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Babu B, Singh J, Salazar González JF, Zalmai S, Ahmed A, Padekar HD, Eichemberger MR, Abdallah AI, Ahamed S I, Nazir Z. A Narrative Review on the Role of Artificial Intelligence (AI) in Colorectal Cancer Management. Cureus 2025; 17:e79570. [PMID: 40144438 PMCID: PMC11940584 DOI: 10.7759/cureus.79570] [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: 02/24/2025] [Indexed: 03/28/2025] Open
Abstract
The role of artificial intelligence (AI) tools and deep learning in medical practice in the management of colorectal cancer has gathered significant attention in recent years. Colorectal cancer, being the third most common type of malignancy, requires an innovative approach to augment early detection and advanced surgical techniques to reduce morbidity and mortality. With its emerging potential, AI improves colorectal cancer management by assisting with accuracy in screening, pathology evaluation, precision, and postoperative care. Evidence suggests that AI minimizes missed cases during colorectal cancer screening, plays a promising role in pathology and imaging diagnoses, and facilitates accurate staging. In surgical management, AI demonstrates comparable or superior outcomes to laparoscopic approaches, with reduced hospital stays and conversion rates. However, these outcomes are influenced by clinical expertise and other dependable factors, including expertise in implementing AI-based software and detecting possible errors. Despite these advancements, limited multicenter studies and randomized trials restrict the comprehensive evaluation of AI's true potential and integration into standard practice. We used Pubmed, Google Scholar, Cochrane Library, and Scopus databases for this review. The final number of articles selected, depending on inclusion and exclusion criteria, is 122. We included papers published in the English language, literature published in the last 10 years, and adult patient populations above 35 years with colorectal cancer. We thoroughly included randomized controlled trials, cohort studies, meta-analyses, systematic reviews, narrative reviews, and case-control studies. The use of AI paves the way for the adoption of more personalized medicine. This review highlights the advantages of AI at various disease stages for colorectal cancer patients and evaluates its potential for cost-effective implementation in clinical practice.
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Affiliation(s)
- Bijily Babu
- Clinical Research, Network Cancer Aid and Research Foundation, Cochin, IND
| | - Jyoti Singh
- Department of Medicine, American University of Barbados, Bridgetown, BRB
| | | | - Sadaf Zalmai
- Emergency Medicine, New York Presbyterian Hospital, New York, USA
| | - Adnan Ahmed
- Medicine and Surgery, York University, Bradford, CAN
| | - Harshal D Padekar
- General Surgery, Grant Medical College and Sir Jamshedjee Jeejeebhoy Group of Hospitals, Mumbai, IND
| | | | - Abrar I Abdallah
- Medicine and Surgery, Sulaiman Al Rajhi University, Al Bukayriyah, SAU
| | - Irshad Ahamed S
- General Surgery, Pondicherry Institute of Medical Sciences, Pondicherry, IND
| | - Zahra Nazir
- Internal Medicine, Combined Military Hospital, Quetta, PAK
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Rønborg SN, Ujjal S, Kroijer R, Ploug M. Assessing the potential of artificial intelligence to enhance colonoscopy adenoma detection in clinical practice: a prospective observational trial. Clin Endosc 2024; 57:783-789. [PMID: 39188117 PMCID: PMC11637665 DOI: 10.5946/ce.2024.038] [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: 02/19/2024] [Revised: 05/21/2024] [Accepted: 05/23/2024] [Indexed: 08/28/2024] Open
Abstract
BACKGROUND/AIMS This study aimed to evaluate the effectiveness of the GI Genius (Medtronic) module in clinical practice, focusing on the adenoma detection rate (ADR) during colonoscopy. Computer-aided polyp detection (CADe) systems using artificial intelligence have been shown to improve adenoma detection in controlled trials. However, the effectiveness of these systems in clinical practice has recently been questioned. METHODS This single-center prospective observational study was conducted at the University Hospital of Southern Denmark and included all individuals referred for colonoscopy between November 2020 and January 2021. The primary outcome was ADR, comparing patients examined with CADe to those examined without it. The selection of patients to be examined with the CADe module was completely random. RESULTS A total of 502 patients were analyzed (318 in the control group and 184 in the CADe group). The overall ADR was 32.1% with a slight increase in the CADe group (34.7% vs. 30.5%). Multivariable analysis showed a very modest and statistically insignificant increase in ADR (risk ratio, 1.12; 95% confidence interval, 0.88-1.43). CONCLUSIONS The use of CADe in clinical practice did not increase ADR with statistical significance when compared to colonoscopy without CADe. These findings suggest that the impact of CADe systems in everyday clinical practice are modest.
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Affiliation(s)
- Søren Nicolaj Rønborg
- Department of Surgical Gastroenterology, Esbjerg Hospital, University Hospital of Southern Denmark, Esbjerg, Denmark
| | - Suresh Ujjal
- Department of Surgical Gastroenterology, Esbjerg Hospital, University Hospital of Southern Denmark, Esbjerg, Denmark
| | - Rasmus Kroijer
- Department of Surgical Gastroenterology, Esbjerg Hospital, University Hospital of Southern Denmark, Esbjerg, Denmark
| | - Magnus Ploug
- Department of Surgical Gastroenterology, Esbjerg Hospital, University Hospital of Southern Denmark, Esbjerg, Denmark
- Department of Regional Health Research, University of Southern Denmark, Esbjerg, Denmark
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Peng C, Tian CX, Mu Y, Ma M, Zhang Z, Wan M, Liu J, Li Z, Zuo X, Li W, Li Y. Hyperspectral imaging facilitating resect-and-discard strategy through artificial intelligence-assisted diagnosis of colorectal polyps: A pilot study. Cancer Med 2024; 13:e70195. [PMID: 39320133 PMCID: PMC11423483 DOI: 10.1002/cam4.70195] [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: 12/06/2023] [Revised: 08/15/2024] [Accepted: 08/25/2024] [Indexed: 09/26/2024] Open
Abstract
BACKGROUND AND AIMS The resect-and-discard strategy for colorectal polyps based on accurate optical diagnosis remains challenges. Our aim was to investigate the feasibility of hyperspectral imaging (HSI) for identifying colorectal polyp properties and diagnosis of colorectal cancer in fresh tissues during colonoscopy. METHODS 144,900 two dimensional images generated from 161 hyperspectral images of colorectal polyp tissues were prospectively obtained from patients undergoing colonoscopy. A residual neural network model was trained with transfer learning to automatically differentiate colorectal polyps, validated by histopathologic diagnosis. The diagnostic performances of the HSI-AI model and endoscopists were calculated respectively, and the auxiliary efficiency of the model was evaluated after a 2-week interval. RESULTS Quantitative HSI revealed histological differences in colorectal polyps. The HSI-AI model showed considerable efficacy in differentiating nonneoplastic polyps, non-advanced adenomas, and advanced neoplasia in vitro, with sensitivities of 96.0%, 94.0%, and 99.0% and specificities of 99.0%, 99.0%, and 96.5%, respectively. With the assistance of the model, the median negative predictive value of neoplastic polyps increased from 50.0% to 88.2% (p = 0.013) in novices. CONCLUSION This study demonstrated the feasibility of using HSI as a diagnostic tool to differentiate neoplastic colorectal polyps in vitro and the potential of AI-assisted diagnosis synchronized with colonoscopy. The tool may improve the diagnostic performance of novices and facilitate the application of resect-and-discard strategy to decrease the cost.
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Affiliation(s)
- Cheng Peng
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of MedicineShandong UniversityJinanChina
| | - Chong Xuan Tian
- Department of Biomedical Engineering Institute, School of Control Science and EngineeringShandong UniversityJinanChina
| | - Yijun Mu
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of MedicineShandong UniversityJinanChina
| | - Mingjun Ma
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of MedicineShandong UniversityJinanChina
| | - Zhenlei Zhang
- Department of Biomedical Engineering Institute, School of Control Science and EngineeringShandong UniversityJinanChina
| | - Meng Wan
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of MedicineShandong UniversityJinanChina
| | - Jing Liu
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of MedicineShandong UniversityJinanChina
| | - Zhen Li
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of MedicineShandong UniversityJinanChina
| | - Xiuli Zuo
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of MedicineShandong UniversityJinanChina
- Laboratory of Translational Gastroenterology, Qilu Hospital, Cheeloo College of MedicineShandong UniversityJinanChina
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of GI Tumor, Qilu Hospital, Cheeloo College of MedicineShandong UniversityJinanChina
- Shandong Provincial Clinical Research Center for Digestive DiseaseJinanChina
| | - Wei Li
- Department of Biomedical Engineering Institute, School of Control Science and EngineeringShandong UniversityJinanChina
| | - Yanqing Li
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of MedicineShandong UniversityJinanChina
- Laboratory of Translational Gastroenterology, Qilu Hospital, Cheeloo College of MedicineShandong UniversityJinanChina
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of GI Tumor, Qilu Hospital, Cheeloo College of MedicineShandong UniversityJinanChina
- Shandong Provincial Clinical Research Center for Digestive DiseaseJinanChina
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Bangolo A, Wadhwani N, Nagesh VK, Dey S, Tran HHV, Aguilar IK, Auda A, Sidiqui A, Menon A, Daoud D, Liu J, Pulipaka SP, George B, Furman F, Khan N, Plumptre A, Sekhon I, Lo A, Weissman S. Impact of artificial intelligence in the management of esophageal, gastric and colorectal malignancies. Artif Intell Gastrointest Endosc 2024; 5:90704. [DOI: 10.37126/aige.v5.i2.90704] [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: 12/12/2023] [Revised: 01/28/2024] [Accepted: 03/04/2024] [Indexed: 05/11/2024] Open
Abstract
The incidence of gastrointestinal malignancies has increased over the past decade at an alarming rate. Colorectal and gastric cancers are the third and fifth most commonly diagnosed cancers worldwide but are cited as the second and third leading causes of mortality. Early institution of appropriate therapy from timely diagnosis can optimize patient outcomes. Artificial intelligence (AI)-assisted diagnostic, prognostic, and therapeutic tools can assist in expeditious diagnosis, treatment planning/response prediction, and post-surgical prognostication. AI can intercept neoplastic lesions in their primordial stages, accurately flag suspicious and/or inconspicuous lesions with greater accuracy on radiologic, histopathological, and/or endoscopic analyses, and eliminate over-dependence on clinicians. AI-based models have shown to be on par, and sometimes even outperformed experienced gastroenterologists and radiologists. Convolutional neural networks (state-of-the-art deep learning models) are powerful computational models, invaluable to the field of precision oncology. These models not only reliably classify images, but also accurately predict response to chemotherapy, tumor recurrence, metastasis, and survival rates post-treatment. In this systematic review, we analyze the available evidence about the diagnostic, prognostic, and therapeutic utility of artificial intelligence in gastrointestinal oncology.
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Affiliation(s)
- Ayrton Bangolo
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Nikita Wadhwani
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Vignesh K Nagesh
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Shraboni Dey
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Hadrian Hoang-Vu Tran
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Izage Kianifar Aguilar
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Auda Auda
- Department of Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Aman Sidiqui
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Aiswarya Menon
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Deborah Daoud
- Department of Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - James Liu
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Sai Priyanka Pulipaka
- Department of Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Blessy George
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Flor Furman
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Nareeman Khan
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Adewale Plumptre
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Imranjot Sekhon
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Abraham Lo
- Department of Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Simcha Weissman
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
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Tham S, Koh FH, Ladlad J, Chue KM, SKH Endoscopy Centre, Lin CL, Teo EK, Foo FJ. The imitation game: a review of the use of artificial intelligence in colonoscopy, and endoscopists' perceptions thereof. Ann Coloproctol 2023; 39:385-394. [PMID: 36907170 PMCID: PMC10626328 DOI: 10.3393/ac.2022.00878.0125] [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: 10/30/2022] [Revised: 12/22/2022] [Accepted: 01/09/2023] [Indexed: 03/14/2023] Open
Abstract
The development of deep learning systems in artificial intelligence (AI) has enabled advances in endoscopy, and AI-aided colonoscopy has recently been ushered into clinical practice as a clinical decision-support tool. This has enabled real-time AI-aided detection of polyps with a higher sensitivity than the average endoscopist, and evidence to support its use has been promising thus far. This review article provides a summary of currently published data relating to AI-aided colonoscopy, discusses current clinical applications, and introduces ongoing research directions. We also explore endoscopists' perceptions and attitudes toward the use of this technology, and discuss factors influencing its uptake in clinical practice.
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Affiliation(s)
- Sarah Tham
- Department of General Surgery, Sengkang General Hospital, SingHealth Services, Singapore
| | - Frederick H. Koh
- Colorectal Service, Department of General Surgery, Sengkang General Hospital, SingHealth Services, Singapore
- SKH Endoscopy Centre, Division of Hyperacute Care, Sengkang General Hospital, SingHealth Services, Singapore
| | - Jasmine Ladlad
- Colorectal Service, Department of General Surgery, Sengkang General Hospital, SingHealth Services, Singapore
- SKH Endoscopy Centre, Division of Hyperacute Care, Sengkang General Hospital, SingHealth Services, Singapore
| | - Koy-Min Chue
- Department of General Surgery, Sengkang General Hospital, SingHealth Services, Singapore
- SKH Endoscopy Centre, Division of Hyperacute Care, Sengkang General Hospital, SingHealth Services, Singapore
| | - SKH Endoscopy Centre
- SKH Endoscopy Centre, Division of Hyperacute Care, Sengkang General Hospital, SingHealth Services, Singapore
| | - Cui-Li Lin
- SKH Endoscopy Centre, Division of Hyperacute Care, Sengkang General Hospital, SingHealth Services, Singapore
- Department of Gastroenterology and Hepatology, Sengkang General Hospital, SingHealth Services, Singapore
| | - Eng-Kiong Teo
- SKH Endoscopy Centre, Division of Hyperacute Care, Sengkang General Hospital, SingHealth Services, Singapore
- Department of Gastroenterology and Hepatology, Sengkang General Hospital, SingHealth Services, Singapore
| | - Fung-Joon Foo
- Colorectal Service, Department of General Surgery, Sengkang General Hospital, SingHealth Services, Singapore
- SKH Endoscopy Centre, Division of Hyperacute Care, Sengkang General Hospital, SingHealth Services, Singapore
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Tham S, Koh FH, Teo EK, Lin CL, Foo FJ. Knowledge, perceptions and behaviours of endoscopists towards the use of artificial intelligence-aided colonoscopy. Surg Endosc 2023; 37:7395-7400. [PMID: 37670191 DOI: 10.1007/s00464-023-10412-3] [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: 08/10/2022] [Accepted: 08/14/2023] [Indexed: 09/07/2023]
Abstract
BACKGROUND Recent developments in artificial intelligence (AI) systems have enabled advancements in endoscopy. Deep learning systems, using convolutional neural networks, have allowed for real-time AI-aided detection of polyps with higher sensitivity than the average endoscopist. However, not all endoscopists welcome the advent of AI systems. METHODS We conducted a survey on the knowledge of AI, perceptions of AI in medicine, and behaviours regarding use of AI-aided colonoscopy, in a single centre 2 months after the implementation of Medtronic's GI Genius in colonoscopy. We obtained a response rate of 66.7% (16/24) amongst consultant-grade endoscopists. Fisher's exact test was used to calculate the significance of correlations. RESULTS Knowledge of AI varied widely amongst endoscopists. Most endoscopists were optimistic about AI's capabilities in performing objective administrative and clinical tasks, but reserved about AI providing personalised, empathetic care. 68.8% (n = 11) of endoscopists agreed or strongly agreed that GI Genius should be used as an adjunct in colonoscopy. In analysing the 31.3% (n = 5) of endoscopists who disagreed or were ambivalent about its use, there was no significant correlation with their knowledge or perceptions of AI, but a significant number did not enjoy using the programme (p-value = 0.0128) and did not think it improved the quality of colonoscopy (p-value = 0.033). CONCLUSIONS Acceptance of AI-aided colonoscopy systems is more related to the endoscopist's experience with using the programme, rather than general knowledge or perceptions towards AI. Uptake of such systems will rely greatly on how the device is delivered to the end user.
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Affiliation(s)
- Sarah Tham
- Department of General Surgery, Sengkang General Hospital, SingHealth Services, Singapore, Singapore
| | - Frederick H Koh
- Colorectal Service, Department of General Surgery, Sengkang General Hospital, SingHealth Services, 110 Sengkang East Way, Singapore, 544886, Singapore.
| | - Eng-Kiong Teo
- Department of Gastroenterology and Hepatology, Sengkang General Hospital, SingHealth Services, Singapore, Singapore
| | - Cui-Li Lin
- Department of Gastroenterology and Hepatology, Sengkang General Hospital, SingHealth Services, Singapore, Singapore
| | - Fung-Joon Foo
- Colorectal Service, Department of General Surgery, Sengkang General Hospital, SingHealth Services, 110 Sengkang East Way, Singapore, 544886, Singapore
- Endoscopy Centre, Division of Hyperacute Care, Sengkang General Hospital, Singapore, Singapore
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Sui Y, Zheng Y, Wang Q, Lv J, Wang H, Wen Q, Wang Z, Wang G, Jia H, Cao F, Wang N, Hao J, Zhang Y, Wu X, Chen H, Lu J, Chen X. Comparison of missed adenomas in deep-sedated and unsedated colonoscopy: A multicenter retrospective study. Eur J Intern Med 2023; 110:48-53. [PMID: 36710136 DOI: 10.1016/j.ejim.2023.01.019] [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: 12/03/2022] [Revised: 01/17/2023] [Accepted: 01/23/2023] [Indexed: 01/29/2023]
Abstract
BACKGROUND Deep-sedated colonoscopy with propofol is widely used in China. However, its impact on quality metrics remains controversial. We aimed to investigate the effects of deep-sedated colonoscopy on missed adenomas, specifically in each colorectal segment. METHODS Data of 3710 individuals from seven hospitals in China who underwent an initial colonoscopy with or without propofol sedation and a second colonoscopy without sedation within six months for surveillance or polypectomy by endoscopist of the same level between October 2020 and September 2021 were retrospectively analyzed. RESULTS A total of 1113 missed adenomas in 3710 patients were evaluated. The adenoma miss rate (AMR) was significantly higher in deep-sedated colonoscopy than in unsedated colonoscop [19.14% (578/3020) vs. 16.15% (535/3313), P < 0.05]. The risk of missing adenomas in deep-sedated colonoscopy was 1.229 times higher than in unsedated colonoscopy (OR, 1.229; 95% CI: 1.080-1.399). AMRs of the splenic flexure (26.02% [96/369] vs. 16.04% [47/293], P < 0.05) and descending colon (20.86% [102/489] vs. 13.37% [54/404], P < 0.05) were significantly higher in deep-sedated colonoscopy than in unsedated colonoscopy when performed by middle-level endoscopists rather than high-level endoscopists (P < 0.05). CONCLUSIONS AMR was higher in deep-sedated colonoscopy than in unsedated colonoscopy. Furthermore, adenomas in the splenic flexure and descending colon were more frequently missed in deep-sedated colonoscopy than in unsedated colonoscopy, particularly when performed by less experienced endoscopists.
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Affiliation(s)
- Yue Sui
- Shanxi Medical University, 030000, China
| | | | - Qing Wang
- Shanxi Medical University, 030000, China.
| | - Jieping Lv
- The First Hospital of Shanxi Medical University, 03000, China.
| | - Hongjin Wang
- The Second People's Hospital of Datong, 037000, China
| | - Qing Wen
- The Second People's Hospital of Datong, 037000, China.
| | - Zhenzhen Wang
- The Second People's Hospital of Datong, 037000, China
| | - Guanfeng Wang
- The Second People's Hospital of Datong, 037000, China.
| | - Hui Jia
- Ordos Mongolian Medical Hospital, 017000, China.
| | - Fengzhen Cao
- Kangning Physical Examination Center, 017000, China
| | - Naping Wang
- The First Hospital of Shanxi Medical University, Yanhu District Branch, 044000, China
| | - Junlian Hao
- Xiaoyi Traditional Chinese Medicine Hospital, 033000, China.
| | - Yiping Zhang
- Datong Shoujia Digestive Disease Hospital, 037000, China.
| | - Xiaopeng Wu
- Lvliang Traditional Chinese Medicine Hospital, 033000, China.
| | - Haihua Chen
- The First Hospital of Shanxi Medical University, 03000, China.
| | - Junhui Lu
- Shanxi Medical University, 030000, China
| | - Xing Chen
- The First Hospital of Shanxi Medical University, 03000, China.
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Nazarian S, Koo H, Carrington E, Darzi A, Patel N. The future of endoscopy – what are the thoughts on artificial intelligence? J EXP THEOR ARTIF IN 2023. [DOI: 10.1080/0952813x.2023.2178516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
Affiliation(s)
- S. Nazarian
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - H.F Koo
- Department of Surgery, Royal Free London NHS Foundation Trust, London, UK
| | - E. Carrington
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - A. Darzi
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - N. Patel
- Department of Surgery and Cancer, Imperial College London, London, UK
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Eysenbach G, Liu SHK, Leung K, Wu JT, Zauber AG, Leung WK. The Impacts of Computer-Aided Detection of Colorectal Polyps on Subsequent Colonoscopy Surveillance Intervals: Simulation Study. J Med Internet Res 2023; 25:e42665. [PMID: 36763451 PMCID: PMC9960036 DOI: 10.2196/42665] [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: 09/13/2022] [Revised: 01/10/2023] [Accepted: 01/23/2023] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Computer-aided detection (CADe) of colorectal polyps has been shown to increase adenoma detection rates, which would potentially shorten subsequent surveillance intervals. OBJECTIVE The purpose of this study is to simulate the potential changes in subsequent colonoscopy surveillance intervals after the application of CADe in a large cohort of patients. METHODS We simulated the projected increase in polyp and adenoma detection by universal CADe application in our patients who had undergone colonoscopy with complete endoscopic and histological findings between 2016 and 2020. The simulation was based on bootstrapping the published performance of CADe. The corresponding changes in surveillance intervals for each patient, as recommended by the US Multi-Society Task Force on Colorectal Cancer (USMSTF) or the European Society of Gastrointestinal Endoscopy (ESGE), were determined after the CADe was determined. RESULTS A total of 3735 patients who had undergone colonoscopy were included. Based on the simulated CADe effect, the application of CADe would result in 19.1% (n=714) and 1.9% (n=71) of patients having shorter surveillance intervals, according to the USMSTF and ESGE guidelines, respectively. In particular, all (or 2.7% (n=101) of the total) patients who were originally scheduled to have 3-5 years of surveillance would have their surveillance intervals shortened to 3 years, following the USMSTF guidelines. The changes in this group of patients were largely attributed to an increase in the number of adenomas (n=75, 74%) rather than serrated lesions being detected. CONCLUSIONS Widespread adoption of CADe would inevitably increase the demand for surveillance colonoscopies with the shortening of original surveillance intervals, particularly following the current USMSTF guideline.
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Affiliation(s)
| | - Sze Hang Kevin Liu
- Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
| | - Kathy Leung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
| | - Joseph T Wu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
| | - Ann G Zauber
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Wai Keung Leung
- Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
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The effectiveness of real-time computer-aided and quality control systems in colorectal adenoma and polyp detection during colonoscopies: a meta-analysis. Ann Med Surg (Lond) 2023; 85:80-91. [PMID: 36845807 PMCID: PMC9949794 DOI: 10.1097/ms9.0000000000000079] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 12/08/2022] [Indexed: 02/28/2023] Open
Abstract
This meta-analysis aims to quantify the effectiveness of artificial intelligence (AI)-supported colonoscopy compared to standard colonoscopy in adenoma detection rate (ADR) differences with the use of computer-aided detection and quality control systems. Moreover, the polyp detection rate (PDR) intergroup differences and withdrawal times will be analyzed. Methods This study was conducted adhering to PRISMA guidelines. Studies were searched across PubMed, CINAHL, EMBASE, Scopus, Cochrane, and Web of Science. Keywords including the following 'Artificial Intelligence, Polyp, Adenoma, Detection, Rate, Colonoscopy, Colorectal, Colon, Rectal' were used. Odds ratio (OR) applying 95% CI for PDR and ADR were computed. SMD with 95% CI for withdrawal times were computed using RevMan 5.4.1 (Cochrane). The risk of bias was assessed using the RoB 2 tool. Results Of 2562 studies identified, 11 trials were included comprising 6856 participants. Of these, 57.4% participants were in the AI group and 42.6% individuals were in in the standard group. ADR was higher in the AI group compared to the standard of care group (OR=1.51, P=0.003). PDR favored the intervened group compared to the standard group (OR=1.89, P<0.0001). A medium measure of effect was found for withdrawal times (SMD=0.25, P<0.0001), therefore with limited practical applications. Conclusion AI-supported colonoscopies improve PDR and ADR; however, no noticeable worsening of withdrawal times is noted. Colorectal cancers are highly preventable if diagnosed early-on. With AI-assisted tools in clinical practice, there is a strong potential to reduce the incidence rates of cancers in the near future.
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Agrawal LS, Acharya S, Shukla S, Parekh YC. Future of Endoscopy in Inflammatory Bowel Diseases (IBDs). Cureus 2022; 14:e29567. [PMID: 36312686 PMCID: PMC9596090 DOI: 10.7759/cureus.29567] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 09/25/2022] [Indexed: 12/15/2022] Open
Abstract
Gastrointestinal (GI) endoscopy has transformed over the years in scope, safety, accuracy, acceptability, and cost effectiveness of the clinical practice. There has been a reduction in the superiority of the endoscopic devices as innovations have taken place and increased the diagnostic values with certain limitations. There are particular difficulties in striking a balance between the development of new technology and the device's acceptance. The wide use of endoscopy for investigating GI lesions and diagnosis has led to an increase in more advanced methods and their broad application. It can simultaneously diagnose pre-malignant and malignant lesions, and newer interventions have made the biopsy specimen uptake possible. In this review article, we focus on the more recent roles, indications, applications, and usage of the innovative methods of endoscopy.
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Xiao Y, Song Z, Zou S, You Y, Cui J, Wang S, Ku C, Wu X, Xue X, Han W, Zhou W. Artificial Intelligence Assisted Topographic Mapping System for Endoscopic Submucosal Dissection Specimens. Front Med (Lausanne) 2022; 9:822731. [PMID: 35755069 PMCID: PMC9219602 DOI: 10.3389/fmed.2022.822731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Accepted: 05/16/2022] [Indexed: 11/13/2022] Open
Abstract
Background Endoscopic submucosal dissection (ESD), a minimally invasive surgery used to treat early gastrointestinal malignancies, has been widely embraced around the world. The gross reconstruction of ESD specimens can facilitate a more precise pathological diagnosis and allow endoscopists to explore lesions thoroughly. The traditional method of mapping is time-consuming and inaccurate. We aim to design a topographic mapping system via artificial intelligence to perform the job automatically. Methods The topographic mapping system was built using computer vision techniques. We enrolled 23 ESD cases at the Peking Union Medical College Hospital from September to November 2019. The reconstruction maps were created for each case using both the traditional approach and the system. Results Using the system, the time saved per case ranges from 34 to 3,336 s. Two approaches revealed no significant variations in the shape, size, or tumor area. Conclusion We developed an AI-assisted system that would help pathologists complete the ESD topographic mapping process rapidly and accurately.
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Affiliation(s)
- Yu Xiao
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhigang Song
- Department of Pathology, The Chinese PLA General Hospital, Beijing, China
| | - Shuangmei Zou
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yan You
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jie Cui
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shuhao Wang
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China.,Thorough Images, Beijing, China
| | | | - Xi Wu
- Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaowei Xue
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wenqi Han
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Weixun Zhou
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Artificial Endoscopy and Inflammatory Bowel Disease: Welcome to the Future. J Clin Med 2022; 11:jcm11030569. [PMID: 35160021 PMCID: PMC8836846 DOI: 10.3390/jcm11030569] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 01/17/2022] [Accepted: 01/18/2022] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence (AI) is assuming an increasingly important and central role in several medical fields. Its application in endoscopy provides a powerful tool supporting human experiences in the detection, characterization, and classification of gastrointestinal lesions. Lately, the potential of AI technology has been emerging in the field of inflammatory bowel disease (IBD), where the current cornerstone is the treat-to-target strategy. A sensible and specific tool able to overcome human limitations, such as AI, could represent a great ally and guide precision medicine decisions. Here we reviewed the available literature on the endoscopic applications of AI in order to properly describe the current state-of-the-art and identify the research gaps in IBD at the dawn of 2022.
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Liang F, Wang S, Zhang K, Liu TJ, Li JN. Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer. World J Gastrointest Oncol 2022; 14:124-152. [PMID: 35116107 PMCID: PMC8790413 DOI: 10.4251/wjgo.v14.i1.124] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 08/19/2021] [Accepted: 11/15/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) technology has made leaps and bounds since its invention. AI technology can be subdivided into many technologies such as machine learning and deep learning. The application scope and prospect of different technologies are also totally different. Currently, AI technologies play a pivotal role in the highly complex and wide-ranging medical field, such as medical image recognition, biotechnology, auxiliary diagnosis, drug research and development, and nutrition. Colorectal cancer (CRC) is a common gastrointestinal cancer that has a high mortality, posing a serious threat to human health. Many CRCs are caused by the malignant transformation of colorectal polyps. Therefore, early diagnosis and treatment are crucial to CRC prognosis. The methods of diagnosing CRC are divided into imaging diagnosis, endoscopy, and pathology diagnosis. Treatment methods are divided into endoscopic treatment, surgical treatment, and drug treatment. AI technology is in the weak era and does not have communication capabilities. Therefore, the current AI technology is mainly used for image recognition and auxiliary analysis without in-depth communication with patients. This article reviews the application of AI in the diagnosis, treatment, and prognosis of CRC and provides the prospects for the broader application of AI in CRC.
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Affiliation(s)
- Feng Liang
- Department of General Surgery, The Second Hospital of Jilin University, Changchun 130041, Jilin Province, China
| | - Shu Wang
- Department of Radiotherapy, Jilin University Second Hospital, Changchun 130041, Jilin Province, China
| | - Kai Zhang
- Department of General Surgery, The Second Hospital of Jilin University, Changchun 130041, Jilin Province, China
| | - Tong-Jun Liu
- Department of General Surgery, The Second Hospital of Jilin University, Changchun 130041, Jilin Province, China
| | - Jian-Nan Li
- Department of General Surgery, The Second Hospital of Jilin University, Changchun 130041, Jilin Province, China
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