. Single Versus Second Observer vs Artificial Intelligence to Increase the ADENOMA Detection Rate of Colonoscopy-A Network Analysis. Dig Dis Sci 2024; 69:1380-1388. [PMID: 38436866 PMCID: PMC11026252 DOI: 10.1007/s10620-024-08341-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 02/07/2024] [Indexed: 03/05/2024]
Abstract
BACKGROUND AND AIMS Screening colonoscopy has significantly contributed to the reduction of the incidence of colorectal cancer (CRC) and its associated mortality, with adenoma detection rate (ADR) as the quality marker. To increase the ADR, various solutions have been proposed including the utilization of Artificial Intelligence (AI) and employing second observers during colonoscopies. In the interest of AI improving ADR independently, without a second observer, and the operational similarity between AI and second observer, this network meta-analysis aims at evaluating the effectiveness of AI, second observer, and a single observer in improving ADR. METHODS We searched the Medline, Embase, Cochrane, Web of Science Core Collection, Korean Citation Index, SciELO, Global Index Medicus, and Cochrane. A direct head-to-head comparator analysis and network meta-analysis were performed using the random-effects model. The odds ratio (OR) was calculated with a 95% confidence interval (CI) and p-value < 0.05 was considered statistically significant. RESULTS We analyzed 26 studies, involving 22,560 subjects. In the direct comparative analysis, AI demonstrated higher ADR (OR: 0.668, 95% CI 0.595-0.749, p < 0.001) than single observer. Dual observer demonstrated a higher ADR (OR: 0.771, 95% CI 0.688-0.865, p < 0.001) than single operator. In network meta-analysis, results were consistent on the network meta-analysis, maintaining consistency. No statistical difference was noted when comparing AI to second observer. (RR 1.1 (0.9-1.2, p = 0.3). Results were consistent when evaluating only RCTs. Net ranking provided higher score to AI followed by second observer followed by single observer. CONCLUSION Artificial Intelligence and second-observer colonoscopy showed superior success in Adenoma Detection Rate when compared to single-observer colonoscopy. Although not statistically significant, net ranking model favors the superiority of AI to the second observer.
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Affiliation(s)
| | - Hossein Haghbin
- Department of Gastroenterology and Hepatology, Ascension Providence Hospital, Southfield, MI, USA
| | - Rizwan Ishtiaq
- Department of Medicine, St Francis Hospital and Medical Center, Hartford, CT, USA
| | - Fariha Hasan
- Department of Internal Medicine, Cooper University Hospital, Camden, NJ, USA
| | - Julia Dillard
- Department of Medicine, University of Toledo Medical Center, Toledo, OH, USA
| | - Fouad Jaber
- Department of Internal Medicine, University of Missouri-Kansas City, Kansas City, MO, USA
| | - Dushyant Singh Dahiya
- Department of Medicine, Central Michigan University College of Medicine, Mount Pleasant, MI, USA
| | - Hassam Ali
- Department of Gastroenterology and Hepatology, East Carolina University Health, Greenville, NC, USA
| | - Shaharyar Salim
- Department of Internal Medicine, Aga Khan University Hospital, Karachi, Pakistan
| | - Wade Lee-Smith
- University of Toledo Libraries, University of Toledo, Toledo, OH, USA
| | - Amir Humza Sohail
- Department of General Surgery, New York University Langone Health, Long Island, NY, USA
| | - Sumant Inamdar
- Department of Gastroenterology and Hepatology, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Muhammad Aziz
- Department of Gastroenterology and Hepatology, University of Toledo Medical Center, Toledo, OH, USA
| | - Benjamin Hart
- Depertment of Hepatology and Gastroenterology, University of Michigan, Ann Arbor, MI, USA
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27
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Hsu CM, Chen TH, Hsu CC, Wu CH, Lin CJ, Le PH, Lin CY, Kuo T. Two-stage deep-learning-based colonoscopy polyp detection incorporating fisheye and reflection correction. J Gastroenterol Hepatol 2024; 39:733-739. [PMID: 38225761 DOI: 10.1111/jgh.16470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 11/28/2023] [Accepted: 12/14/2023] [Indexed: 01/17/2024]
Abstract
BACKGROUND AND AIM Colonoscopy is a useful method for the diagnosis and management of colorectal diseases. Many computer-aided systems have been developed to assist clinicians in detecting colorectal lesions by analyzing colonoscopy images. However, fisheye-lens distortion and light reflection in colonoscopy images can substantially affect the clarity of these images and their utility in detecting polyps. This study proposed a two-stage deep-learning model to correct distortion and reflections in colonoscopy images and thus facilitate polyp detection. METHODS Images were collected from the PolypSet dataset, the Kvasir-SEG dataset, and one medical center's patient archiving and communication system. The training, validation, and testing datasets comprised 808, 202, and 1100 images, respectively. The first stage involved the correction of fisheye-related distortion in colonoscopy images and polyp detection, which was performed using a convolutional neural network. The second stage involved the use of generative and adversarial networks for correcting reflective colonoscopy images before the convolutional neural network was used for polyp detection. RESULTS The model had higher accuracy when it was validated using corrected images than when it was validated using uncorrected images (96.8% vs 90.8%, P < 0.001). The model's accuracy in detecting polyps in the Kvasir-SEG dataset reached 96%, and the area under the receiver operating characteristic curve was 0.94. CONCLUSION The proposed model can facilitate the clinical diagnosis of colorectal polyps and improve the quality of colonoscopy.
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Affiliation(s)
- Chen-Ming Hsu
- Department of Gastroenterology and Hepatology, Chang Gung Memorial Hospital Taoyuan Branch, Taoyuan, Taiwan
- Department of Gastroenterology and Hepatology, Chang Gung Memorial Hospital Linkou Main Branch, Taoyuan, Taiwan
- Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Tsung-Hsing Chen
- Department of Gastroenterology and Hepatology, Chang Gung Memorial Hospital Linkou Main Branch, Taoyuan, Taiwan
- Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Chien-Chang Hsu
- Department of Computer Science and Information Engineering, Fu Jen Catholic University, Taipei, Taiwan
| | - Che-Hao Wu
- Department of Computer Science and Information Engineering, Fu Jen Catholic University, Taipei, Taiwan
| | - Chun-Jung Lin
- Department of Gastroenterology and Hepatology, Chang Gung Memorial Hospital Linkou Main Branch, Taoyuan, Taiwan
- Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Puo-Hsien Le
- Department of Gastroenterology and Hepatology, Chang Gung Memorial Hospital Linkou Main Branch, Taoyuan, Taiwan
- Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Cheng-Yu Lin
- Department of Gastroenterology and Hepatology, Chang Gung Memorial Hospital Linkou Main Branch, Taoyuan, Taiwan
| | - Tony Kuo
- Department of Gastroenterology and Hepatology, Chang Gung Memorial Hospital Linkou Main Branch, Taoyuan, Taiwan
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28
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Martindale APL, Llewellyn CD, de Visser RO, Ng B, Ngai V, Kale AU, di Ruffano LF, Golub RM, Collins GS, Moher D, McCradden MD, Oakden-Rayner L, Rivera SC, Calvert M, Kelly CJ, Lee CS, Yau C, Chan AW, Keane PA, Beam AL, Denniston AK, Liu X. Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines. Nat Commun 2024; 15:1619. [PMID: 38388497 PMCID: PMC10883966 DOI: 10.1038/s41467-024-45355-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 01/22/2024] [Indexed: 02/24/2024] Open
Abstract
The Consolidated Standards of Reporting Trials extension for Artificial Intelligence interventions (CONSORT-AI) was published in September 2020. Since its publication, several randomised controlled trials (RCTs) of AI interventions have been published but their completeness and transparency of reporting is unknown. This systematic review assesses the completeness of reporting of AI RCTs following publication of CONSORT-AI and provides a comprehensive summary of RCTs published in recent years. 65 RCTs were identified, mostly conducted in China (37%) and USA (18%). Median concordance with CONSORT-AI reporting was 90% (IQR 77-94%), although only 10 RCTs explicitly reported its use. Several items were consistently under-reported, including algorithm version, accessibility of the AI intervention or code, and references to a study protocol. Only 3 of 52 included journals explicitly endorsed or mandated CONSORT-AI. Despite a generally high concordance amongst recent AI RCTs, some AI-specific considerations remain systematically poorly reported. Further encouragement of CONSORT-AI adoption by journals and funders may enable more complete adoption of the full CONSORT-AI guidelines.
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Affiliation(s)
| | - Carrie D Llewellyn
- Department of Primary Care and Public Health, Brighton and Sussex Medical School, Brighton, UK
| | - Richard O de Visser
- Department of Primary Care and Public Health, Brighton and Sussex Medical School, Brighton, UK
| | - Benjamin Ng
- Birmingham and Midland Eye Centre, Sandwell and West Birmingham NHS Trust, Birmingham, UK
- Christ Church, University of Oxford, Oxford, UK
| | - Victoria Ngai
- University College London Medical School, London, UK
| | - Aditya U Kale
- Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, UK
| | | | - Robert M Golub
- Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Gary S Collins
- Centre for Statistics in Medicine//UK EQUATOR Centre, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - David Moher
- Centre for Journalology, Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottowa, Canada
| | - Melissa D McCradden
- Department of Bioethics, The Hospital for Sick Children, Toronto, Canada
- Genetics & Genome Biology Research Program, Peter Gilgan Centre for Research & Learning, Toronto, Canada
- Division of Clinical and Public Health, Dalla Lana School of Public Health, Toronto, Canada
| | - Lauren Oakden-Rayner
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, Australia
| | - Samantha Cruz Rivera
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK
- Centre for Patient Reported Outcomes Research (CPROR), Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Melanie Calvert
- National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, UK
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK
- Centre for Patient Reported Outcomes Research (CPROR), Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
- NIHR Applied Research Collaboration (ARC) West Midlands, University of Birmingham, Birmingham, UK
- NIHR Blood and Transplant Research Unit (BTRU) in Precision Transplant and Cellular Therapeutics, University of Birmingham, Birmingham, UK
| | | | | | - Christopher Yau
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
- Health Data Research UK, London, UK
| | - An-Wen Chan
- Department of Medicine, Women's College Hospital. University of Toronto, Toronto, Canada
| | - Pearse A Keane
- NIHR Biomedical Research Centre at Moorfields, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Andrew L Beam
- Department of Epidemiology, Harvard. T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Alastair K Denniston
- Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, UK
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK
- NIHR Biomedical Research Centre at Moorfields, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Xiaoxuan Liu
- Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK.
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK.
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29
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Chino A, Ide D, Abe S, Yoshinaga S, Ichimasa K, Kudo T, Ninomiya Y, Oka S, Tanaka S, Igarashi M. Performance evaluation of a computer-aided polyp detection system with artificial intelligence for colonoscopy. Dig Endosc 2024; 36:185-194. [PMID: 37099623 DOI: 10.1111/den.14578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Accepted: 04/25/2023] [Indexed: 04/27/2023]
Abstract
OBJECTIVES A computer-aided detection (CAD) system was developed to support the detection of colorectal lesions by deep learning using video images of lesions and normal mucosa recorded during colonoscopy. The study's purpose was to evaluate the stand-alone performance of this device under blinded conditions. METHODS This multicenter prospective observational study was conducted at four Japanese institutions. We used 326 videos of colonoscopies recorded with patient consent at institutions in which the Ethics Committees approved the study. The sensitivity of successful detection of the CAD system was calculated using the target lesions, which were detected by adjudicators from two facilities for each lesion appearance frame; inconsistencies were settled by consensus. Successful detection was defined as display of the detection flag on the lesion for more than 0.5 s within 3 s of appearance. RESULTS Of the 556 target lesions from 185 cases, detection success sensitivity was 97.5% (95% confidence interval [CI] 95.8-98.5%). The "successful detection sensitivity per colonoscopy" was 93% (95% CI 88.3-95.8%). For the frame-based sensitivity, specificity, positive predictive value, and negative predictive value were 86.6% (95% CI 84.8-88.4%), 84.7% (95% CI 83.8-85.6%), 34.9% (95% CI 32.3-37.4%), and 98.2% (95% CI 97.8-98.5%), respectively. TRIAL REGISTRATION University Hospital Medical Information Network (UMIN000044622).
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Affiliation(s)
- Akiko Chino
- Department of Gastroenterology, Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Daisuke Ide
- Department of Gastroenterology, Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Seiichiro Abe
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | | | - Katsuro Ichimasa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Toyoki Kudo
- Tokyo Endoscopic Clinic, Tokyo, Japan
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Yuki Ninomiya
- Department of Endoscopy, Hiroshima University Hospital, Hiroshima, Japan
| | - Shiro Oka
- Department of Endoscopy, Hiroshima University Hospital, Hiroshima, Japan
| | - Shinji Tanaka
- Department of Endoscopy, Hiroshima University Hospital, Hiroshima, Japan
| | - Masahiro Igarashi
- Department of Gastroenterology, Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
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30
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Togashi K. Computer-aided detection in colonoscopic practice: Ready for prime time? Dig Endosc 2024; 36:49-50. [PMID: 37526067 DOI: 10.1111/den.14637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 07/12/2023] [Indexed: 08/02/2023]
Affiliation(s)
- Kazutomo Togashi
- Aizu Medical Center, Fukushima Medical University, Fukushima, Japan
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31
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Goetz N, Hanigan K, Cheng RKY. Artificial intelligence fails to improve colonoscopy quality: A single centre retrospective cohort study. Artif Intell Gastrointest Endosc 2023; 4:18-26. [DOI: 10.37126/aige.v4.i2.18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 11/07/2023] [Accepted: 11/30/2023] [Indexed: 12/07/2023] Open
Abstract
BACKGROUND Limited data currently exists on the clinical utility of Artificial Intelligence Assisted Colonoscopy (AIAC) outside of clinical trials.
AIM To evaluate the impact of AIAC on key markers of colonoscopy quality compared to conventional colonoscopy (CC).
METHODS This single-centre retrospective observational cohort study included all patients undergoing colonoscopy at a secondary centre in Brisbane, Australia. CC outcomes between October 2021 and October 2022 were compared with AIAC outcomes after the introduction of the Olympus Endo-AID module from October 2022 to January 2023. Endoscopists who conducted over 50 procedures before and after AIAC introduction were included. Procedures for surveillance of inflammatory bowel disease were excluded. Patient demographics, proceduralist specialisation, indication for colonoscopy, and colonoscopy quality metrics were collected. Adenoma detection rate (ADR) and sessile serrated lesion detection rate (SSLDR) were calculated for both AIAC and CC.
RESULTS The study included 746 AIAC procedures and 2162 CC procedures performed by seven endoscopists. Baseline patient demographics were similar, with median age of 60 years with a slight female predominance (52.1%). Procedure indications, bowel preparation quality, and caecal intubation rates were comparable between groups. AIAC had a slightly longer withdrawal time compared to CC, but the difference was not statistically significant. The introduction of AIAC did not significantly change ADR (52.1% for AIAC vs 52.6% for CC, P = 0.91) or SSLDR (17.4% for AIAC vs 18.1% for CC, P = 0.44).
CONCLUSION The implementation of AIAC failed to improve key markers of colonoscopy quality, including ADR, SSLDR and withdrawal time. Further research is required to assess the utility and cost-efficiency of AIAC for high performing endoscopists.
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Affiliation(s)
- Naeman Goetz
- Department of Gastroenterology, Redcliffe Hospital, Redcliffe 4020, Australia
| | - Katherine Hanigan
- Department of Gastroenterology, Redcliffe Hospital, Redcliffe 4020, Australia
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Lou S, Du F, Song W, Xia Y, Yue X, Yang D, Cui B, Liu Y, Han P. Artificial intelligence for colorectal neoplasia detection during colonoscopy: a systematic review and meta-analysis of randomized clinical trials. EClinicalMedicine 2023; 66:102341. [PMID: 38078195 PMCID: PMC10698672 DOI: 10.1016/j.eclinm.2023.102341] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 11/14/2023] [Accepted: 11/15/2023] [Indexed: 05/11/2024] Open
Abstract
BACKGROUND The use of artificial intelligence (AI) in detecting colorectal neoplasia during colonoscopy holds the potential to enhance adenoma detection rates (ADRs) and reduce adenoma miss rates (AMRs). However, varied outcomes have been observed across studies. Thus, this study aimed to evaluate the potential advantages and disadvantages of employing AI-aided systems during colonoscopy. METHODS Using Medical Subject Headings (MeSH) terms and keywords, a comprehensive electronic literature search was performed of the Embase, Medline, and the Cochrane Library databases from the inception of each database until October 04, 2023, in order to identify randomized controlled trials (RCTs) comparing AI-assisted with standard colonoscopy for detecting colorectal neoplasia. Primary outcomes included AMR, ADR, and adenomas detected per colonoscopy (APC). Secondary outcomes comprised the poly missed detection rate (PMR), poly detection rate (PDR), and poly detected per colonoscopy (PPC). We utilized random-effects meta-analyses with Hartung-Knapp adjustment to consolidate results. The prediction interval (PI) and I2 statistics were utilized to quantify between-study heterogeneity. Moreover, meta-regression and subgroup analyses were performed to investigate the potential sources of heterogeneity. This systematic review and meta-analysis is registered with PROSPERO (CRD42023428658). FINDINGS This study encompassed 33 trials involving 27,404 patients. Those undergoing AI-aided colonoscopy experienced a significant decrease in PMR (RR, 0.475; 95% CI, 0.294-0.768; I2 = 87.49%) and AMR (RR, 0.495; 95% CI, 0.390-0.627; I2 = 48.76%). Additionally, a significant increase in PDR (RR, 1.238; 95% CI, 1.158-1.323; I2 = 81.67%) and ADR (RR, 1.242; 95% CI, 1.159-1.332; I2 = 78.87%), along with a significant increase in the rates of PPC (IRR, 1.388; 95% CI, 1.270-1.517; I2 = 91.99%) and APC (IRR, 1.390; 95% CI, 1.277-1.513; I2 = 86.24%), was observed. This resulted in 0.271 more PPCs (95% CI, 0.144-0.259; I2 = 65.61%) and 0.202 more APCs (95% CI, 0.144-0.259; I2 = 68.15%). INTERPRETATION AI-aided colonoscopy significantly enhanced the detection of colorectal neoplasia detection, likely by reducing the miss rate. However, future studies should focus on evaluating the cost-effectiveness and long-term benefits of AI-aided colonoscopy in reducing cancer incidence. FUNDING This work was supported by the Heilongjiang Provincial Natural Science Foundation of China (LH2023H096), the Postdoctoral research project in Heilongjiang Province (LBH-Z22210), the National Natural Science Foundation of China's General Program (82072640) and the Outstanding Youth Project of Heilongjiang Natural Science Foundation (YQ2021H023).
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Affiliation(s)
- Shenghan Lou
- Department of Colorectal Surgery, Harbin Medical University Cancer Hospital, No.150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Fenqi Du
- Department of Colorectal Surgery, Harbin Medical University Cancer Hospital, No.150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Wenjie Song
- Department of Colorectal Surgery, Harbin Medical University Cancer Hospital, No.150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Yixiu Xia
- Department of Colorectal Surgery, Harbin Medical University Cancer Hospital, No.150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Xinyu Yue
- Department of Colorectal Surgery, Harbin Medical University Cancer Hospital, No.150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Da Yang
- Department of Colorectal Surgery, Harbin Medical University Cancer Hospital, No.150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Binbin Cui
- Department of Colorectal Surgery, Harbin Medical University Cancer Hospital, No.150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Yanlong Liu
- Department of Colorectal Surgery, Harbin Medical University Cancer Hospital, No.150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Peng Han
- Department of Colorectal Surgery, Harbin Medical University Cancer Hospital, No.150 Haping Road, Harbin, Heilongjiang, 150081, China
- Key Laboratory of Tumor Immunology in Heilongjiang, No.150 Haping Road, Harbin, Heilongjiang, 150081, China
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Sekiguchi M, Igarashi A, Toyoshima N, Takamaru H, Yamada M, Esaki M, Kobayashi N, Saito Y. Cost-effectiveness analysis of computer-aided detection systems for colonoscopy in Japan. Dig Endosc 2023; 35:891-899. [PMID: 36752676 DOI: 10.1111/den.14532] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 02/05/2023] [Indexed: 02/09/2023]
Abstract
OBJECTIVES The usefulness of computer-aided detection systems (CADe) for colonoscopy has been increasingly reported. In many countries, however, data on the cost-effectiveness of their use are lacking; consequently, CADe for colonoscopy has not been covered by health insurance. We aimed to evaluate the cost-effectiveness of colonoscopy using CADe in Japan. METHODS We conducted a simulation model analysis using Japanese data to examine the cost-effectiveness of colonoscopy with and without CADe for a population aged 40-74 years who received colorectal cancer (CRC) screening with a fecal immunochemical test (FIT). The rates of receiving FIT screening and colonoscopy following a positive FIT were set as 40% and 70%, respectively. The sensitivities of FIT for advanced adenomas and CRC Dukes' A-D were 26.5% and 52.8-78.3%, respectively. CADe colonoscopy was judged to be cost-effective when its incremental cost-effectiveness ratio (ICER) was below JPY 5,000,000 per quality-adjusted life-years (QALYs) gained. RESULTS Compared to conventional colonoscopy, CADe colonoscopy showed a higher QALY (20.4098 vs. 20.4088) and lower CRC incidence (2373 vs. 2415 per 100,000) and mortality (561 vs. 569 per 100,000). When the CADe cost was set at JPY 1000-6000, the ICER per QALY gained for CADe colonoscopy was lower than JPY 5,000,000 (JPY 796,328-4,971,274). The CADe cost threshold at which the ICER for CADe colonoscopy exceeded JPY 5,000,000 was JPY 6040. CONCLUSIONS Computer-aided detection systems for colonoscopy has the potential to be cost-effective when the CADe cost is up to JPY 6000. These results suggest that the insurance reimbursement of CADe for colonoscopy is reasonable.
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Affiliation(s)
- Masau Sekiguchi
- Cancer Screening Center, National Cancer Center Hospital, Tokyo, Japan
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
- Division of Screening Technology, National Cancer Center Institute for Cancer Control, Tokyo, Japan
| | - Ataru Igarashi
- Department of Health Economics and Outcomes Research, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan
- Department of Public Health, Yokohama City University School of Medicine, Kanagawa, Japan
| | - Naoya Toyoshima
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | | | - Masayoshi Yamada
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | - Minoru Esaki
- Hepatobiliary and Pancreatic Surgery Division, National Cancer Center Hospital, Tokyo, Japan
| | - Nozomu Kobayashi
- Cancer Screening Center, National Cancer Center Hospital, Tokyo, Japan
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
- Division of Screening Technology, National Cancer Center Institute for Cancer Control, Tokyo, Japan
| | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
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Leśniewska M, Patryn R, Kopystecka A, Kozioł I, Budzyńska J. Third Eye? The Assistance of Artificial Intelligence (AI) in the Endoscopy of Gastrointestinal Neoplasms. J Clin Med 2023; 12:6721. [PMID: 37959187 PMCID: PMC10650785 DOI: 10.3390/jcm12216721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 10/19/2023] [Accepted: 10/21/2023] [Indexed: 11/15/2023] Open
Abstract
Gastrointestinal cancers are characterized by high incidence and mortality. However, there are well-established methods of screening. The endoscopy exam provides the macroscopical image and enables harvesting the tissue samples for further histopathological diagnosis. The efficiency of endoscopies relies not only on proper patient preparation, but also on the skills of the personnel conducting the exam. In recent years, a number of reports concerning the application of artificial intelligence (AI) in medicine have arisen. Numerous studies aimed to assess the utility of deep learning/ neural network systems supporting endoscopies. In this review, we summarized the most recent reports and randomized clinical trials regarding the application of AI in screening and surveillance of gastrointestinal cancers among patients suffering from esophageal, gastric, and colorectal cancer, along with the advantages, limitations, and controversies of those novel solutions.
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Affiliation(s)
- Magdalena Leśniewska
- Students’ Scientific Circle on Medical Law at the Department of Humanities and Social Medicine, Medical University of Lublin, 20-093 Lublin, Poland; (M.L.); (A.K.); (I.K.); (J.B.)
| | - Rafał Patryn
- Department of Humanities and Social Medicine, Medical University of Lublin, 20-093 Lublin, Poland
| | - Agnieszka Kopystecka
- Students’ Scientific Circle on Medical Law at the Department of Humanities and Social Medicine, Medical University of Lublin, 20-093 Lublin, Poland; (M.L.); (A.K.); (I.K.); (J.B.)
| | - Ilona Kozioł
- Students’ Scientific Circle on Medical Law at the Department of Humanities and Social Medicine, Medical University of Lublin, 20-093 Lublin, Poland; (M.L.); (A.K.); (I.K.); (J.B.)
| | - Julia Budzyńska
- Students’ Scientific Circle on Medical Law at the Department of Humanities and Social Medicine, Medical University of Lublin, 20-093 Lublin, Poland; (M.L.); (A.K.); (I.K.); (J.B.)
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Young E, Edwards L, Singh R. The Role of Artificial Intelligence in Colorectal Cancer Screening: Lesion Detection and Lesion Characterization. Cancers (Basel) 2023; 15:5126. [PMID: 37958301 PMCID: PMC10647850 DOI: 10.3390/cancers15215126] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 10/14/2023] [Accepted: 10/14/2023] [Indexed: 11/15/2023] Open
Abstract
Colorectal cancer remains a leading cause of cancer-related morbidity and mortality worldwide, despite the widespread uptake of population surveillance strategies. This is in part due to the persistent development of 'interval colorectal cancers', where patients develop colorectal cancer despite appropriate surveillance intervals, implying pre-malignant polyps were not resected at a prior colonoscopy. Multiple techniques have been developed to improve the sensitivity and accuracy of lesion detection and characterisation in an effort to improve the efficacy of colorectal cancer screening, thereby reducing the incidence of interval colorectal cancers. This article presents a comprehensive review of the transformative role of artificial intelligence (AI), which has recently emerged as one such solution for improving the quality of screening and surveillance colonoscopy. Firstly, AI-driven algorithms demonstrate remarkable potential in addressing the challenge of overlooked polyps, particularly polyp subtypes infamous for escaping human detection because of their inconspicuous appearance. Secondly, AI empowers gastroenterologists without exhaustive training in advanced mucosal imaging to characterise polyps with accuracy similar to that of expert interventionalists, reducing the dependence on pathologic evaluation and guiding appropriate resection techniques or referrals for more complex resections. AI in colonoscopy holds the potential to advance the detection and characterisation of polyps, addressing current limitations and improving patient outcomes. The integration of AI technologies into routine colonoscopy represents a promising step towards more effective colorectal cancer screening and prevention.
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Affiliation(s)
- Edward Young
- Faculty of Health and Medical Sciences, University of Adelaide, Lyell McEwin Hospital, Haydown Rd, Elizabeth Vale, SA 5112, Australia
| | - Louisa Edwards
- Faculty of Health and Medical Sciences, University of Adelaide, Queen Elizabeth Hospital, Port Rd, Woodville South, SA 5011, Australia
| | - Rajvinder Singh
- Faculty of Health and Medical Sciences, University of Adelaide, Lyell McEwin Hospital, Haydown Rd, Elizabeth Vale, SA 5112, Australia
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Wei MT, Shankar U, Parvin R, Abbas SH, Chaudhary S, Friedlander Y, Friedland S. Evaluation of Computer-Aided Detection During Colonoscopy in the Community (AI-SEE): A Multicenter Randomized Clinical Trial. Am J Gastroenterol 2023; 118:1841-1847. [PMID: 36892545 DOI: 10.14309/ajg.0000000000002239] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 03/07/2023] [Indexed: 03/10/2023]
Abstract
INTRODUCTION There has been increasing interest in artificial intelligence in gastroenterology. To reduce miss rates during colonoscopy, there has been significant exploration in computer-aided detection (CADe) devices. In this study, we evaluate the use of CADe in colonoscopy in community-based, nonacademic practices. METHODS Between September 28, 2020, and September 24, 2021, a randomized controlled trial (AI-SEE) was performed evaluating the impact of CADe on polyp detection in 4 community-based endoscopy centers in the United States Patients were block-randomized to undergoing colonoscopy with or without CADe (EndoVigilant). Primary outcomes measured were adenomas per colonoscopy and adenomas per extraction (the percentage of polyps removed that are adenomas). Secondary end points included serrated polyps per colonoscopy; nonadenomatous, nonserrated polyps per colonoscopy; adenoma and serrated polyp detection rates; and procedural time. RESULTS A total of 769 patients were enrolled (387 with CADe), with similar patient demographics between the 2 groups. There was no significant difference in adenomas per colonoscopy in the CADe and non-CADe groups (0.73 vs 0.67, P = 0.496). Although the use of CADe did not improve identification of serrated polyps per colonoscopy (0.08 vs 0.08, P = 0.965), the use of CADe increased identification of nonadenomatous, nonserrated polyps per colonoscopy (0.90 vs 0.51, P < 0.0001), resulting in detection of fewer adenomas per extraction in the CADe group. The adenoma detection rate (35.9 vs 37.2%, P = 0.774) and serrated polyp detection rate (6.5 vs 6.3%, P = 1.000) were similar in the CADe and non-CADe groups. Mean withdrawal time was longer in the CADe group compared with the non-CADe group (11.7 vs 10.7 minutes, P = 0.003). However, when no polyps were identified, there was similar mean withdrawal time (9.1 vs 8.8 minutes, P = 0.288). There were no adverse events. DISCUSSION The use of CADe did not result in a statistically significant difference in the number of adenomas detected. Additional studies are needed to better understand why some endoscopists derive substantial benefits from CADe and others do not. ClinicalTrials.gov number: NCT04555135.
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Affiliation(s)
- Mike T Wei
- Stanford University, Stanford, California, USA
| | - Uday Shankar
- Trinity Health of New England, Waterbury, Connecticut, USA
| | - Russell Parvin
- Trinity Health of New England, Waterbury, Connecticut, USA
| | | | | | | | - Shai Friedland
- Stanford University, Stanford, California, USA
- Veterans Affairs Palo Alto Health Care System, Palo Alto, California, USA
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Jing Y, Li C, Du T, Jiang T, Sun H, Yang J, Shi L, Gao M, Grzegorzek M, Li X. A comprehensive survey of intestine histopathological image analysis using machine vision approaches. Comput Biol Med 2023; 165:107388. [PMID: 37696178 DOI: 10.1016/j.compbiomed.2023.107388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 08/06/2023] [Accepted: 08/25/2023] [Indexed: 09/13/2023]
Abstract
Colorectal Cancer (CRC) is currently one of the most common and deadly cancers. CRC is the third most common malignancy and the fourth leading cause of cancer death worldwide. It ranks as the second most frequent cause of cancer-related deaths in the United States and other developed countries. Histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of CRC. In order to improve the objectivity and diagnostic efficiency for image analysis of intestinal histopathology, Computer-aided Diagnosis (CAD) methods based on machine learning (ML) are widely applied in image analysis of intestinal histopathology. In this investigation, we conduct a comprehensive study on recent ML-based methods for image analysis of intestinal histopathology. First, we discuss commonly used datasets from basic research studies with knowledge of intestinal histopathology relevant to medicine. Second, we introduce traditional ML methods commonly used in intestinal histopathology, as well as deep learning (DL) methods. Then, we provide a comprehensive review of the recent developments in ML methods for segmentation, classification, detection, and recognition, among others, for histopathological images of the intestine. Finally, the existing methods have been studied, and the application prospects of these methods in this field are given.
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Affiliation(s)
- Yujie Jing
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Chen Li
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China.
| | - Tianming Du
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Tao Jiang
- School of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China; International Joint Institute of Robotics and Intelligent Systems, Chengdu University of Information Technology, Chengdu, China
| | - Hongzan Sun
- Shengjing Hospital of China Medical University, Shenyang, China
| | - Jinzhu Yang
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Liyu Shi
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Minghe Gao
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Marcin Grzegorzek
- Institute for Medical Informatics, University of Luebeck, Luebeck, Germany; Department of Knowledge Engineering, University of Economics in Katowice, Katowice, Poland
| | - Xiaoyan Li
- Cancer Hospital of China Medical University, Liaoning Cancer Hospital, Shenyang, China.
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Mangas-Sanjuan C, de-Castro L, Cubiella J, Díez-Redondo P, Suárez A, Pellisé M, Fernández N, Zarraquiños S, Núñez-Rodríguez H, Álvarez-García V, Ortiz O, Sala-Miquel N, Zapater P, Jover R. Role of Artificial Intelligence in Colonoscopy Detection of Advanced Neoplasias : A Randomized Trial. Ann Intern Med 2023; 176:1145-1152. [PMID: 37639723 DOI: 10.7326/m22-2619] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND The role of computer-aided detection in identifying advanced colorectal neoplasia is unknown. OBJECTIVE To evaluate the contribution of computer-aided detection to colonoscopic detection of advanced colorectal neoplasias as well as adenomas, serrated polyps, and nonpolypoid and right-sided lesions. DESIGN Multicenter, parallel, randomized controlled trial. (ClinicalTrials.gov: NCT04673136). SETTING Spanish colorectal cancer screening program. PARTICIPANTS 3213 persons with a positive fecal immunochemical test. INTERVENTION Enrollees were randomly assigned to colonoscopy with or without computer-aided detection. MEASUREMENTS Advanced colorectal neoplasia was defined as advanced adenoma and/or advanced serrated polyp. RESULTS The 2 comparison groups showed no significant difference in advanced colorectal neoplasia detection rate (34.8% with intervention vs. 34.6% for controls; adjusted risk ratio [aRR], 1.01 [95% CI, 0.92 to 1.10]) or the mean number of advanced colorectal neoplasias detected per colonoscopy (0.54 [SD, 0.95] with intervention vs. 0.52 [SD, 0.95] for controls; adjusted rate ratio, 1.04 [99.9% CI, 0.88 to 1.22]). Adenoma detection rate also did not differ (64.2% with intervention vs. 62.0% for controls; aRR, 1.06 [99.9% CI, 0.91 to 1.23]). Computer-aided detection increased the mean number of nonpolypoid lesions (0.56 [SD, 1.25] vs. 0.47 [SD, 1.18] for controls; adjusted rate ratio, 1.19 [99.9% CI, 1.01 to 1.41]), proximal adenomas (0.94 [SD, 1.62] vs. 0.81 [SD, 1.52] for controls; adjusted rate ratio, 1.17 [99.9% CI, 1.03 to 1.33]), and lesions of 5 mm or smaller (polyps in general and adenomas and serrated lesions in particular) detected per colonoscopy. LIMITATIONS The high adenoma detection rate in the control group may limit the generalizability of the findings to endoscopists with low detection rates. CONCLUSION Computer-aided detection did not improve colonoscopic identification of advanced colorectal neoplasias. PRIMARY FUNDING SOURCE Medtronic.
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Affiliation(s)
- Carolina Mangas-Sanjuan
- Department of Gastroenterology, Hospital General Universitario Dr. Balmis, Servicio de Medicina Digestiva, Instituto de Investigación Sanitaria y Biomédica de Alicante (ISABIAL), Spain (C.M., N.S.)
| | - Luisa de-Castro
- Department of Gastroenterology, Hospital Álvaro Cunqueiro, Digestive Pathology Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain (L. de-C., N.F.)
| | - Joaquín Cubiella
- Department of Gastroenterology, Hospital Universitario de Ourense, Centro de Investigación Biomédica en Red Enfermedades Hepáticas y Digestivas (CIBERehd), Ourense, Spain (J.C., S.Z.)
| | - Pilar Díez-Redondo
- Department of Gastroenterology, Hospital Río-Hortega, Valladolid, Spain (P.D., H.N.)
| | - Adolfo Suárez
- Department of Gastroenterology, Hospital Central de Asturias, Oviedo, Spain (A.S., V.A.)
| | - María Pellisé
- Department of Gastroenterology, Hospital Clínic Barcelona, Barcelona, Spain (M.P., O.O.)
| | - Nereida Fernández
- Department of Gastroenterology, Hospital Álvaro Cunqueiro, Digestive Pathology Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain (L. de-C., N.F.)
| | - Sara Zarraquiños
- Department of Gastroenterology, Hospital Universitario de Ourense, Centro de Investigación Biomédica en Red Enfermedades Hepáticas y Digestivas (CIBERehd), Ourense, Spain (J.C., S.Z.)
| | - Henar Núñez-Rodríguez
- Department of Gastroenterology, Hospital Río-Hortega, Valladolid, Spain (P.D., H.N.)
| | | | - Oswaldo Ortiz
- Department of Gastroenterology, Hospital Clínic Barcelona, Barcelona, Spain (M.P., O.O.)
| | - Noelia Sala-Miquel
- Department of Gastroenterology, Hospital General Universitario Dr. Balmis, Servicio de Medicina Digestiva, Instituto de Investigación Sanitaria y Biomédica de Alicante (ISABIAL), Spain (C.M., N.S.)
| | - Pedro Zapater
- Hospital General Universitario Dr. Balmis, Clinical Pharmacology Department, Instituto de Investigación Sanitaria y Biomédica de Alicante (ISABIAL), Departamento de Farmacología, Universidad Miguel Hernández, Alicante, CIBERehd, Spain (P.Z.)
| | - Rodrigo Jover
- Department of Gastroenterology, Hospital General Universitario Dr. Balmis, Servicio de Medicina Digestiva, Instituto de Investigación Sanitaria y Biomédica de Alicante (ISABIAL), Departamento de Medicina Clínica, Universidad Miguel Hernández, Alicante, Spain (R.J.)
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Hassan C, Spadaccini M, Mori Y, Foroutan F, Facciorusso A, Gkolfakis P, Tziatzios G, Triantafyllou K, Antonelli G, Khalaf K, Rizkala T, Vandvik PO, Fugazza A, Rondonotti E, Glissen-Brown JR, Kamba S, Maida M, Correale L, Bhandari P, Jover R, Sharma P, Rex DK, Repici A. Real-Time Computer-Aided Detection of Colorectal Neoplasia During Colonoscopy : A Systematic Review and Meta-analysis. Ann Intern Med 2023; 176:1209-1220. [PMID: 37639719 DOI: 10.7326/m22-3678] [Citation(s) in RCA: 77] [Impact Index Per Article: 38.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND Artificial intelligence computer-aided detection (CADe) of colorectal neoplasia during colonoscopy may increase adenoma detection rates (ADRs) and reduce adenoma miss rates, but it may increase overdiagnosis and overtreatment of nonneoplastic polyps. PURPOSE To quantify the benefits and harms of CADe in randomized trials. DESIGN Systematic review and meta-analysis. (PROSPERO: CRD42022293181). DATA SOURCES Medline, Embase, and Scopus databases through February 2023. STUDY SELECTION Randomized trials comparing CADe-assisted with standard colonoscopy for polyp and cancer detection. DATA EXTRACTION Adenoma detection rate (proportion of patients with ≥1 adenoma), number of adenomas detected per colonoscopy, advanced adenoma (≥10 mm with high-grade dysplasia and villous histology), number of serrated lesions per colonoscopy, and adenoma miss rate were extracted as benefit outcomes. Number of polypectomies for nonneoplastic lesions and withdrawal time were extracted as harm outcomes. For each outcome, studies were pooled using a random-effects model. Certainty of evidence was assessed using the GRADE (Grading of Recommendations Assessment, Development and Evaluation) framework. DATA SYNTHESIS Twenty-one randomized trials on 18 232 patients were included. The ADR was higher in the CADe group than in the standard colonoscopy group (44.0% vs. 35.9%; relative risk, 1.24 [95% CI, 1.16 to 1.33]; low-certainty evidence), corresponding to a 55% (risk ratio, 0.45 [CI, 0.35 to 0.58]) relative reduction in miss rate (moderate-certainty evidence). More nonneoplastic polyps were removed in the CADe than the standard group (0.52 vs. 0.34 per colonoscopy; mean difference [MD], 0.18 polypectomy [CI, 0.11 to 0.26 polypectomy]; low-certainty evidence). Mean inspection time increased only marginally with CADe (MD, 0.47 minute [CI, 0.23 to 0.72 minute]; moderate-certainty evidence). LIMITATIONS This review focused on surrogates of patient-important outcomes. Most patients, however, may consider cancer incidence and cancer-related mortality important outcomes. The effect of CADe on such patient-important outcomes remains unclear. CONCLUSION The use of CADe for polyp detection during colonoscopy results in increased detection of adenomas but not advanced adenomas and in higher rates of unnecessary removal of nonneoplastic polyps. PRIMARY FUNDING SOURCE European Commission Horizon 2020 Marie Skłodowska-Curie Individual Fellowship.
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Affiliation(s)
- Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, and Endoscopy Unit, Humanitas Clinical and Research Center IRCCS, Rozzano, Italy (C.H., M.S., A.R.)
| | - Marco Spadaccini
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, and Endoscopy Unit, Humanitas Clinical and Research Center IRCCS, Rozzano, Italy (C.H., M.S., A.R.)
| | - Yuichi Mori
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway, and Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan (Y.M.)
| | - Farid Foroutan
- Ted Rogers Centre for Heart Research, University Health Network, Toronto, Ontario, Canada (F.F.)
| | - Antonio Facciorusso
- Department of Medical Sciences, Section of Gastroenterology, University of Foggia, Foggia, Italy (A.Facciorusso)
| | - Paraskevas Gkolfakis
- Department of Gastroenterology, Hepatopancreatology, and Digestive Oncology, Erasme Hospital, Université Libre de Bruxelles, Brussels, Belgium (P.G.)
| | - Georgios Tziatzios
- Hepatogastroenterology Unit, Second Department of Internal Medicine-Propaedeutic, Medical School, National and Kapodistrian University of Athens, Attikon University General Hospital, Athens, Greece (G.T., K.T.)
| | - Konstantinos Triantafyllou
- Hepatogastroenterology Unit, Second Department of Internal Medicine-Propaedeutic, Medical School, National and Kapodistrian University of Athens, Attikon University General Hospital, Athens, Greece (G.T., K.T.)
| | - Giulio Antonelli
- Gastroenterology and Digestive Endoscopy Unit, Ospedale dei Castelli, Ariccia, and Department of Anatomical, Histological, Forensic Medicine and Orthopedics Sciences, Sapienza University of Rome, Rome, Italy (G.A.)
| | - Kareem Khalaf
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy (K.K., T.R.)
| | - Tommy Rizkala
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy (K.K., T.R.)
| | - Per Olav Vandvik
- Department of Medicine, Lovisenberg Diaconal Hospital, Oslo, Norway (P.O.V.)
| | - Alessandro Fugazza
- Endoscopy Unit, Humanitas Clinical and Research Center IRCCS, Rozzano, Italy (A.Fugazza, L.C.)
| | | | - Jeremy R Glissen-Brown
- Center for Advanced Endoscopy, Division of Gastroenterology and Hepatology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts (J.R.G.)
| | - Shunsuke Kamba
- Department of Endoscopy, The Jikei University School of Medicine, Tokyo, Japan (S.K.)
| | - Marcello Maida
- Gastroenterology and Endoscopy Unit, S. Elia-Raimondi Hospital, Caltanissetta, Italy (M.M.)
| | - Loredana Correale
- Endoscopy Unit, Humanitas Clinical and Research Center IRCCS, Rozzano, Italy (A.Fugazza, L.C.)
| | - Pradeep Bhandari
- Department of Gastroenterology, Queen Alexandra Hospital, Portsmouth, United Kingdom (P.B.)
| | - Rodrigo Jover
- Departamento de Medicina Clínica, Servicio de Gastroenterología, Hospital General Universitario Dr. Balmis, Instituto de Investigación Biomédica de Alicante ISABIAL, Universidad Miguel Hernández, Alicante, Spain (R.J.)
| | - Prateek Sharma
- Gastroenterology and Hepatology, Kansas City VA Medical Center, Kansas City, Missouri (P.S.)
| | - Douglas K Rex
- Division of Gastroenterology, Indiana University School of Medicine, Indianapolis, Indiana (D.K.R.)
| | - Alessandro Repici
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, and Endoscopy Unit, Humanitas Clinical and Research Center IRCCS, Rozzano, Italy (C.H., M.S., A.R.)
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Thomas J, Ravichandran R, Nag A, Gupta L, Singh M, Panjiyar BK. Advancing Colorectal Cancer Screening: A Comprehensive Systematic Review of Artificial Intelligence (AI)-Assisted Versus Routine Colonoscopy. Cureus 2023; 15:e45278. [PMID: 37846251 PMCID: PMC10576852 DOI: 10.7759/cureus.45278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/14/2023] [Indexed: 10/18/2023] Open
Abstract
Colorectal cancer (CRC) is a rapidly escalating public health concern, which underlines the significance of its early detection and the need for the refinement of current screening methods. In this systematic review, we aimed to analyze the potential advantages and limitations of artificial intelligence (AI)-based computer-aided detection (CADe) systems as compared to routine colonoscopy. This review begins by shedding light on the global prevalence and mortality rates of CRC, highlighting the urgent need for effective screening techniques and early detection of this cancer type. It addresses the problems associated with undetected adenomas and polyps and the subsequent risk of interval CRC following colonoscopy. The incorporation of AI into diagnostics has been studied, specifically the use of CADe systems which are powered by deep learning. The review summarizes the findings from 13 randomized controlled trials (RCTs) (2019-2023), evaluating the impact of CADe on polyp and adenoma detection. The findings from the studies consistently show that CADe is superior to conventional colonoscopy procedures in terms of adenoma detection rate (ADR) and polyp detection rate (PDR), particularly with regard to small and flat lesions which are easily overlooked. The review acknowledges certain limitations of the included studies, such as potential performance bias and geographic limitations. The review ultimately concludes that AI-assisted colonoscopy can reduce missed lesion rates and improve CRC diagnosis. Collaboration between experts and clinicians is key for successful implementation. In summary, this review analyzes recent RCTs on AI-assisted colonoscopy for polyp and adenoma detection. It describes the likely benefits, limitations, and future implications of AI in enhancing colonoscopy procedures and lowering the incidence of CRC. More double-blinded trials and studies among diverse populations from different countries must be conducted to substantiate and expand upon the findings of this review.
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Affiliation(s)
- Jingle Thomas
- Internal Medicine, Al-Ameen Medical College, Vijayapura, IND
| | | | - Aiswarya Nag
- Internal Medicine, Sri Ramachandra Institute of Higher Education and Research, Chennai, IND
| | - Lovish Gupta
- Internal Medicine, Maulana Azad Medical College, New Delhi, IND
| | - Mansi Singh
- Medicine, O.O. Bogomolets National Medical University, Kyiv, UKR
| | - Binay K Panjiyar
- GCSRT, PGMEE, Harvard Medical School, Boston, USA
- Internal Medicine, California Institute of Behavioral Neurosciences & Psychology, Fairfield, USA
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Karsenti D, Tharsis G, Perrot B, Cattan P, Percie du Sert A, Venezia F, Zrihen E, Gillet A, Lab JP, Tordjman G, Cavicchi M. Effect of real-time computer-aided detection of colorectal adenoma in routine colonoscopy (COLO-GENIUS): a single-centre randomised controlled trial. Lancet Gastroenterol Hepatol 2023; 8:726-734. [PMID: 37269872 DOI: 10.1016/s2468-1253(23)00104-8] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 04/03/2023] [Accepted: 04/06/2023] [Indexed: 06/05/2023]
Abstract
BACKGROUND Artificial intelligence systems have been developed to improve polyp detection. We aimed to evaluate the effect of real-time computer-aided detection (CADe) on the adenoma detection rate (ADR) in routine colonoscopy. METHODS This single-centre randomised controlled trial (COLO-GENIUS) was done at the Digestive Endoscopy Unit, Pôle Digestif Paris-Bercy, Clinique Paris-Bercy, Charenton-le-Pont, France. All consecutive individuals aged 18 years or older who were scheduled for a total colonoscopy and had an American Society of Anesthesiologists score of 1-3 were screened for inclusion. After the caecum was reached and the colonic preparation was appropriate, eligible participants were randomly assigned (1:1; computer-generated random numbers list) to either standard colonoscopy or CADe-assisted colonoscopy (GI Genius 2.0.2; Medtronic). Participants and cytopathologists were masked to study assignment, whereas endoscopists were not. The primary outcome was ADR, which was assessed in the modified intention-to-treat population (all randomly assigned participants except those with misplaced consent forms). Safety was analysed in all included patients. According to statistical calculations, 20 endoscopists from the Clinique Paris-Bercy had to include approximately 2100 participants with 1:1 randomisation. The trial is complete and registered with ClinicalTrials.gov, NCT04440865. FINDINGS Between May 1, 2021, and May 1, 2022, 2592 participants were assessed for eligibility, of whom 2039 were randomly assigned to standard colonoscopy (n=1026) or CADe-assisted colonoscopy (n=1013). 14 participants in the standard group and ten participants in the CADe group were then excluded due to misplaced consent forms, leaving 2015 participants (979 [48·6%] men and 1036 [51·4%] women) in the modified intention-to-treat analysis. ADR was 33·7% (341 of 1012 colonoscopies) in the standard group and 37·5% (376 of 1003 colonoscopies) in the CADe group (estimated mean absolute difference 4·1 percentage points [95% CI 0·0-8·1]; p=0·051). One bleeding event without deglobulisation occurred in the CADe group after a large (>2 cm) polyp resection and resolved after a haemostasis clip was placed during a second colonoscopy. INTERPRETATION Our findings support the benefits of CADe, even in a non-academic centre. Systematic use of CADe in routine colonoscopy should be considered. FUNDING None.
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Affiliation(s)
- David Karsenti
- Digestive Endoscopy Unit, Pôle Digestif Paris-Bercy, Clinique Paris-Bercy, Charenton-le-Pont, France.
| | - Gaëlle Tharsis
- Digestive Endoscopy Unit, Pôle Digestif Paris-Bercy, Clinique Paris-Bercy, Charenton-le-Pont, France
| | - Bastien Perrot
- UMR 1246 SPHERE, INSERM, Nantes University and Tours University, Nantes, France
| | - Philippe Cattan
- Digestive Endoscopy Unit, Pôle Digestif Paris-Bercy, Clinique Paris-Bercy, Charenton-le-Pont, France
| | - Alice Percie du Sert
- Digestive Endoscopy Unit, Pôle Digestif Paris-Bercy, Clinique Paris-Bercy, Charenton-le-Pont, France
| | - Franck Venezia
- Digestive Endoscopy Unit, Pôle Digestif Paris-Bercy, Clinique Paris-Bercy, Charenton-le-Pont, France
| | - Elie Zrihen
- Digestive Endoscopy Unit, Pôle Digestif Paris-Bercy, Clinique Paris-Bercy, Charenton-le-Pont, France
| | - Agnès Gillet
- Digestive Endoscopy Unit, Pôle Digestif Paris-Bercy, Clinique Paris-Bercy, Charenton-le-Pont, France
| | | | - Gilles Tordjman
- Digestive Endoscopy Unit, Pôle Digestif Paris-Bercy, Clinique Paris-Bercy, Charenton-le-Pont, France
| | - Maryan Cavicchi
- Digestive Endoscopy Unit, Pôle Digestif Paris-Bercy, Clinique Paris-Bercy, Charenton-le-Pont, France
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Utsumi T, Yamada Y, Diaz-Meco MT, Moscat J, Nakanishi Y. Sessile serrated lesions with dysplasia: is it possible to nip them in the bud? J Gastroenterol 2023; 58:705-717. [PMID: 37219625 PMCID: PMC10366009 DOI: 10.1007/s00535-023-02003-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 05/14/2023] [Indexed: 05/24/2023]
Abstract
The serrated neoplasia pathway constitutes an "alternative route" to colorectal cancer (CRC), and sessile serrated lesions with dysplasia (SSLDs) are an intermediate step between sessile serrated lesions (SSLs) and invasive CRC in this pathway. While SSLs show indolent growth before becoming dysplastic (> 10-15 years), SSLDs are considered to rapidly progress to either immunogenic microsatellite instable-high (MSI-H) CRC (presumably 75% of cases) or mesenchymal microsatellite stable (MSS) CRC. Their flat shapes and the relatively short window of this intermediate state make it difficult to detect and diagnose SSLDs; thus, these lesions are potent precursors of post-colonoscopy/interval cancers. Confusing terminology and the lack of longitudinal observation data of serrated polyps have hampered the accumulation of knowledge about SSLDs; however, a growing body of evidence has started to clarify their characteristics and biology. Together with recent efforts to incorporate terminology, histological studies of SSLDs have identified distinct dysplastic patterns and revealed alterations in the tumor microenvironment (TME). Molecular studies at the single-cell level have identified distinct gene alterations in both the epithelium and the TME. Mouse serrated tumor models have demonstrated the importance of TME in disease progression. Advances in colonoscopy provide clues to distinguish pre-malignant from non-malignant-SSLs. Recent progress in all aspects of the field has enhanced our understanding of the biology of SSLDs. The aim of this review article was to assess the current knowledge of SSLDs and highlight their clinical implications.
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Affiliation(s)
- Takahiro Utsumi
- Department of Gastroenterology and Hepatology, Kyoto University Graduate School of Medicine, 54 Kawaharacho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Yosuke Yamada
- Department of Diagnostic Pathology, Kyoto University Hospital, Kyoto, Japan
| | - Maria Teresa Diaz-Meco
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York City, NY, USA
| | - Jorge Moscat
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York City, NY, USA
| | - Yuki Nakanishi
- Department of Gastroenterology and Hepatology, Kyoto University Graduate School of Medicine, 54 Kawaharacho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan.
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Maida M, Marasco G, Facciorusso A, Shahini E, Sinagra E, Pallio S, Ramai D, Murino A. Effectiveness and application of artificial intelligence for endoscopic screening of colorectal cancer: the future is now. Expert Rev Anticancer Ther 2023; 23:719-729. [PMID: 37194308 DOI: 10.1080/14737140.2023.2215436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 05/15/2023] [Indexed: 05/18/2023]
Abstract
INTRODUCTION Artificial intelligence (AI) in gastrointestinal endoscopy includes systems designed to interpret medical images and increase sensitivity during examination. This may be a promising solution to human biases and may provide support during diagnostic endoscopy. AREAS COVERED This review aims to summarize and evaluate data supporting AI technologies in lower endoscopy, addressing their effectiveness, limitations, and future perspectives. EXPERT OPINION Computer-aided detection (CADe) systems have been studied with promising results, allowing for an increase in adenoma detection rate (ADR), adenoma per colonoscopy (APC), and a reduction in adenoma miss rate (AMR). This may lead to an increase in the sensitivity of endoscopic examinations and a reduction in the risk of interval-colorectal cancer. In addition, computer-aided characterization (CADx) has also been implemented, aiming to distinguish adenomatous and non-adenomatous lesions through real-time assessment using advanced endoscopic imaging techniques. Moreover, computer-aided quality (CADq) systems have been developed with the aim of standardizing quality measures in colonoscopy (e.g. withdrawal time and adequacy of bowel cleansing) both to improve the quality of examinations and set a reference standard for randomized controlled trials.
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Affiliation(s)
- Marcello Maida
- Gastroenterology and Endoscopy Unit, S. Elia-Raimondi Hospital, Caltanissetta, Italy
| | - Giovanni Marasco
- IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
- Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
| | - Antonio Facciorusso
- Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy
| | - Endrit Shahini
- Gastroenterology Unit, National Institute of Gastroenterology-IRCCS "Saverio de Bellis", Castellana Grotte, Bari, Italy
| | - Emanuele Sinagra
- Gastroenterology and Endoscopy Unit, Fondazione Istituto San Raffaele Giglio, Cefalu, Italy
| | - Socrate Pallio
- Digestive Diseases Endoscopy Unit, Policlinico G. Martino Hospital, University of Messina, Messina, Italy
| | - Daryl Ramai
- Gastroenterology & Hepatology, University of Utah Health, Salt Lake City, UT, USA
| | - Alberto Murino
- Royal Free Unit for Endoscopy, The Royal Free Hospital and University College London Institute for Liver and Digestive Health, Hampstead, London, UK
- Department of Gastroenterology, Cleveland Clinic London, London, UK
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Galati JS, Lin K, Gross SA. Recent advances in devices and technologies that might prove revolutionary for colonoscopy procedures. Expert Rev Med Devices 2023; 20:1087-1103. [PMID: 37934873 DOI: 10.1080/17434440.2023.2280773] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 11/03/2023] [Indexed: 11/09/2023]
Abstract
INTRODUCTION Colorectal cancer (CRC) is the third most common malignancy and second leading cause of cancer-related mortality in the world. Adenoma detection rate (ADR), a quality indicator for colonoscopy, has gained prominence as it is inversely related to CRC incidence and mortality. As such, recent efforts have focused on developing novel colonoscopy devices and technologies to improve ADR. AREAS COVERED The main objective of this paper is to provide an overview of advancements in the fields of colonoscopy mechanical attachments, artificial intelligence-assisted colonoscopy, and colonoscopy optical enhancements with respect to ADR. We accomplished this by performing a comprehensive search of multiple electronic databases from inception to September 2023. This review is intended to be an introduction to colonoscopy devices and technologies. EXPERT OPINION Numerous mechanical attachments and optical enhancements have been developed that have the potential to improve ADR and AI has gone from being an inaccessible concept to a feasible means for improving ADR. While these advances are exciting and portend a change in what will be considered standard colonoscopy, they continue to require refinement. Future studies should focus on combining modalities to further improve ADR and exploring the use of these technologies in other facets of colonoscopy.
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Affiliation(s)
- Jonathan S Galati
- Department of Internal Medicine, NYU Langone Health, New York, NY, USA
| | - Kevin Lin
- Department of Internal Medicine, NYU Langone Health, New York, NY, USA
| | - Seth A Gross
- Division of Gastroenterology, NYU Langone Health, New York, NY, USA
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Akashi T, Okumura T, Terabayashi K, Yoshino Y, Tanaka H, Yamazaki T, Numata Y, Fukuda T, Manabe T, Baba H, Miwa T, Watanabe T, Hirano K, Igarashi T, Sekine S, Hashimoto I, Shibuya K, Hojo S, Yoshioka I, Matsui K, Yamada A, Sasaki T, Fujii T. The use of an artificial intelligence algorithm for circulating tumor cell detection in patients with esophageal cancer. Oncol Lett 2023; 26:320. [PMID: 37332339 PMCID: PMC10272959 DOI: 10.3892/ol.2023.13906] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 05/25/2023] [Indexed: 06/20/2023] Open
Abstract
Despite recent advances in multidisciplinary treatments of esophageal squamous cell carcinoma (ESCC), patients frequently suffer from distant metastasis after surgery. For numerous types of cancer, circulating tumor cells (CTCs) are considered predictors of distant metastasis, therapeutic response and prognosis. However, as more markers of cytopathological heterogeneity are discovered, the overall detection process for the expression of these markers in CTCs becomes increasingly complex and time consuming. In the present study, the use of a convolutional neural network (CNN)-based artificial intelligence (AI) for CTC detection was assessed using KYSE ESCC cell lines and blood samples from patients with ESCC. The AI algorithm distinguished KYSE cells from peripheral blood-derived mononuclear cells (PBMCs) from healthy volunteers, accompanied with epithelial cell adhesion molecule (EpCAM) and nuclear DAPI staining, with an accuracy of >99.8% when the AI was trained on the same KYSE cell line. In addition, AI trained on KYSE520 distinguished KYSE30 from PBMCs with an accuracy of 99.8%, despite the marked differences in EpCAM expression between the two KYSE cell lines. The average accuracy of distinguishing KYSE cells from PBMCs for the AI and four researchers was 100 and 91.8%, respectively (P=0.011). The average time to complete cell classification for 100 images by the AI and researchers was 0.74 and 630.4 sec, respectively (P=0.012). The average number of EpCAM-positive/DAPI-positive cells detected in blood samples by the AI was 44.5 over 10 patients with ESCC and 2.4 over 5 healthy volunteers (P=0.019). These results indicated that the CNN-based image processing algorithm for CTC detection provides a higher accuracy and shorter analysis time compared to humans, suggesting its applicability for clinical use in patients with ESCC. Moreover, the finding that AI accurately identified even EpCAM-negative KYSEs suggested that the AI algorithm may distinguish CTCs based on as yet unknown features, independent of known marker expression.
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Affiliation(s)
- Takahisa Akashi
- Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan
| | - Tomoyuki Okumura
- Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan
| | - Kenji Terabayashi
- Department of Mechanical and Intellectual Systems Engineering, Faculty of Engineering, University of Toyama, Toyama 930-8555, Japan
| | - Yuki Yoshino
- Department of Mechanical and Intellectual Systems Engineering, Faculty of Engineering, University of Toyama, Toyama 930-8555, Japan
| | - Haruyoshi Tanaka
- Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan
| | - Takeyoshi Yamazaki
- Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan
| | - Yoshihisa Numata
- Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan
| | - Takuma Fukuda
- Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan
| | - Takahiro Manabe
- Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan
| | - Hayato Baba
- Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan
| | - Takeshi Miwa
- Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan
| | - Toru Watanabe
- Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan
| | - Katsuhisa Hirano
- Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan
| | - Takamichi Igarashi
- Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan
| | - Shinichi Sekine
- Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan
| | - Isaya Hashimoto
- Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan
| | - Kazuto Shibuya
- Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan
| | - Shozo Hojo
- Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan
| | - Isaku Yoshioka
- Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan
| | - Koshi Matsui
- Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan
| | - Akane Yamada
- Department of Mechanical and Intellectual Systems Engineering, Faculty of Engineering, University of Toyama, Toyama 930-8555, Japan
| | - Tohru Sasaki
- Department of Mechanical and Intellectual Systems Engineering, Faculty of Engineering, University of Toyama, Toyama 930-8555, Japan
| | - Tsutomu Fujii
- Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan
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Spadaccini M, Schilirò A, Sharma P, Repici A, Hassan C, Voza A. Adenoma detection rate in colonoscopy: how can it be improved? Expert Rev Gastroenterol Hepatol 2023; 17:1089-1099. [PMID: 37869781 DOI: 10.1080/17474124.2023.2273990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 10/18/2023] [Indexed: 10/24/2023]
Abstract
INTRODUCTION The introduction of widespread colonoscopy screening programs has helped in decreasing the incidence of Colorectal Cancer (CRC). However, 'back-to-back' colonoscopies revealed relevant percentage of missed adenomas. Quality indicators were created to further homogenize detection performances and decrease the incidence of post-colonoscopy CRC. Among them, the Adenoma Detection Rate (ADR), defined as the percentage obtained by dividing the number of endoscopic procedures in which at least one adenoma was resected, by the total number of procedures, was found to be inversely associated with the risks of interval colorectal cancer, advanced-stage interval cancer, and fatal interval cancer. AREAS COVERED In this paper, we performed a comprehensive review of the literature focusing on promising new devices and technologies, which are meant to positively affect the endoscopist performance in detecting adenomas, therefore increasing ADR. EXPERT OPINION Considering the current knowledge, although several devices and technologies have been proposed with this intent, the recent implementation of AI ranked over all of the other strategies and it is likely to become the new standard within few years. However, the combination of different device/technologies need to be investigated in the future aiming at even further increasing of endoscopist detection performances.
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Affiliation(s)
- Marco Spadaccini
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Humanitas Clinical and Research Center -IRCCS-, Endoscopy Unit, Rozzano, Italy
| | - Alessandro Schilirò
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
| | | | - Alessandro Repici
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Humanitas Clinical and Research Center -IRCCS-, Endoscopy Unit, Rozzano, Italy
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Humanitas Clinical and Research Center -IRCCS-, Endoscopy Unit, Rozzano, Italy
| | - Antonio Voza
- Humanitas Clinical and Research Center -IRCCS-, Emergency Department, Rozzano, Italy
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Abstract
PURPOSE OF REVIEW Artificial intelligence (AI) is a rapidly growing field in gastrointestinal endoscopy, and its potential applications are virtually endless, with studies demonstrating use of AI for early gastric cancer, inflammatory bowel disease, Barrett's esophagus, capsule endoscopy, as well as other areas in gastroenterology. Much of the early studies and applications of AI in gastroenterology have revolved around colonoscopy, particularly with regards to real-time polyp detection and characterization. This review will cover much of the existing data on computer-aided detection (CADe), computer-aided diagnosis (CADx), and briefly discuss some other interesting applications of AI for colonoscopy, while also considering some of the challenges and limitations that exist around the use of AI for colonoscopy. RECENT FINDINGS Multiple randomized controlled trials have now been published which show a statistically significant improvement when using AI to improve adenoma detection and reduce adenoma miss rates during colonoscopy. There is also a growing pool of literature showing that AI can be helpful for characterizing/diagnosing colorectal polyps in real time. AI has also shown promise in other areas of colonoscopy, including polyp sizing and automated measurement and monitoring of quality metrics during colonoscopy. AI is a promising tool that has the ability to shape the future of gastrointestinal endoscopy, with much of the early data showing significant benefits to use of AI during colonoscopy. However, there remain several challenges that may delay or hamper the widespread use of AI in the field.
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Affiliation(s)
- Nabil M Mansour
- Section of Gastroenterology and Hepatology, Baylor College of Medicine, 7200 Cambridge St., Suite 8B, Houston, TX, 77030, USA.
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Mori Y, East JE, Hassan C, Halvorsen N, Berzin TM, Byrne M, von Renteln D, Hewett DG, Repici A, Ramchandani M, Al Khatry M, Kudo SE, Wang P, Yu H, Saito Y, Misawa M, Parasa S, Matsubayashi CO, Ogata H, Tajiri H, Pausawasdi N, Dekker E, Ahmad OF, Sharma P, Rex DK. Benefits and challenges in implementation of artificial intelligence in colonoscopy: World Endoscopy Organization position statement. Dig Endosc 2023; 35:422-429. [PMID: 36749036 DOI: 10.1111/den.14531] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 02/06/2023] [Indexed: 02/08/2023]
Abstract
The number of artificial intelligence (AI) tools for colonoscopy on the market is increasing with supporting clinical evidence. Nevertheless, their implementation is not going smoothly for a variety of reasons, including lack of data on clinical benefits and cost-effectiveness, lack of trustworthy guidelines, uncertain indications, and cost for implementation. To address this issue and better guide practitioners, the World Endoscopy Organization (WEO) has provided its perspective about the status of AI in colonoscopy as the position statement. WEO Position Statement: Statement 1.1: Computer-aided detection (CADe) for colorectal polyps is likely to improve colonoscopy effectiveness by reducing adenoma miss rates and thus increase adenoma detection; Statement 1.2: In the short term, use of CADe is likely to increase health-care costs by detecting more adenomas; Statement 1.3: In the long term, the increased cost by CADe could be balanced by savings in costs related to cancer treatment (surgery, chemotherapy, palliative care) due to CADe-related cancer prevention; Statement 1.4: Health-care delivery systems and authorities should evaluate the cost-effectiveness of CADe to support its use in clinical practice; Statement 2.1: Computer-aided diagnosis (CADx) for diminutive polyps (≤5 mm), when it has sufficient accuracy, is expected to reduce health-care costs by reducing polypectomies, pathological examinations, or both; Statement 2.2: Health-care delivery systems and authorities should evaluate the cost-effectiveness of CADx to support its use in clinical practice; Statement 3: We recommend that a broad range of high-quality cost-effectiveness research should be undertaken to understand whether AI implementation benefits populations and societies in different health-care systems.
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Affiliation(s)
- Yuichi Mori
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway
- Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - James E East
- Translational Gastroenterology Unit, John Radcliffe Hospital, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Oxford, UK
- Division of Gastroenterology and Hepatology, Mayo Clinic Healthcare, London, UK
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Endoscopy Unit, Humanitas Clinical and Research Center - IRCCS, Rozzano, Italy
| | - Natalie Halvorsen
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway
| | - Tyler M Berzin
- Division of Gastroenterology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, USA
| | - Michael Byrne
- Department of Medicine, The University of British Columbia, Vancouver, Canada
| | - Daniel von Renteln
- Division of Gastroenterology, University of Montreal Medical Center (CHUM) and Research Center (CRCHUM), Montreal, Canada
| | - David G Hewett
- School of Medicine, The University of Queensland, Brisbane, Australia
| | - Alessandro Repici
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Endoscopy Unit, Humanitas Clinical and Research Center - IRCCS, Rozzano, Italy
| | | | - Maryam Al Khatry
- Department of Gastroenterology, Obaidulla Hospital, Ras Al Khaimah, United Arab Emirates
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Pu Wang
- Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, China
| | - Honggang Yu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | - Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | | | - Carolina Ogawa Matsubayashi
- Gastrointestinal Endoscopy Unit, Gastroenterology Department, University of São Paulo Medical School, São Paulo, Brazil
| | - Haruhiko Ogata
- Center for Diagnostic and Therapeutic Endoscopy, School of Medicine, Keio University, Tokyo, Japan
| | - Hisao Tajiri
- Jikei University School of Medicine, Tokyo, Japan
| | - Nonthalee Pausawasdi
- Vikit Viranuvatti Siriraj GI Endoscopy Center, Mahidol University, Bangkok, Thailand
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Evelien Dekker
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | | | - Prateek Sharma
- Division of Gastroenterology and Hepatology, University of Kansas School of Medicine and VA Medical Center, Kansas City, USA
| | - Douglas K Rex
- Division of Gastroenterology, Indiana University School of Medicine, Indianapolis, USA
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Jiang W, Xin L, Zhu S, Liu Z, Wu J, Ji F, Yu C, Shen Z. Risk Factors Related to Polyp Miss Rate of Short-Term Repeated Colonoscopy. Dig Dis Sci 2023; 68:2040-2049. [PMID: 37017819 DOI: 10.1007/s10620-023-07848-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 01/25/2023] [Indexed: 04/06/2023]
Abstract
BACKGROUND Colonoscopy is regarded as the gold standard for colorectal cancer screening and surveillance. However, previous studies have reported large numbers of polyps were missed during routine colonoscopy. AIMS To evaluate polyp miss rate in short-term repeated colonoscopy and explore the related risk factors. METHODS A total of 3695 patients and 12,412 polyps were included in our studies. We calculated the miss rate for polyps of different sizes, pathologies, morphologies and locations, and patients of different characteristics. Univariate and multivariate logistic regression analyses were performed to evaluate risk factors related to miss rate. RESULTS The polyp miss rate was 26.3% and the adenoma miss rate was 22.4% in our study. The advanced adenoma miss rate was 11.0% and the proportion of missed advanced adenomas in missed adenomas sized > 5 mm was up to 22.8%. Polyps sized < 5 mm had a significantly higher miss rate. The miss rate of pedunculated polyps was lower than that of flat or sessile polyps. Polyps in the right colon were prone to be missed than that in the left colon. For older men, current smokers, individuals with multiple polyps detected in the first colonoscopy, the risk of missing polyps was significantly higher. CONCLUSION Nearly a quarter of polyps were missed during routine colonoscopy. Diminutive, flat, sessile, and right-side colon polyps were at higher risk of missing. The risk of missing polyps was higher in older men, current smokers, and individuals with multiple polyps detected in the first colonoscopy than their counterparts.
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Affiliation(s)
- Wenxi Jiang
- Department of Gastroenterology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310003, China
| | - Linying Xin
- Department of Gastroenterology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310003, China
| | - Shefeng Zhu
- Department of Gastroenterology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310003, China
| | - Zhaoxue Liu
- Department of Gastroenterology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310003, China
| | - Jiali Wu
- Department of Gastroenterology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310003, China
| | - Feng Ji
- Department of Gastroenterology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310003, China
| | - Chaohui Yu
- Department of Gastroenterology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310003, China
| | - Zhe Shen
- Department of Gastroenterology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310003, China.
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50
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Mori Y, Wang P, Løberg M, Misawa M, Repici A, Spadaccini M, Correale L, Antonelli G, Yu H, Gong D, Ishiyama M, Kudo SE, Kamba S, Sumiyama K, Saito Y, Nishino H, Liu P, Glissen Brown JR, Mansour NM, Gross SA, Kalager M, Bretthauer M, Rex DK, Sharma P, Berzin TM, Hassan C. Impact of Artificial Intelligence on Colonoscopy Surveillance After Polyp Removal: A Pooled Analysis of Randomized Trials. Clin Gastroenterol Hepatol 2023; 21:949-959.e2. [PMID: 36038128 DOI: 10.1016/j.cgh.2022.08.022] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 08/08/2022] [Accepted: 08/11/2022] [Indexed: 02/07/2023]
Abstract
BACKGROUND AND AIMS Artificial intelligence (AI) tools aimed at improving polyp detection have been shown to increase the adenoma detection rate during colonoscopy. However, it is unknown how increased polyp detection rates by AI affect the burden of patient surveillance after polyp removal. METHODS We conducted a pooled analysis of 9 randomized controlled trials (5 in China, 2 in Italy, 1 in Japan, and 1 in the United States) comparing colonoscopy with or without AI detection aids. The primary outcome was the proportion of patients recommended to undergo intensive surveillance (ie, 3-year interval). We analyzed intervals for AI and non-AI colonoscopies for the U.S. and European recommendations separately. We estimated proportions by calculating relative risks using the Mantel-Haenszel method. RESULTS A total of 5796 patients (51% male, mean 53 years of age) were included; 2894 underwent AI-assisted colonoscopy and 2902 non-AI colonoscopy. When following U.S. guidelines, the proportion of patients recommended intensive surveillance increased from 8.4% (95% CI, 7.4%-9.5%) in the non-AI group to 11.3% (95% CI, 10.2%-12.6%) in the AI group (absolute difference, 2.9% [95% CI, 1.4%-4.4%]; risk ratio, 1.35 [95% CI, 1.16-1.57]). When following European guidelines, it increased from 6.1% (95% CI, 5.3%-7.0%) to 7.4% (95% CI, 6.5%-8.4%) (absolute difference, 1.3% [95% CI, 0.01%-2.6%]; risk ratio, 1.22 [95% CI, 1.01-1.47]). CONCLUSIONS The use of AI during colonoscopy increased the proportion of patients requiring intensive colonoscopy surveillance by approximately 35% in the United States and 20% in Europe (absolute increases of 2.9% and 1.3%, respectively). While this may contribute to improved cancer prevention, it significantly adds patient burden and healthcare costs.
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Affiliation(s)
- Yuichi Mori
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway; Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway; Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan.
| | - Pu Wang
- Department of Gastroenterology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Sichuan, China
| | - Magnus Løberg
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway; Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway
| | - Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Alessandro Repici
- Endoscopy Unit, Humanitas Clinical and Research Center-IRCCS, Rozzano, Italy; Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
| | - Marco Spadaccini
- Endoscopy Unit, Humanitas Clinical and Research Center-IRCCS, Rozzano, Italy
| | - Loredana Correale
- Endoscopy Unit, Humanitas Clinical and Research Center-IRCCS, Rozzano, Italy
| | - Giulio Antonelli
- Gastroenterology and Digestive Endoscopy Unit, Ospedale dei Castelli Hospital, Ariccia, Rome, Italy; Department of Anatomical, Histological, Forensic Medicine and Orthopedics Sciences, Sapienza University of Rome, Italy
| | - Honggang Yu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Wuhan University Renmin Hospital, Wuhan, China
| | - Dexin Gong
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Wuhan University Renmin Hospital, Wuhan, China
| | - Misaki Ishiyama
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Shunsuke Kamba
- Department of Endoscopy, the Jikei University School of Medicine, Tokyo, Japan
| | - Kazuki Sumiyama
- Department of Endoscopy, the Jikei University School of Medicine, Tokyo, Japan
| | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | - Haruo Nishino
- Coloproctology Center, Matsushima Hospital, Yokohama, Japan
| | - Peixi Liu
- Department of Gastroenterology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Sichuan, China
| | | | - Nabil M Mansour
- Section of Gastroenterology and Hepatology, Baylor College of Medicine, Houston, Texas
| | - Seth A Gross
- Division of Gastroenterology and Hepatology, NYU Langone Health, New York, New York
| | - Mette Kalager
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway; Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway
| | - Michael Bretthauer
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway; Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway
| | - Douglas K Rex
- Division of Gastroenterology/Hepatology, Indiana University School of Medicine, Indianapolis, Indiana
| | - Prateek Sharma
- Department of Gastroenterology and Hepatology, Kansas City VA Medical Center and University of Kansas School of Medicine, Kansas City, Kansas
| | - Tyler M Berzin
- Center for Advanced Endoscopy, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts
| | - Cesare Hassan
- Endoscopy Unit, Humanitas Clinical and Research Center-IRCCS, Rozzano, Italy; Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
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