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Djinbachian R, Rex DK, Von Renteln D. Reply. Gastroenterology 2024; 167:1499-1500. [PMID: 39209123 DOI: 10.1053/j.gastro.2024.08.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2024] [Accepted: 08/19/2024] [Indexed: 09/04/2024]
Affiliation(s)
- Roupen Djinbachian
- Division of Gastroenterology, Montreal University Hospital Center (CHUM), Montreal University Hospital Research Center (CRCHUM), Montreal, Quebec, Canada
| | - Douglas K Rex
- Division of Gastroenterology, Indiana University School of Medicine, Indianapolis, Indiana
| | - Daniel Von Renteln
- Division of Gastroenterology, Montreal University Hospital Center (CHUM), Montreal University Hospital Research Center (CRCHUM), Montreal, Quebec, Canada
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Djinbachian R, Rex DK, Von Renteln D. Reply. Gastroenterology 2024; 167:1499-1500. [PMID: 39209123 DOI: 10.1053/j.gastro.2024.08.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2024] [Accepted: 08/19/2024] [Indexed: 09/04/2024]
Affiliation(s)
- Roupen Djinbachian
- Division of Gastroenterology, Montreal University Hospital Center (CHUM), Montreal University Hospital Research Center (CRCHUM), Montreal, Quebec, Canada
| | - Douglas K Rex
- Division of Gastroenterology, Indiana University School of Medicine, Indianapolis, Indiana
| | - Daniel Von Renteln
- Division of Gastroenterology, Montreal University Hospital Center (CHUM), Montreal University Hospital Research Center (CRCHUM), Montreal, Quebec, Canada
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Djinbachian R, El Yamani MEM, Rex DK, Pohl H, Taghiakbari M, von Renteln D. Using Computer-aided Optical Diagnosis and Expert Review to Evaluate Colorectal Polyps Diagnosed as Normal Mucosa in Pathology. Clin Gastroenterol Hepatol 2024; 22:2344-2346.e1. [PMID: 38705436 DOI: 10.1016/j.cgh.2024.03.041] [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: 01/03/2024] [Revised: 03/20/2024] [Accepted: 03/26/2024] [Indexed: 05/07/2024]
Abstract
Pathological assessment of colorectal polyps is considered the current reference standard for histologic diagnosis. About 10% of polyps sent to the pathology lab are returned with the diagnosis of mucosal folds, mucosal prolapse, or normal mucosa.1,2 Two recent publications have indicated that disagreements between endoscopic optical diagnosis and the subsequent pathological diagnoses might be due to misdiagnosis in pathology.3,4 We were therefore interested in re-evaluating pathology-based diagnosis of "mucosal polyps" using expert endoscopists and computer-assisted diagnosis (CADx) evaluation.
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Affiliation(s)
- Roupen Djinbachian
- Division of Gastroenterology, University of Montreal Hospital Center (CHUM), Montreal, Quebec, Canada; University of Montreal Hospital Research Center (CRCHUM), Montreal, Quebec, Canada
| | | | - Douglas K Rex
- Division of Gastroenterology/Hepatology, Indiana University School of Medicine, Indianapolis, Indiana
| | - Heiko Pohl
- Department of Veterans Affairs Medical Center, White River Junction, Vermont; The Geisel School of Medicine at Dartmouth, Hanover, New Hampshire
| | - Mahsa Taghiakbari
- University of Montreal Hospital Research Center (CRCHUM), Montreal, Quebec, Canada
| | - Daniel von Renteln
- Division of Gastroenterology, University of Montreal Hospital Center (CHUM), Montreal, Quebec, Canada; University of Montreal Hospital Research Center (CRCHUM), Montreal, Quebec, Canada.
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Cheng Y, Li L, Bi Y, Su S, Zhang B, Feng X, Wang N, Zhang W, Yao Y, Ru N, Xiang J, Sun L, Hu K, Wen F, Wang Z, Bai L, Wang X, Wang R, Lv X, Wang P, Meng F, Xiao W, Linghu E, Chai N. Computer-aided diagnosis system for optical diagnosis of colorectal polyps under white light imaging. Dig Liver Dis 2024; 56:1738-1745. [PMID: 38744557 DOI: 10.1016/j.dld.2024.04.023] [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: 12/31/2023] [Revised: 03/21/2024] [Accepted: 04/23/2024] [Indexed: 05/16/2024]
Abstract
OBJECTIVES This study presents a novel computer-aided diagnosis (CADx) designed for optically diagnosing colorectal polyps using white light imaging (WLI).We aimed to evaluate the effectiveness of the CADx and its auxiliary role among endoscopists with different levels of expertise. METHODS We collected 2,324 neoplastic and 3,735 nonneoplastic polyp WLI images for model training, and 838 colorectal polyp images from 740 patients for model validation. We compared the diagnostic accuracy of the CADx with that of 15 endoscopists under WLI and narrow band imaging (NBI). The auxiliary benefits of CADx for endoscopists of different experience levels and for identifying different types of colorectal polyps was also evaluated. RESULTS The CADx demonstrated an optical diagnostic accuracy of 84.49%, showing considerable superiority over all endoscopists, irrespective of whether WLI or NBI was used (P < 0.001). Assistance from the CADx significantly improved the diagnostic accuracy of the endoscopists from 68.84% to 77.49% (P = 0.001), with the most significant impact observed among novice endoscopists. Notably, novices using CADx-assisted WLI outperform junior and expert endoscopists without such assistance. CONCLUSIONS The CADx demonstrated a crucial role in substantially enhancing the precision of optical diagnosis for colorectal polyps under WLI and showed the greatest auxiliary benefits for novice endoscopists.
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Affiliation(s)
- Yaxuan Cheng
- Chinese PLA Medical School, Beijing, 100853, PR China; Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Longsong Li
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Yawei Bi
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Song Su
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Bo Zhang
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Xiuxue Feng
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Nanjun Wang
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Wengang Zhang
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Yi Yao
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Nan Ru
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Jingyuan Xiang
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Lihua Sun
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Kang Hu
- Department of Gastroenterology, The 987 Hospital of PLA Joint Logistic Support Force, Baoji, 721004, PR China
| | - Feng Wen
- Department of Gastroenterology, General Hospital of Central Theater Command of PLA,Wuhan 430070, PR China
| | - Zixin Wang
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Lu Bai
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Xueting Wang
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Runzi Wang
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Xingping Lv
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Pengju Wang
- Chinese PLA Medical School, Beijing, 100853, PR China; Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Fanqi Meng
- Medical Department, HighWise Medical Technology Co, Ltd, Changsha, 410000, PR China
| | - Wen Xiao
- Medical Department, HighWise Medical Technology Co, Ltd, Changsha, 410000, PR China
| | - Enqiang Linghu
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China.
| | - Ningli Chai
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China.
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Ortiz O, Daca-Alvarez M, Rivero-Sanchez L, Gimeno-Garcia AZ, Carrillo-Palau M, Alvarez V, Ledo-Rodriguez A, Ricciardiello L, Pierantoni C, Hüneburg R, Nattermann J, Bisschops R, Tejpar S, Huerta A, Riu Pons F, Alvarez-Urturi C, López-Vicente J, Repici A, Hassan C, Cid L, Cavestro GM, Romero-Mascarell C, Gordillo J, Puig I, Herraiz M, Betes M, Herrero J, Jover R, Balaguer F, Pellisé M. An artificial intelligence-assisted system versus white light endoscopy alone for adenoma detection in individuals with Lynch syndrome (TIMELY): an international, multicentre, randomised controlled trial. Lancet Gastroenterol Hepatol 2024; 9:802-810. [PMID: 39033774 DOI: 10.1016/s2468-1253(24)00187-0] [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: 04/20/2024] [Revised: 05/27/2024] [Accepted: 05/29/2024] [Indexed: 07/23/2024]
Abstract
BACKGROUND Computer-aided detection (CADe) systems for colonoscopy have been shown to increase small polyp detection during colonoscopy in the general population. People with Lynch syndrome represent an ideal target population for CADe-assisted colonoscopy because adenomas, the primary cancer precursor lesions, are characterised by their small size and higher likelihood of showing advanced histology. We aimed to evaluate the performance of CADe-assisted colonoscopy in detecting adenomas in individuals with Lynch syndrome. METHODS TIMELY was an international, multicentre, parallel, randomised controlled trial done in 11 academic centres and six community centres in Belgium, Germany, Italy, and Spain. We enrolled individuals aged 18 years or older with pathogenic or likely pathogenic MLH1, MSH2, MSH6, or EPCAM variants. Participants were consecutively randomly assigned (1:1) to either CADe (GI Genius) assisted white light endoscopy (WLE) or WLE alone. A centre-stratified randomisation sequence was generated through a computer-generated system with a separate randomisation list for each centre according to block-permuted randomisation (block size 26 patients per centre). Allocation was automatically provided by the online AEG-REDCap database. Participants were masked to the random assignment but endoscopists were not. The primary outcome was the mean number of adenomas per colonoscopy, calculated by dividing the total number of adenomas detected by the total number of colonoscopies and assessed in the intention-to-treat population. This trial is registered with ClinicalTrials.gov, NCT04909671. FINDINGS Between Sept 13, 2021, and April 6, 2023, 456 participants were screened for eligibility, 430 of whom were randomly assigned to receive CADe-assisted colonoscopy (n=214) or WLE (n=216). 256 (60%) participants were female and 174 (40%) were male. In the intention-to-treat analysis, the mean number of adenomas per colonoscopy was 0·64 (SD 1·57) in the CADe group and 0·64 (1·17) in the WLE group (adjusted rate ratio 1·03 [95% CI 0·72-1·47); p=0·87). No adverse events were reported during the trial. INTERPRETATION In this multicentre international trial, CADe did not improve the detection of adenomas in individuals with Lynch syndrome. High-quality procedures and thorough inspection and exposure of the colonic mucosa remain the cornerstone in surveillance of Lynch syndrome. FUNDING Spanish Gastroenterology Association, Spanish Society of Digestive Endoscopy, European Society of Gastrointestinal Endoscopy, Societat Catalana de Digestologia, Instituto Carlos III, Beca de la Marato de TV3 2020. Co-funded by the European Union.
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Affiliation(s)
- Oswaldo Ortiz
- Hospital Clinic Barcelona, Gastroenterology Department, Barcelona, Spain; Centro de Investigación Biomédica en Red en Enfermedades Hepáticas y Digestivas (CIBEREHD), Institut d'Investigacions Biomédiques August Pi i Sunyer, Barcelona, Spain
| | - Maria Daca-Alvarez
- Hospital Clinic Barcelona, Gastroenterology Department, Barcelona, Spain; Centro de Investigación Biomédica en Red en Enfermedades Hepáticas y Digestivas (CIBEREHD), Institut d'Investigacions Biomédiques August Pi i Sunyer, Barcelona, Spain
| | - Liseth Rivero-Sanchez
- Hospital Clinic Barcelona, Gastroenterology Department, Barcelona, Spain; Centro de Investigación Biomédica en Red en Enfermedades Hepáticas y Digestivas (CIBEREHD), Institut d'Investigacions Biomédiques August Pi i Sunyer, Barcelona, Spain
| | | | - Marta Carrillo-Palau
- Hospital Universitario de Canarias, Digestive System Service, Santa Cruz de Tenerife, Spain
| | - Victoria Alvarez
- Complejo Hospitalario de Pontevedra, Department of Gastroenterology, Pontevedra, Spain
| | | | - Luigi Ricciardiello
- IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy; Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy; Gastroenterology, Hepatology, and Nutrition, University of Texas at MD Anderson Cancer Center, Houston, TX, USA
| | | | - Robert Hüneburg
- Department of Internal Medicine I and National Center for Hereditary Tumor Syndromes, University Hospital Bonn, Bonn, Germany; European Reference Network for Genetic Tumor Risk Syndromes (ERN Genturis), Bonn, Germany
| | - Jacob Nattermann
- Department of Internal Medicine I and National Center for Hereditary Tumor Syndromes, University Hospital Bonn, Bonn, Germany; European Reference Network for Genetic Tumor Risk Syndromes (ERN Genturis), Bonn, Germany
| | - Raf Bisschops
- Gastroenterology Department, University Hospital Leuven, Leuven, Belgium
| | - Sabine Tejpar
- Gastroenterology Department, University Hospital Leuven, Leuven, Belgium
| | - Alain Huerta
- Hospital Galdakao-Usansolo, Department of Gastroenterology, Galdakao, Spain
| | - Faust Riu Pons
- Gastroenterology Department, Hospital del Mar Research Institute, Barcelona, Spain
| | | | - Jorge López-Vicente
- Hospital Universitario de Móstoles, Digestive System Service, Móstoles, Spain
| | - Alessandro Repici
- Gastroenterology Department, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Cessare Hassan
- Gastroenterology Department, IRCCS Humanitas Research Hospital, Milan, Italy; Department of Biomedical Sciences, Humanitas University, Milan, Italy
| | - Lucia Cid
- Hospital Alvaro Cunqueiro, Galicia, Spain; Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
| | - Giulia Martina Cavestro
- Gastroenterology and Gastrointestinal Endoscopy Unit, Vita-Salute San Raffaele University, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | - Jordi Gordillo
- Hospital de la Santa Creu i Sant Pau, Gastroenterology Unit, Barcelona, Spain
| | - Ignasi Puig
- Institut de Recerca i Innovació en Ciències de la Vida i de la Salut a la Catalunya Central (IRIS-CC), Vic, Spain; Digestive Diseases Department, Althaia Xarxa Assistencial Universitària de Manresa, Manresa, Spain; Facultat de Medicina, Universitat de Vic-Central de Cataluña (UVIC-UCC), Vic, Spain
| | - Maite Herraiz
- University of Navarra Clinic-IdiSNA, Gastroenterology Department, Pamplona, Spain
| | - Maite Betes
- University of Navarra Clinic-IdiSNA, Gastroenterology Department, Pamplona, Spain
| | - Jesús Herrero
- Complexo Hospitalario Universitario de Ourense, Instituto de Investigación Biomédica Galicia Sur, CIBERehd, Ourense, Spain
| | - Rodrigo Jover
- Hospital Universitario de Alicante, Pais Valencia, Spain
| | - Francesc Balaguer
- Hospital Clinic Barcelona, Gastroenterology Department, Barcelona, Spain; Centro de Investigación Biomédica en Red en Enfermedades Hepáticas y Digestivas (CIBEREHD), Institut d'Investigacions Biomédiques August Pi i Sunyer, Barcelona, Spain; University of Barcelona, Barcelona, Spain
| | - Maria Pellisé
- Hospital Clinic Barcelona, Gastroenterology Department, Barcelona, Spain; Centro de Investigación Biomédica en Red en Enfermedades Hepáticas y Digestivas (CIBEREHD), Institut d'Investigacions Biomédiques August Pi i Sunyer, Barcelona, Spain; University of Barcelona, Barcelona, Spain.
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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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Vadhwana B, Tarazi M, Patel V. The Role of Artificial Intelligence in Prospective Real-Time Histological Prediction of Colorectal Lesions during Colonoscopy: A Systematic Review and Meta-Analysis. Diagnostics (Basel) 2023; 13:3267. [PMID: 37892088 PMCID: PMC10606449 DOI: 10.3390/diagnostics13203267] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 10/16/2023] [Accepted: 10/19/2023] [Indexed: 10/29/2023] Open
Abstract
Artificial intelligence (AI) presents a novel platform for improving disease diagnosis. However, the clinical utility of AI remains limited to discovery studies, with poor translation to clinical practice. Current data suggests that 26% of diminutive pre-malignant lesions and 3.5% of colorectal cancers are missed during colonoscopies. The primary aim of this study was to explore the role of artificial intelligence in real-time histological prediction of colorectal lesions during colonoscopy. A systematic search using MeSH headings relating to "AI", "machine learning", "computer-aided", "colonoscopy", and "colon/rectum/colorectal" identified 2290 studies. Thirteen studies reporting real-time analysis were included. A total of 2958 patients with 5908 colorectal lesions were included. A meta-analysis of six studies reporting sensitivities (95% CI) demonstrated that endoscopist diagnosis was superior to a computer-assisted detection platform, although no statistical significance was reached (p = 0.43). AI applications have shown encouraging results in differentiating neoplastic and non-neoplastic lesions using narrow-band imaging, white light imaging, and blue light imaging. Other modalities include autofluorescence imaging and elastic scattering microscopy. The current literature demonstrates that despite the promise of new endoscopic AI models, they remain inferior to expert endoscopist diagnosis. There is a need to focus developments on real-time histological predictions prior to clinical translation to demonstrate improved diagnostic capabilities and time efficiency.
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Affiliation(s)
- Bhamini Vadhwana
- Department of Surgery and Cancer, Imperial College London, Du Cane Road, London W12 0HS, UK
| | - Munir Tarazi
- Department of Surgery and Cancer, Imperial College London, Du Cane Road, London W12 0HS, UK
| | - Vanash Patel
- Department of Surgery and Cancer, Imperial College London, Du Cane Road, London W12 0HS, UK
- West Hertfordshire Hospital NHS Trust, Vicarage Road, Watford WD18 0HB, UK
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8
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Kaltenbach T, Patel SG, Nguyen-Vu T, Malvar C, Keswani RN, Hall M, Aagaard E, Asokkumar R, Chin YK, Hammad H, Rastogi A, Shergill A, Simon V, Soetikno A, Soetikno R, Wani S. Varied Trainee Competence in Cold Snare Polypectomy: Results of the COMPLETE Randomized Controlled Trial. Am J Gastroenterol 2023; 118:1880-1887. [PMID: 37307537 DOI: 10.14309/ajg.0000000000002368] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 04/27/2023] [Indexed: 06/14/2023]
Abstract
INTRODUCTION Cold snare polypectomy (CSP) is strongly recommended as the optimal technique for the complete removal of small polyps. Though significant variability in polypectomy technique and quality has been established, the learning curve and impact of targeted training on CSP are unknown. Video feedback has shown promise as an effective pedagogy to improve performance among surgical trainees. We aimed to compare CSP performance between trainees who received video-based feedback and those who received conventional apprentice-based concurrent feedback. We hypothesized that video-based feedback would accelerate competence. METHODS We conducted a single-blinded, randomized controlled trial on competence for CSP of polyps <1 cm, comparing video-based feedback with conventional feedback. We randomly assigned deidentified consecutively recorded CSP videos to blinded raters to assess using the CSP Assessment Tool. We shared cumulative sum learning curves every 25 CSP with each trainee. The video feedback trainees also received biweekly individualized terminal feedback. Control trainees received conventional feedback during colonoscopy. The primary outcome was CSP competence. We also assessed competence across domains and change over polypectomy volume. RESULTS We enrolled and randomized 22 trainees, 12 to video-based feedback and 10 to conventional feedback, and evaluated 2,339 CSP. The learning curve was long; 2 trainees (16.7%) in the video feedback achieved competence, after a mean of 135 polyps, and no one in the control ( P = 0.481) achieved competence. Overall and in all steps of CSP, a higher percentage of the video feedback group met competence, increasing 3% every 20 CSP ( P = 0.0004). DISCUSSION Video feedback aided trainees to competence in CSP. However, the learning curve was long. Our findings strongly suggest that current training methods are not sufficient to support trainees to competency by the completion of their fellowship programs. The impact of new training methods, such as simulation-based mastery learning, should be assessed to determine whether such methods can result in achievement of competence at a faster rate; ClinicalTrials.gov : NCT03115008.
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Affiliation(s)
- Tonya Kaltenbach
- Department of Medicine, University of California, San Francisco, San Francisco, California, USA
- Division of Gastroenterology, San Francisco Veterans Affairs Medical Center, San Francisco, California, USA
| | - Swati G Patel
- Division of Gastroenterology and Hepatology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
- Division of Gastroenterology, Rocky Mountain Regional Veterans Affairs Hospital, Aurora, Colorado, USA
| | - Tiffany Nguyen-Vu
- Department of Medicine, University of California, San Francisco, San Francisco, California, USA
- Division of Gastroenterology, San Francisco Veterans Affairs Medical Center, San Francisco, California, USA
| | - Carmel Malvar
- Department of Medicine, University of California, San Francisco, San Francisco, California, USA
- Division of Gastroenterology, San Francisco Veterans Affairs Medical Center, San Francisco, California, USA
| | - Rajesh N Keswani
- Department of Gastroenterology and Hepatology, Northwestern University, Chicago, Illinois, USA
| | - Matt Hall
- Biostatistics, Children's Hospital Association, Kansas City, Kansas, USA
| | - Eva Aagaard
- Department of Medicine, Division of General Internal Medicine, Washington University School of Medicine at St. Louis, St. Louis, Missouri, USA
| | - Ravishankar Asokkumar
- Department of Gastroenterology and Hepatology, Singapore General Hospital, Singapore
| | - Yung Ka Chin
- Department of Gastroenterology and Hepatology, Singapore General Hospital, Singapore
| | - Hazem Hammad
- Division of Gastroenterology and Hepatology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Amit Rastogi
- Division of Gastroenterology, Hepatology and Motility, University of Kansas Medical Center, Kansas City, Kansas, USA; and
| | - Amandeep Shergill
- Department of Medicine, University of California, San Francisco, San Francisco, California, USA
- Division of Gastroenterology, San Francisco Veterans Affairs Medical Center, San Francisco, California, USA
| | - Violette Simon
- Division of Gastroenterology and Hepatology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Alan Soetikno
- Northwestern University School of Medicine, Chicago, Illinois, USA
| | - Roy Soetikno
- Department of Medicine, University of California, San Francisco, San Francisco, California, USA
- Division of Gastroenterology, San Francisco Veterans Affairs Medical Center, San Francisco, California, USA
| | - Sachin Wani
- Division of Gastroenterology and Hepatology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
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González-Bueno Puyal J, Brandao P, Ahmad OF, Bhatia KK, Toth D, Kader R, Lovat L, Mountney P, Stoyanov D. Spatio-temporal classification for polyp diagnosis. BIOMEDICAL OPTICS EXPRESS 2023; 14:593-607. [PMID: 36874484 PMCID: PMC9979670 DOI: 10.1364/boe.473446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 11/25/2022] [Accepted: 12/06/2022] [Indexed: 06/18/2023]
Abstract
Colonoscopy remains the gold standard investigation for colorectal cancer screening as it offers the opportunity to both detect and resect pre-cancerous polyps. Computer-aided polyp characterisation can determine which polyps need polypectomy and recent deep learning-based approaches have shown promising results as clinical decision support tools. Yet polyp appearance during a procedure can vary, making automatic predictions unstable. In this paper, we investigate the use of spatio-temporal information to improve the performance of lesions classification as adenoma or non-adenoma. Two methods are implemented showing an increase in performance and robustness during extensive experiments both on internal and openly available benchmark datasets.
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Affiliation(s)
- Juana González-Bueno Puyal
- Wellcome/EPSRC Centre for Interventional
and Surgical Sciences (WEISS), University College London, London
W1W 7TY, UK
- Odin Vision, London W1W 7TY, UK
| | | | - Omer F. Ahmad
- Wellcome/EPSRC Centre for Interventional
and Surgical Sciences (WEISS), University College London, London
W1W 7TY, UK
| | | | | | - Rawen Kader
- Wellcome/EPSRC Centre for Interventional
and Surgical Sciences (WEISS), University College London, London
W1W 7TY, UK
| | - Laurence Lovat
- Wellcome/EPSRC Centre for Interventional
and Surgical Sciences (WEISS), University College London, London
W1W 7TY, UK
| | | | - Danail Stoyanov
- Wellcome/EPSRC Centre for Interventional
and Surgical Sciences (WEISS), University College London, London
W1W 7TY, UK
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10
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Scheppach MW, Rauber D, Stallhofer J, Muzalyova A, Otten V, Manzeneder C, Schwamberger T, Wanzl J, Schlottmann J, Tadic V, Probst A, Schnoy E, Römmele C, Fleischmann C, Meinikheim M, Miller S, Märkl B, Stallmach A, Palm C, Messmann H, Ebigbo A. Detection of duodenal villous atrophy on endoscopic images using a deep learning algorithm. Gastrointest Endosc 2023; 97:911-916. [PMID: 36646146 DOI: 10.1016/j.gie.2023.01.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 12/16/2022] [Accepted: 01/01/2023] [Indexed: 01/18/2023]
Abstract
BACKGROUND AND AIMS Celiac disease with its endoscopic manifestation of villous atrophy (VA) is underdiagnosed worldwide. The application of artificial intelligence (AI) for the macroscopic detection of VA at routine EGD may improve diagnostic performance. METHODS A dataset of 858 endoscopic images of 182 patients with VA and 846 images from 323 patients with normal duodenal mucosa was collected and used to train a ResNet18 deep learning model to detect VA. An external dataset was used to test the algorithm, in addition to 6 fellows and 4 board-certified gastroenterologists. Fellows could consult the AI algorithm's result during the test. From their consultation distribution, a stratification of test images into "easy" and "difficult" was performed and used for classified performance measurement. RESULTS External validation of the AI algorithm yielded values of 90%, 76%, and 84% for sensitivity, specificity, and accuracy, respectively. Fellows scored corresponding values of 63%, 72%, and 67% and experts scored 72%, 69%, and 71%, respectively. AI consultation significantly improved all trainee performance statistics. Although fellows and experts showed significantly lower performance for difficult images, the performance of the AI algorithm was stable. CONCLUSIONS In this study, an AI algorithm outperformed endoscopy fellows and experts in the detection of VA on endoscopic still images. AI decision support significantly improved the performance of nonexpert endoscopists. The stable performance on difficult images suggests a further positive add-on effect in challenging cases.
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Affiliation(s)
- Markus W Scheppach
- Internal Medicine III-Gastroenterology, University Hospital of Augsburg, Augsburg, Germany
| | - David Rauber
- Regensburg Medical Image Computing (ReMIC), Ostbayerische Technische Hochschule Regensburg, Regensburg, Germany; Regensburg Center of Biomedical Engineering, Ostbayerische Technische Hochschule Regensburg, Regensburg, Germany
| | - Johannes Stallhofer
- Department of Internal Medicine IV (Gastroenterology, Hepatology, and Infectious Diseases), Jena University Hospital, Jena, Germany
| | - Anna Muzalyova
- Internal Medicine III-Gastroenterology, University Hospital of Augsburg, Augsburg, Germany
| | - Vera Otten
- Internal Medicine III-Gastroenterology, University Hospital of Augsburg, Augsburg, Germany
| | - Carolin Manzeneder
- Internal Medicine III-Gastroenterology, University Hospital of Augsburg, Augsburg, Germany
| | - Tanja Schwamberger
- Internal Medicine III-Gastroenterology, University Hospital of Augsburg, Augsburg, Germany
| | - Julia Wanzl
- Internal Medicine III-Gastroenterology, University Hospital of Augsburg, Augsburg, Germany
| | - Jakob Schlottmann
- Internal Medicine III-Gastroenterology, University Hospital of Augsburg, Augsburg, Germany
| | - Vidan Tadic
- Internal Medicine III-Gastroenterology, University Hospital of Augsburg, Augsburg, Germany
| | - Andreas Probst
- Internal Medicine III-Gastroenterology, University Hospital of Augsburg, Augsburg, Germany
| | - Elisabeth Schnoy
- Internal Medicine III-Gastroenterology, University Hospital of Augsburg, Augsburg, Germany
| | - Christoph Römmele
- Internal Medicine III-Gastroenterology, University Hospital of Augsburg, Augsburg, Germany
| | - Carola Fleischmann
- Internal Medicine III-Gastroenterology, University Hospital of Augsburg, Augsburg, Germany
| | - Michael Meinikheim
- Internal Medicine III-Gastroenterology, University Hospital of Augsburg, Augsburg, Germany
| | - Silvia Miller
- Department of Pathology, University Hospital of Augsburg, Augsburg, Germany
| | - Bruno Märkl
- Department of Pathology, University Hospital of Augsburg, Augsburg, Germany
| | - Andreas Stallmach
- Department of Internal Medicine IV (Gastroenterology, Hepatology, and Infectious Diseases), Jena University Hospital, Jena, Germany
| | - Christoph Palm
- Regensburg Medical Image Computing (ReMIC), Ostbayerische Technische Hochschule Regensburg, Regensburg, Germany; Regensburg Center of Biomedical Engineering, Ostbayerische Technische Hochschule Regensburg, Regensburg, Germany
| | - Helmut Messmann
- Internal Medicine III-Gastroenterology, University Hospital of Augsburg, Augsburg, Germany
| | - Alanna Ebigbo
- Internal Medicine III-Gastroenterology, University Hospital of Augsburg, Augsburg, Germany
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11
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Ko YS, Choi YM, Kim M, Park Y, Ashraf M, Quiñones Robles WR, Kim MJ, Jang J, Yun S, Hwang Y, Jang H, Yi MY. Improving quality control in the routine practice for histopathological interpretation of gastrointestinal endoscopic biopsies using artificial intelligence. PLoS One 2022; 17:e0278542. [PMID: 36520777 PMCID: PMC9754254 DOI: 10.1371/journal.pone.0278542] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 11/18/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Colorectal and gastric cancer are major causes of cancer-related deaths. In Korea, gastrointestinal (GI) endoscopic biopsy specimens account for a high percentage of histopathologic examinations. Lack of a sufficient pathologist workforce can cause an increase in human errors, threatening patient safety. Therefore, we developed a digital pathology total solution combining artificial intelligence (AI) classifier models and pathology laboratory information system for GI endoscopic biopsy specimens to establish a post-analytic daily fast quality control (QC) system, which was applied in clinical practice for a 3-month trial run by four pathologists. METHODS AND FINDINGS Our whole slide image (WSI) classification framework comprised patch-generator, patch-level classifier, and WSI-level classifier. The classifiers were both based on DenseNet (Dense Convolutional Network). In laboratory tests, the WSI classifier achieved accuracy rates of 95.8% and 96.0% in classifying histopathological WSIs of colorectal and gastric endoscopic biopsy specimens, respectively, into three classes (Negative for dysplasia, Dysplasia, and Malignant). Classification by pathologic diagnosis and AI prediction were compared and daily reviews were conducted, focusing on discordant cases for early detection of potential human errors by the pathologists, allowing immediate correction, before the pathology report error is conveyed to the patients. During the 3-month AI-assisted daily QC trial run period, approximately 7-10 times the number of slides compared to that in the conventional monthly QC (33 months) were reviewed by pathologists; nearly 100% of GI endoscopy biopsy slides were double-checked by the AI models. Further, approximately 17-30 times the number of potential human errors were detected within an average of 1.2 days. CONCLUSIONS The AI-assisted daily QC system that we developed and established demonstrated notable improvements in QC, in quantitative, qualitative, and time utility aspects. Ultimately, we developed an independent AI-assisted post-analytic daily fast QC system that was clinically applicable and influential, which could enhance patient safety.
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Affiliation(s)
- Young Sin Ko
- Pathology Center, Seegene Medical Foundation, Seoul, Republic of Korea
| | - Yoo Mi Choi
- Pathology Center, Seegene Medical Foundation, Seoul, Republic of Korea
| | - Mujin Kim
- Graduate School of Data Science, Department of Industrial & Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Youngjin Park
- Graduate School of Data Science, Department of Industrial & Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Murtaza Ashraf
- Graduate School of Data Science, Department of Industrial & Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Willmer Rafell Quiñones Robles
- Graduate School of Data Science, Department of Industrial & Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Min-Ju Kim
- Department of Pathology, Incheon Sejong Hospital, Incheon, Republic of Korea
| | - Jiwook Jang
- AI Research Team, Digital Innovation Sector, Seegene Medical Foundation, Seoul, Republic of Korea
| | - Seokju Yun
- AI Research Team, Digital Innovation Sector, Seegene Medical Foundation, Seoul, Republic of Korea
| | - Yuri Hwang
- AI Research Team, Digital Innovation Sector, Seegene Medical Foundation, Seoul, Republic of Korea
| | - Hani Jang
- AI Research Team, Digital Innovation Sector, Seegene Medical Foundation, Seoul, Republic of Korea
| | - Mun Yong Yi
- Graduate School of Data Science, Department of Industrial & Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
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12
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Li MD, Huang ZR, Shan QY, Chen SL, Zhang N, Hu HT, Wang W. Performance and comparison of artificial intelligence and human experts in the detection and classification of colonic polyps. BMC Gastroenterol 2022; 22:517. [PMID: 36513975 PMCID: PMC9749329 DOI: 10.1186/s12876-022-02605-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 12/05/2022] [Indexed: 12/15/2022] Open
Abstract
OBJECTIVE The main aim of this study was to analyze the performance of different artificial intelligence (AI) models in endoscopic colonic polyp detection and classification and compare them with doctors with different experience. METHODS We searched the studies on Colonoscopy, Colonic Polyps, Artificial Intelligence, Machine Learning, and Deep Learning published before May 2020 in PubMed, EMBASE, Cochrane, and the citation index of the conference proceedings. The quality of studies was assessed using the QUADAS-2 table of diagnostic test quality evaluation criteria. The random-effects model was calculated using Meta-DISC 1.4 and RevMan 5.3. RESULTS A total of 16 studies were included for meta-analysis. Only one study (1/16) presented externally validated results. The area under the curve (AUC) of AI group, expert group and non-expert group for detection and classification of colonic polyps were 0.940, 0.918, and 0.871, respectively. AI group had slightly lower pooled specificity than the expert group (79% vs. 86%, P < 0.05), but the pooled sensitivity was higher than the expert group (88% vs. 80%, P < 0.05). While the non-experts had less pooled specificity in polyp recognition than the experts (81% vs. 86%, P < 0.05), and higher pooled sensitivity than the experts (85% vs. 80%, P < 0.05). CONCLUSION The performance of AI in polyp detection and classification is similar to that of human experts, with high sensitivity and moderate specificity. Different tasks may have an impact on the performance of deep learning models and human experts, especially in terms of sensitivity and specificity.
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Affiliation(s)
- Ming-De Li
- grid.412615.50000 0004 1803 6239Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080 People’s Republic of China
| | - Ze-Rong Huang
- grid.412615.50000 0004 1803 6239Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080 People’s Republic of China
| | - Quan-Yuan Shan
- grid.412615.50000 0004 1803 6239Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080 People’s Republic of China
| | - Shu-Ling Chen
- grid.412615.50000 0004 1803 6239Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080 People’s Republic of China
| | - Ning Zhang
- grid.412615.50000 0004 1803 6239Department of Gastroenterology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Hang-Tong Hu
- grid.412615.50000 0004 1803 6239Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080 People’s Republic of China
| | - Wei Wang
- grid.412615.50000 0004 1803 6239Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080 People’s Republic of China
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13
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Ebigbo A, Mendel R, Scheppach MW, Probst A, Shahidi N, Prinz F, Fleischmann C, Römmele C, Goelder SK, Braun G, Rauber D, Rueckert T, de Souza LA, Papa J, Byrne M, Palm C, Messmann H. Vessel and tissue recognition during third-space endoscopy using a deep learning algorithm. Gut 2022; 71:2388-2390. [PMID: 36109151 PMCID: PMC9664130 DOI: 10.1136/gutjnl-2021-326470] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 06/29/2022] [Indexed: 01/26/2023]
Abstract
In this study, we aimed to develop an artificial intelligence clinical decision support solution to mitigate operator-dependent limitations during complex endoscopic procedures such as endoscopic submucosal dissection and peroral endoscopic myotomy, for example, bleeding and perforation. A DeepLabv3-based model was trained to delineate vessels, tissue structures and instruments on endoscopic still images from such procedures. The mean cross-validated Intersection over Union and Dice Score were 63% and 76%, respectively. Applied to standardised video clips from third-space endoscopic procedures, the algorithm showed a mean vessel detection rate of 85% with a false-positive rate of 0.75/min. These performance statistics suggest a potential clinical benefit for procedure safety, time and also training.
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Affiliation(s)
- Alanna Ebigbo
- Department of Gastroenterology, Universitätsklinikum Augsburg, Augsburg, Germany
| | - Robert Mendel
- Regensburg Medical Image Computing (ReMIC), Ostbayerische Technische Hochschule Regensburg, Regensburg, Germany
| | - Markus W Scheppach
- Department of Gastroenterology, Universitätsklinikum Augsburg, Augsburg, Germany
| | - Andreas Probst
- Department of Gastroenterology, Universitätsklinikum Augsburg, Augsburg, Germany
| | - Neal Shahidi
- Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Friederike Prinz
- Department of Gastroenterology, Universitätsklinikum Augsburg, Augsburg, Germany
| | - Carola Fleischmann
- Department of Gastroenterology, Universitätsklinikum Augsburg, Augsburg, Germany
| | - Christoph Römmele
- Department of Gastroenterology, Universitätsklinikum Augsburg, Augsburg, Germany
| | | | - Georg Braun
- Department of Gastroenterology, Universitätsklinikum Augsburg, Augsburg, Germany
| | - David Rauber
- Regensburg Medical Image Computing (ReMIC), Ostbayerische Technische Hochschule Regensburg, Regensburg, Germany
| | - Tobias Rueckert
- Regensburg Medical Image Computing (ReMIC), Ostbayerische Technische Hochschule Regensburg, Regensburg, Germany
| | - Luis A de Souza
- Department of Computing, Federal University of São Carlos, São Carlos, Brazil
| | - Joao Papa
- Department of Computing, São Paulo State University, Botucatu, Brazil
| | - Michael Byrne
- Vancouver General Hospital, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Christoph Palm
- Regensburg Medical Image Computing (ReMIC), Ostbayerische Technische Hochschule Regensburg, Regensburg, Germany
| | - Helmut Messmann
- Department of Gastroenterology, Universitätsklinikum Augsburg, Augsburg, Germany
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14
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Taghiakbari M, Pohl H, Djinbachian R, Anderson JC, Metellus D, Barkun AN, Bouin M, von Renteln D. What size cutoff level should be used to implement optical polyp diagnosis? Endoscopy 2022; 54:1182-1190. [PMID: 35668663 DOI: 10.1055/a-1843-9535] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
BACKGROUND : The risk of advanced pathology increases with polyp size, as does the potential for mismanagement when optical diagnosis is used. This study aimed to evaluate the proportion of patients who would be assigned inadequate surveillance intervals when different size cutoffs are adopted for use of optical diagnosis. METHODS : In a post hoc analysis of three prospective studies, the use of optical diagnosis was evaluated for three polyp size groups: 1-3, 1-5, and 1-10 mm. The primary outcome was the proportion of patients in whom advanced adenomas were found and optical diagnosis resulted in delayed surveillance. Secondary outcomes included agreements between surveillance intervals based on high confidence optical diagnosis and pathology outcomes, reduction in histopathological examinations, and proportion of patients who could receive an immediate surveillance recommendation. RESULTS : We included 3374 patients (7291 polyps ≤ 10 mm) undergoing complete colonoscopies (median age 66.0 years, 75.2 % male, 29.6 % for screening). The percentage of patients with advanced adenomas and either 2- or 7-year delayed surveillance intervals (n = 79) was 3.8 %, 15.2 %, and 25.3 % for size cutoffs of 1-3, 1-5, and 1-10 mm polyps, respectively (P < 0.001). Surveillance interval agreements between pathology and optical diagnosis for the three groups were 97.2 %, 95.5 %, and 94.2 %, respectively. Total reductions in pathology examinations for the three groups were 33.5 %, 62.3 %, and 78.2 %, respectively. CONCLUSION : A 3-mm cutoff for clinical implementation of optical diagnosis resulted in a very low risk of delayed management of advanced neoplasia while showing high surveillance interval agreement with pathology and a one-third reduction in overall requirement for pathology examinations.
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Affiliation(s)
- Mahsa Taghiakbari
- University of Montreal, Montreal, Quebec, Canada
- University of Montreal Hospital Research Center (CRCHUM), Montreal, Quebec, Canada
| | - Heiko Pohl
- Dartmouth Geisel School of Medicine, Hanover, New Hampshire, United States
- VA Medical Center, Whiter River Junction, Vermont, United States
| | - Roupen Djinbachian
- University of Montreal Hospital Research Center (CRCHUM), Montreal, Quebec, Canada
- Division of Internal Medicine, University of Montreal Hospital Center (CHUM), Montreal, Quebec, Canada
| | - Joseph C Anderson
- Dartmouth Geisel School of Medicine, Hanover, New Hampshire, United States
- VA Medical Center, Whiter River Junction, Vermont, United States
| | - Danny Metellus
- University of Montreal Hospital Research Center (CRCHUM), Montreal, Quebec, Canada
- Division of Internal Medicine, University of Montreal Hospital Center (CHUM), Montreal, Quebec, Canada
| | - Alan N Barkun
- Division of Gastroenterology, McGill University Health Center, McGill University, Montreal, Quebec, Canada
| | - Mickael Bouin
- University of Montreal Hospital Research Center (CRCHUM), Montreal, Quebec, Canada
- Division of Gastroenterology, University of Montreal Hospital Center (CHUM), Montreal, Quebec, Canada
| | - Daniel von Renteln
- University of Montreal Hospital Research Center (CRCHUM), Montreal, Quebec, Canada
- Division of Gastroenterology, University of Montreal Hospital Center (CHUM), Montreal, Quebec, Canada
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15
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Biffi C, Salvagnini P, Dinh NN, Hassan C, Sharma P, Cherubini A. A novel AI device for real-time optical characterization of colorectal polyps. NPJ Digit Med 2022; 5:84. [PMID: 35773468 PMCID: PMC9247164 DOI: 10.1038/s41746-022-00633-6] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 06/16/2022] [Indexed: 01/03/2023] Open
Abstract
Accurate in-vivo optical characterization of colorectal polyps is key to select the optimal treatment regimen during colonoscopy. However, reported accuracies vary widely among endoscopists. We developed a novel intelligent medical device able to seamlessly operate in real-time using conventional white light (WL) endoscopy video stream without virtual chromoendoscopy (blue light, BL). In this work, we evaluated the standalone performance of this computer-aided diagnosis device (CADx) on a prospectively acquired dataset of unaltered colonoscopy videos. An international group of endoscopists performed optical characterization of each polyp acquired in a prospective study, blinded to both histology and CADx result, by means of an online platform enabling careful video assessment. Colorectal polyps were categorized by reviewers, subdivided into 10 experts and 11 non-experts endoscopists, and by the CADx as either “adenoma” or “non-adenoma”. A total of 513 polyps from 165 patients were assessed. CADx accuracy in WL was found comparable to the accuracy of expert endoscopists (CADxWL/Exp; OR 1.211 [0.766–1.915]) using histopathology as the reference standard. Moreover, CADx accuracy in WL was found superior to the accuracy of non-expert endoscopists (CADxWL/NonExp; OR 1.875 [1.191–2.953]), and CADx accuracy in BL was found comparable to it (CADxBL/CADxWL; OR 0.886 [0.612–1.282]). The proposed intelligent device shows the potential to support non-expert endoscopists in systematically reaching the performances of expert endoscopists in optical characterization.
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Affiliation(s)
- Carlo Biffi
- Artificial Intelligence Group, Cosmo AI/Linkverse, Lainate/Rome, Italy
| | - Pietro Salvagnini
- Artificial Intelligence Group, Cosmo AI/Linkverse, Lainate/Rome, Italy
| | - Nhan Ngo Dinh
- Artificial Intelligence Group, Cosmo AI/Linkverse, Lainate/Rome, Italy
| | - Cesare Hassan
- Gastroenterology Unit, Nuovo Regina Margherita Hospital, Rome, Italy.,Endoscopy Unit, Humanitas Clinical and Research Center IRCCS, Rozzano, Italy
| | - Prateek Sharma
- VA Medical Center, Kansas City, MO, USA.,University of Kansas School of Medicine, Kansas City, MO, USA
| | | | - Andrea Cherubini
- Artificial Intelligence Group, Cosmo AI/Linkverse, Lainate/Rome, Italy. .,Milan Center for Neuroscience, University of Milano-Bicocca, 20126, Milano, Italy.
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16
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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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17
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Cai YW, Dong FF, Shi YH, Lu LY, Chen C, Lin P, Xue YS, Chen JH, Chen SY, Luo XB. Deep learning driven colorectal lesion detection in gastrointestinal endoscopic and pathological imaging. World J Clin Cases 2021; 9:9376-9385. [PMID: 34877273 PMCID: PMC8610875 DOI: 10.12998/wjcc.v9.i31.9376] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 07/26/2021] [Accepted: 08/13/2021] [Indexed: 02/06/2023] Open
Abstract
Colorectal cancer has the second highest incidence of malignant tumors and is the fourth leading cause of cancer deaths in China. Early diagnosis and treatment of colorectal cancer will lead to an improvement in the 5-year survival rate, which will reduce medical costs. The current diagnostic methods for early colorectal cancer include excreta, blood, endoscopy, and computer-aided endoscopy. In this paper, research on image analysis and prediction of colorectal cancer lesions based on deep learning is reviewed with the goal of providing a reference for the early diagnosis of colorectal cancer lesions by combining computer technology, 3D modeling, 5G remote technology, endoscopic robot technology, and surgical navigation technology. The findings will supplement the research and provide insights to improve the cure rate and reduce the mortality of colorectal cancer.
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Affiliation(s)
- Yu-Wen Cai
- Department of Clinical Medicine, Fujian Medical University, Fuzhou 350004, Fujian Province, China
| | - Fang-Fen Dong
- Department of Medical Technology and Engineering, Fujian Medical University, Fuzhou 350004, Fujian Province, China
| | - Yu-Heng Shi
- Computer Science and Engineering College, University of Alberta, Edmonton T6G 2R3, Canada
| | - Li-Yuan Lu
- Department of Clinical Medicine, Fujian Medical University, Fuzhou 350004, Fujian Province, China
| | - Chen Chen
- Department of Clinical Medicine, Fujian Medical University, Fuzhou 350004, Fujian Province, China
| | - Ping Lin
- Department of Clinical Medicine, Fujian Medical University, Fuzhou 350004, Fujian Province, China
| | - Yu-Shan Xue
- Department of Clinical Medicine, Fujian Medical University, Fuzhou 350004, Fujian Province, China
| | - Jian-Hua Chen
- Endoscopy Center, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou 350014, Fujian Province, China
| | - Su-Yu Chen
- Endoscopy Center, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou 350014, Fujian Province, China
| | - Xiong-Biao Luo
- Department of Computer Science, Xiamen University, Xiamen 361005, Fujian, China
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18
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Kader R, Hadjinicolaou AV, Georgiades F, Stoyanov D, Lovat LB. Optical diagnosis of colorectal polyps using convolutional neural networks. World J Gastroenterol 2021; 27:5908-5918. [PMID: 34629808 PMCID: PMC8475008 DOI: 10.3748/wjg.v27.i35.5908] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 04/29/2021] [Accepted: 08/24/2021] [Indexed: 02/06/2023] Open
Abstract
Colonoscopy remains the gold standard investigation for colorectal cancer screening as it offers the opportunity to both detect and resect pre-malignant and neoplastic polyps. Although technologies for image-enhanced endoscopy are widely available, optical diagnosis has not been incorporated into routine clinical practice, mainly due to significant inter-operator variability. In recent years, there has been a growing number of studies demonstrating the potential of convolutional neural networks (CNN) to enhance optical diagnosis of polyps. Data suggest that the use of CNNs might mitigate the inter-operator variability amongst endoscopists, potentially enabling a "resect and discard" or "leave in" strategy to be adopted in real-time. This would have significant financial benefits for healthcare systems, avoid unnecessary polypectomies of non-neoplastic polyps and improve the efficiency of colonoscopy. Here, we review advances in CNN for the optical diagnosis of colorectal polyps, current limitations and future directions.
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Affiliation(s)
- Rawen Kader
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, United Kingdom
- Division of Surgery and Interventional Sciences, University College London, London W1W 7TY, United Kingdom
| | - Andreas V Hadjinicolaou
- MRC Cancer Unit, Department of Gastroenterology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Fanourios Georgiades
- Department of Surgery, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Danail Stoyanov
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, United Kingdom
- Department of Computer Science, University College London, London W1W 7TY, United Kingdom
| | - Laurence B Lovat
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, United Kingdom
- Division of Surgery and Interventional Sciences, University College London, London W1W 7TY, United Kingdom
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19
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Bang CS, Lee JJ, Baik GH. Computer-Aided Diagnosis of Diminutive Colorectal Polyps in Endoscopic Images: Systematic Review and Meta-analysis of Diagnostic Test Accuracy. J Med Internet Res 2021; 23:e29682. [PMID: 34432643 PMCID: PMC8427459 DOI: 10.2196/29682] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 07/04/2021] [Accepted: 07/27/2021] [Indexed: 12/19/2022] Open
Abstract
Background Most colorectal polyps are diminutive and benign, especially those in the rectosigmoid colon, and the resection of these polyps is not cost-effective. Advancements in image-enhanced endoscopy have improved the optical prediction of colorectal polyp histology. However, subjective interpretability and inter- and intraobserver variability prohibits widespread implementation. The number of studies on computer-aided diagnosis (CAD) is increasing; however, their small sample sizes limit statistical significance. Objective This review aims to evaluate the diagnostic test accuracy of CAD models in predicting the histology of diminutive colorectal polyps by using endoscopic images. Methods Core databases were searched for studies that were based on endoscopic imaging, used CAD models for the histologic diagnosis of diminutive colorectal polyps, and presented data on diagnostic performance. A systematic review and diagnostic test accuracy meta-analysis were performed. Results Overall, 13 studies were included. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD models for the diagnosis of diminutive colorectal polyps (adenomatous or neoplastic vs nonadenomatous or nonneoplastic) were 0.96 (95% CI 0.93-0.97), 0.93 (95% CI 0.91-0.95), 0.87 (95% CI 0.76-0.93), and 87 (95% CI 38-201), respectively. The meta-regression analysis showed no heterogeneity, and no publication bias was detected. Subgroup analyses showed robust results. The negative predictive value of CAD models for the diagnosis of adenomatous polyps in the rectosigmoid colon was 0.96 (95% CI 0.95-0.97), and this value exceeded the threshold of the diagnosis and leave strategy. Conclusions CAD models show potential for the optical histological diagnosis of diminutive colorectal polyps via the use of endoscopic images. Trial Registration PROSPERO CRD42021232189; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=232189
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Affiliation(s)
- Chang Seok Bang
- Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Republic of Korea.,Institute for Liver and Digestive Diseases, Hallym University College of Medicine, Chuncheon, Republic of Korea.,Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon, Republic of Korea.,Division of Big Data and Artificial Intelligence, Chuncheon Sacred Heart Hospital, Chuncheon, Republic of Korea
| | - Jae Jun Lee
- Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon, Republic of Korea.,Division of Big Data and Artificial Intelligence, Chuncheon Sacred Heart Hospital, Chuncheon, Republic of Korea.,Department of Anesthesiology and Pain Medicine, Hallym University College of Medicine, Chuncheon, Republic of Korea
| | - Gwang Ho Baik
- Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Republic of Korea.,Institute for Liver and Digestive Diseases, Hallym University College of Medicine, Chuncheon, Republic of Korea
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20
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Parsa N, Byrne MF. Artificial intelligence for identification and characterization of colonic polyps. Ther Adv Gastrointest Endosc 2021; 14:26317745211014698. [PMID: 34263163 PMCID: PMC8252334 DOI: 10.1177/26317745211014698] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 04/07/2021] [Indexed: 12/27/2022] Open
Abstract
Colonoscopy remains the gold standard exam for colorectal cancer screening due to its ability to detect and resect pre-cancerous lesions in the colon. However, its performance is greatly operator dependent. Studies have shown that up to one-quarter of colorectal polyps can be missed on a single colonoscopy, leading to high rates of interval colorectal cancer. In addition, the American Society for Gastrointestinal Endoscopy has proposed the “resect-and-discard” and “diagnose-and-leave” strategies for diminutive colorectal polyps to reduce the costs of unnecessary polyp resection and pathology evaluation. However, the performance of optical biopsy has been suboptimal in community practice. With recent improvements in machine-learning techniques, artificial intelligence–assisted computer-aided detection and diagnosis have been increasingly utilized by endoscopists. The application of computer-aided design on real-time colonoscopy has been shown to increase the adenoma detection rate while decreasing the withdrawal time and improve endoscopists’ optical biopsy accuracy, while reducing the time to make the diagnosis. These are promising steps toward standardization and improvement of colonoscopy quality, and implementation of “resect-and-discard” and “diagnose-and-leave” strategies. Yet, issues such as real-world applications and regulatory approval need to be addressed before artificial intelligence models can be successfully implemented in clinical practice. In this review, we summarize the recent literature on the application of artificial intelligence for detection and characterization of colorectal polyps and review the limitation of existing artificial intelligence technologies and future directions for this field.
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Affiliation(s)
- Nasim Parsa
- Division of Gastroenterology and Hepatology, Department of Medicine, University of Missouri, Columbia, MO 65211, USA
| | - Michael F Byrne
- Division of Gastroenterology, Department of Medicine, The University of British Columbia, Vancouver, BC, Canada; Satisfai Health, Vancouver, BC, Canada
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21
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Shao Y, Zhang YX, Chen HH, Lu SS, Zhang SC, Zhang JX. Advances in the application of artificial intelligence in solid tumor imaging. Artif Intell Cancer 2021; 2:12-24. [DOI: 10.35713/aic.v2.i2.12] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 04/02/2021] [Accepted: 04/20/2021] [Indexed: 02/06/2023] Open
Affiliation(s)
- Ying Shao
- Department of Laboratory Medicine, People Hospital of Jiangying, Jiangying 214400, Jiangsu Province, China
| | - Yu-Xuan Zhang
- Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
| | - Huan-Huan Chen
- Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
| | - Shan-Shan Lu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
| | - Shi-Chang Zhang
- Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
| | - Jie-Xin Zhang
- Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
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22
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Will machines decipher colonoscopy quality from endoscopists' notes? Gastrointest Endosc 2021; 93:758-760. [PMID: 33583525 DOI: 10.1016/j.gie.2020.11.021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Accepted: 11/20/2020] [Indexed: 12/31/2022]
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23
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Parsa N, Rex DK, Byrne MF. Colorectal polyp characterization with standard endoscopy: Will Artificial Intelligence succeed where human eyes failed? Best Pract Res Clin Gastroenterol 2021; 52-53:101736. [PMID: 34172255 DOI: 10.1016/j.bpg.2021.101736] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 02/16/2021] [Accepted: 02/18/2021] [Indexed: 01/31/2023]
Abstract
The American Society for Gastrointestinal Endoscopy (ASGE) has proposed the "resect-and-discard" and "diagnose-and-leave" strategies for diminutive colorectal polyps to reduce the costs of unnecessary polyp resection and pathology evaluation. However, the diagnostic thresholds set by these guidelines are not always met in community practice. To overcome this sub-optimal performance, artificial intelligence (AI) has been applied to the field of endoscopy. The incorporation of deep learning algorithms with AI models resulted in highly accurate systems that match the expert endoscopists' optical biopsy and exceed the ASGE recommended thresholds. Recent studies have demonstrated that the integration of AI in clinical practice results in significant improvement in endoscopists' diagnostic accuracy while reducing the time to make a diagnosis. Yet, several points need to be addressed before AI models can be successfully implemented in clinical practice. In this review, we summarize the recent literature on the application of AI for characterization of colorectal polyps, and review the current limitation and future directions for this field.
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Affiliation(s)
- Nasim Parsa
- University of Missouri, Department of Medicine, Division of Gastroenterology and Hepatology, Columbia, MO, United States
| | - Douglas K Rex
- Indiana University School of Medicine, Department of Medicine, Division of Gastroenterology and Hepatology, Indianapolis, IN, United States
| | - Michael F Byrne
- University of British Columbia, Department of Medicine, Division of Gastroenterology and Hepatology Vancouver, British Columbia, Canada; Satisfai Health and AI4GI Joint Venture, Vancouver, British Columbia, Canada.
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24
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Zhang Y, Zhang X, Wu Q, Gu C, Wang Z. Artificial Intelligence-Aided Colonoscopy for Polyp Detection: A Systematic Review and Meta-Analysis of Randomized Clinical Trials. J Laparoendosc Adv Surg Tech A 2021; 31:1143-1149. [PMID: 33524298 DOI: 10.1089/lap.2020.0777] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Background: This study aimed to compare artificial intelligence (AI)-aided colonoscopy with conventional colonoscopy for polyp detection. Methods: A systematic literature search was performed in PubMed and Ovid for randomized clinical trials (RCTs) comparing AI-aided colonoscopy with conventional colonoscopy for polyp detection. The last search was performed on July 22, 2020. The primary outcome was polyp detection rate (PDR) and adenoma detection rate (ADR). Results: Seven RCTs published between 2019 and 2020 with a total of 5427 individuals were included. When compared with conventional colonoscopy, AI-aided colonoscopy significantly improved PDR (P < .001, odds ratio [OR] = 1.95, 95% confidence interval [CI]: 1.75 to 2.19, I2 = 0%) and ADR (P < .001, OR = 1.72, 95% CI: 1.52 to 1.95, I2 = 33%). Besides, polyps in the AI-aided group were significantly smaller in size than those in conventional group (P = .004, weighted mean difference = -0.48, 95% CI: -0.81 to -0.15, I2 = 0%). In addition, AI-aided group detected significantly less proportion of advanced adenoma (P = .03, OR = 0.70, 95% CI: 0.50 to 0.97, I2 = 46%), pedicle polyps (P < .001, OR = 0.64, 95% CI: 0.49 to 0.83, I2 = 0%), and pedicle adenomas (P < .001, OR = 0.60, 95% CI: 0.44 to 0.80, I2 = 0%). Conclusion: AI-aided colonoscopy could significantly increase the PDR and ADR, especially for those with small size. Besides, the shape and pathology recognition of the AI technique should be further improved in the future.
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Affiliation(s)
- Yuanchuan Zhang
- Department of General Surgery, The Third People's Hospital of Chengdu, Chengdu, China
| | - Xubing Zhang
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Qingbin Wu
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Chaoyang Gu
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Ziqiang Wang
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu, China
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25
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Keswani RN, Byrd D, Garcia Vicente F, Heller JA, Klug M, Mazumder NR, Wood J, Yang AD, Etemadi M. Amalgamation of cloud-based colonoscopy videos with patient-level metadata to facilitate large-scale machine learning. Endosc Int Open 2021; 9:E233-E238. [PMID: 33553586 PMCID: PMC7857968 DOI: 10.1055/a-1326-1289] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 10/29/2020] [Indexed: 12/27/2022] Open
Abstract
Background and study aims Storage of full-length endoscopic procedures is becoming increasingly popular. To facilitate large-scale machine learning (ML) focused on clinical outcomes, these videos must be merged with the patient-level data in the electronic health record (EHR). Our aim was to present a method of accurately linking patient-level EHR data with cloud stored colonoscopy videos. Methods This study was conducted at a single academic medical center. Most procedure videos are automatically uploaded to the cloud server but are identified only by procedure time and procedure room. We developed and then tested an algorithm to match recorded videos with corresponding exams in the EHR based upon procedure time and room and subsequently extract frames of interest. Results Among 28,611 total colonoscopies performed over the study period, 21,170 colonoscopy videos in 20,420 unique patients (54.2 % male, median age 58) were matched to EHR data. Of 100 randomly sampled videos, appropriate matching was manually confirmed in all. In total, these videos represented 489,721 minutes of colonoscopy performed by 50 endoscopists (median 214 colonoscopies per endoscopist). The most common procedure indications were polyp screening (47.3 %), surveillance (28.9 %) and inflammatory bowel disease (9.4 %). From these videos, we extracted procedure highlights (identified by image capture; mean 8.5 per colonoscopy) and surrounding frames. Conclusions We report the successful merging of a large database of endoscopy videos stored with limited identifiers to rich patient-level data in a highly accurate manner. This technique facilitates the development of ML algorithms based upon relevant patient outcomes.
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Affiliation(s)
- Rajesh N. Keswani
- Digestive Health Center, Northwestern Medicine, Chicago, Illinois, United States
| | - Daniel Byrd
- Department of Anesthesiology, Northwestern Medicine, Chicago, Illinois, United States
| | | | - J. Alex Heller
- Department of Anesthesiology, Northwestern Medicine, Chicago, Illinois, United States
| | - Matthew Klug
- Department of Anesthesiology, Northwestern Medicine, Chicago, Illinois, United States
| | | | - Jordan Wood
- Digestive Health Center, Northwestern Medicine, Chicago, Illinois, United States
| | - Anthony D. Yang
- Surgical Outcomes and Quality Improvement Center, Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States
| | - Mozziyar Etemadi
- Department of Anesthesiology, Northwestern Medicine, Chicago, Illinois, United States
- Department of Biomedical Engineering, McCormick School of Engineering, Chicago, Illinois, United States
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26
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Milluzzo SM, Cesaro P, Grazioli LM, Olivari N, Spada C. Artificial Intelligence in Lower Gastrointestinal Endoscopy: The Current Status and Future Perspective. Clin Endosc 2021; 54:329-339. [PMID: 33434961 PMCID: PMC8182250 DOI: 10.5946/ce.2020.082] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 07/30/2020] [Indexed: 12/24/2022] Open
Abstract
The present manuscript aims to review the history, recent advances, evidence, and challenges of artificial intelligence (AI) in colonoscopy. Although it is mainly focused on polyp detection and characterization, it also considers other potential applications (i.e., inflammatory bowel disease) and future perspectives. Some of the most recent algorithms show promising results that are similar to human expert performance. The integration of AI in routine clinical practice will be challenging, with significant issues to overcome (i.e., regulatory, reimbursement). Medico-legal issues will also need to be addressed. With the exception of an AI system that is already available in selected countries (GI Genius; Medtronic, Minneapolis, MN, USA), the majority of the technology is still in its infancy and has not yet been proven to reach a sufficient diagnostic performance to be adopted in the clinical practice. However, larger players will enter the arena of AI in the next few months.
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Affiliation(s)
- Sebastian Manuel Milluzzo
- Digestive Endoscopy Unit and Gastroenterology, Fondazione Poliambulanza, Brescia, Italy.,Department of Gastroenterology, Fondazione Policlinico Universitario A. Gemelli IRCCS -Università Cattolica del Sacro Cuore, Roma, Italy
| | - Paola Cesaro
- Digestive Endoscopy Unit and Gastroenterology, Fondazione Poliambulanza, Brescia, Italy
| | | | - Nicola Olivari
- Digestive Endoscopy Unit and Gastroenterology, Fondazione Poliambulanza, Brescia, Italy
| | - Cristiano Spada
- Digestive Endoscopy Unit and Gastroenterology, Fondazione Poliambulanza, Brescia, Italy.,Department of Gastroenterology, Fondazione Policlinico Universitario A. Gemelli IRCCS -Università Cattolica del Sacro Cuore, Roma, Italy
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27
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Pannala R, Krishnan K, Melson J, Parsi MA, Schulman AR, Sullivan S, Trikudanathan G, Trindade AJ, Watson RR, Maple JT, Lichtenstein DR. Artificial intelligence in gastrointestinal endoscopy. VIDEOGIE : AN OFFICIAL VIDEO JOURNAL OF THE AMERICAN SOCIETY FOR GASTROINTESTINAL ENDOSCOPY 2020; 5:598-613. [PMID: 33319126 PMCID: PMC7732722 DOI: 10.1016/j.vgie.2020.08.013] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
BACKGROUND AND AIMS Artificial intelligence (AI)-based applications have transformed several industries and are widely used in various consumer products and services. In medicine, AI is primarily being used for image classification and natural language processing and has great potential to affect image-based specialties such as radiology, pathology, and gastroenterology (GE). This document reviews the reported applications of AI in GE, focusing on endoscopic image analysis. METHODS The MEDLINE database was searched through May 2020 for relevant articles by using key words such as machine learning, deep learning, artificial intelligence, computer-aided diagnosis, convolutional neural networks, GI endoscopy, and endoscopic image analysis. References and citations of the retrieved articles were also evaluated to identify pertinent studies. The manuscript was drafted by 2 authors and reviewed in person by members of the American Society for Gastrointestinal Endoscopy Technology Committee and subsequently by the American Society for Gastrointestinal Endoscopy Governing Board. RESULTS Deep learning techniques such as convolutional neural networks have been used in several areas of GI endoscopy, including colorectal polyp detection and classification, analysis of endoscopic images for diagnosis of Helicobacter pylori infection, detection and depth assessment of early gastric cancer, dysplasia in Barrett's esophagus, and detection of various abnormalities in wireless capsule endoscopy images. CONCLUSIONS The implementation of AI technologies across multiple GI endoscopic applications has the potential to transform clinical practice favorably and improve the efficiency and accuracy of current diagnostic methods.
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Key Words
- ADR, adenoma detection rate
- AI, artificial intelligence
- AMR, adenoma miss rate
- ANN, artificial neural network
- BE, Barrett’s esophagus
- CAD, computer-aided diagnosis
- CADe, CAD studies for colon polyp detection
- CADx, CAD studies for colon polyp classification
- CI, confidence interval
- CNN, convolutional neural network
- CRC, colorectal cancer
- DL, deep learning
- GI, gastroenterology
- HD-WLE, high-definition white light endoscopy
- HDWL, high-definition white light
- ML, machine learning
- NBI, narrow-band imaging
- NPV, negative predictive value
- PIVI, preservation and Incorporation of Valuable Endoscopic Innovations
- SVM, support vector machine
- VLE, volumetric laser endomicroscopy
- WCE, wireless capsule endoscopy
- WL, white light
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Affiliation(s)
- Rahul Pannala
- Department of Gastroenterology and Hepatology, Mayo Clinic, Scottsdale, Arizona
| | - Kumar Krishnan
- Division of Gastroenterology, Department of Internal Medicine, Harvard Medical School and Massachusetts General Hospital, Boston, Massachusetts
| | - Joshua Melson
- Division of Digestive Diseases, Department of Internal Medicine, Rush University Medical Center, Chicago, Illinois
| | - Mansour A Parsi
- Section for Gastroenterology and Hepatology, Tulane University Health Sciences Center, New Orleans, Louisiana
| | - Allison R Schulman
- Department of Gastroenterology, Michigan Medicine, University of Michigan, Ann Arbor, Michigan
| | - Shelby Sullivan
- Division of Gastroenterology and Hepatology, University of Colorado School of Medicine, Aurora, Colorado
| | - Guru Trikudanathan
- Department of Gastroenterology, Hepatology and Nutrition, University of Minnesota, Minneapolis, Minnesota
| | - Arvind J Trindade
- Department of Gastroenterology, Zucker School of Medicine at Hofstra/Northwell, Long Island Jewish Medical Center, New Hyde Park, New York
| | - Rabindra R Watson
- Department of Gastroenterology, Interventional Endoscopy Services, California Pacific Medical Center, San Francisco, California
| | - John T Maple
- Division of Digestive Diseases and Nutrition, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma
| | - David R Lichtenstein
- Division of Gastroenterology, Boston Medical Center, Boston University School of Medicine, Boston, Massachusetts
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A Gratifying Step forward for the Application of Artificial Intelligence in the Field of Endoscopy: A Narrative Review. Surg Laparosc Endosc Percutan Tech 2020; 31:254-263. [PMID: 33122593 PMCID: PMC8132898 DOI: 10.1097/sle.0000000000000881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 09/22/2020] [Indexed: 12/24/2022]
Abstract
Endoscopy is the optimal choice of diagnosis of gastrointestinal (GI) diseases. Following the advancements made in medical technology, different kinds of novel endoscopy-methods have emerged. Although the significant progress in the penetration of endoscopic tools that have markedly improved the diagnostic rate of GI diseases, there are still some limitations, including instability of human diagnostic performance caused by intensive labor burden and high missed diagnosis rate of subtle lesions. Recently, artificial intelligence (AI) has been applied gradually to assist endoscopists in addressing these issues.
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29
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Hassan C, Antonelli G, Repici A. Artificial intelligence for polyp characterization: Don't save on your competence! Gastrointest Endosc 2020; 92:912-913. [PMID: 32964835 DOI: 10.1016/j.gie.2020.04.060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 04/18/2020] [Indexed: 02/08/2023]
Affiliation(s)
- Cesare Hassan
- Endoscopy Unit, Nuovo Regina Margherita Hospital, Rome, Italy
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30
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Wang KW, Dong M. Potential applications of artificial intelligence in colorectal polyps and cancer: Recent advances and prospects. World J Gastroenterol 2020; 26:5090-5100. [PMID: 32982111 PMCID: PMC7495038 DOI: 10.3748/wjg.v26.i34.5090] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 07/01/2020] [Accepted: 08/12/2020] [Indexed: 02/06/2023] Open
Abstract
Since the advent of artificial intelligence (AI) technology, it has been constantly studied and has achieved rapid development. The AI assistant system is expected to improve the quality of automatic polyp detection and classification. It could also help prevent endoscopists from missing polyps and make an accurate optical diagnosis. These functions provided by AI could result in a higher adenoma detection rate and decrease the cost of polypectomy for hyperplastic polyps. In addition, AI has good performance in the staging, diagnosis, and segmentation of colorectal cancer. This article provides an overview of recent research focusing on the application of AI in colorectal polyps and cancer and highlights the advances achieved.
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Affiliation(s)
- Ke-Wei Wang
- Department of Gastrointestinal Surgery, the First Affiliated Hospital of China Medical University, Shenyang 110001, Liaoning Province, China
| | - Ming Dong
- Department of Gastrointestinal Surgery, the First Affiliated Hospital of China Medical University, Shenyang 110001, Liaoning Province, China
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31
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Shung DL, Byrne MF. How Artificial Intelligence Will Impact Colonoscopy and Colorectal Screening. Gastrointest Endosc Clin N Am 2020; 30:585-595. [PMID: 32439090 PMCID: PMC12007662 DOI: 10.1016/j.giec.2020.02.010] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Artificial intelligence may improve value in colonoscopy-based colorectal screening and surveillance by improving quality and decreasing unnecessary costs. The quality of screening and surveillance as measured by adenoma detection rates can be improved through real-time computer-assisted detection of polyps. Unnecessary costs can be decreased with optical biopsies to identify low-risk polyps using computer-assisted diagnosis that can undergo the resect-and-discard or diagnose-and-leave strategy. Key challenges include the clinical integration of artificial intelligence-based technology into the endoscopists' workflow, the effect of this technology on endoscopy center efficiency, and the interpretability of the underlying deep learning algorithms. The future for image-based artificial intelligence in gastroenterology will include applications to improve the diagnosis and treatment of cancers throughout the gastrointestinal tract.
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Affiliation(s)
- Dennis L Shung
- Section of Digestive Diseases, Department of Medicine, Yale School of Medicine, P.O. Box 208019, New Haven, CT 06520-8019, USA
| | - Michael F Byrne
- Division of Gastroenterology, Vancouver General Hospital, University of British Columbia, 5153 - 2775 Laurel Street, Vancouver, British Columbia, Canada.
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32
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Jin EH, Lee D, Bae JH, Kang HY, Kwak MS, Seo JY, Yang JI, Yang SY, Lim SH, Yim JY, Lim JH, Chung GE, Chung SJ, Choi JM, Han YM, Kang SJ, Lee J, Chan Kim H, Kim JS. Improved Accuracy in Optical Diagnosis of Colorectal Polyps Using Convolutional Neural Networks with Visual Explanations. Gastroenterology 2020; 158:2169-2179.e8. [PMID: 32119927 DOI: 10.1053/j.gastro.2020.02.036] [Citation(s) in RCA: 90] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 01/10/2020] [Accepted: 02/20/2020] [Indexed: 02/08/2023]
Abstract
BACKGROUND & AIMS Narrow-band imaging (NBI) can be used to determine whether colorectal polyps are adenomatous or hyperplastic. We investigated whether an artificial intelligence (AI) system can increase the accuracy of characterizations of polyps by endoscopists of different skill levels. METHODS We developed convolutional neural networks (CNNs) for evaluation of diminutive colorectal polyps, based on efficient neural architecture searches via parameter sharing with augmentation using NBIs of diminutive (≤5 mm) polyps, collected from October 2015 through October 2017 at the Seoul National University Hospital, Healthcare System Gangnam Center (training set). We trained the CNN using images from 1100 adenomatous polyps and 1050 hyperplastic polyps from 1379 patients. We then tested the system using 300 images of 180 adenomatous polyps and 120 hyperplastic polyps, obtained from January 2018 to May 2019. We compared the accuracy of 22 endoscopists of different skill levels (7 novices, 4 experts, and 11 NBI-trained experts) vs the CNN in evaluation of images (adenomatous vs hyperplastic) from 180 adenomatous and 120 hyperplastic polyps. The endoscopists then evaluated the polyp images with knowledge of the CNN-processed results. We conducted mixed-effect logistic and linear regression analyses to determine the effects of AI assistance on the accuracy of analysis of diminutive colorectal polyps by endoscopists (primary outcome). RESULTS The CNN distinguished adenomatous vs hyperplastic diminutive polyps with 86.7% accuracy, based on histologic analysis as the reference standard. Endoscopists distinguished adenomatous vs hyperplastic diminutive polyps with 82.5% overall accuracy (novices, 73.8% accuracy; experts, 83.8% accuracy; and NBI-trained experts, 87.6% accuracy). With knowledge of the CNN-processed results, the overall accuracy of the endoscopists increased to 88.5% (P < .05). With knowledge of the CNN-processed results, the accuracy of novice endoscopists increased to 85.6% (P < .05). The CNN-processed results significantly reduced endoscopist time of diagnosis (from 3.92 to 3.37 seconds per polyp, P = .042). CONCLUSIONS We developed a CNN that significantly increases the accuracy of evaluation of diminutive colorectal polyps (as adenomatous vs hyperplastic) and reduces the time of diagnosis by endoscopists. This AI assistance system significantly increased the accuracy of analysis by novice endoscopists, who achieved near-expert levels of accuracy without extra training. The CNN assistance system can reduce the skill-level dependence of endoscopists and costs.
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Affiliation(s)
- Eun Hyo Jin
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Dongheon Lee
- Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, Korea
| | - Jung Ho Bae
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Hae Yeon Kang
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Min-Sun Kwak
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Ji Yeon Seo
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Jong In Yang
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Sun Young Yang
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Seon Hee Lim
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Jeong Yoon Yim
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Joo Hyun Lim
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Goh Eun Chung
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Su Jin Chung
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Ji Min Choi
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Yoo Min Han
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Seung Joo Kang
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Jooyoung Lee
- Department of Internal Medicine, Liver Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Hee Chan Kim
- Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, Korea; Department of Biomedical Engineering College of Medicine, Seoul National University, Seoul, Korea; Institute of Medical & Biological Engineering, Medical Research Center, Seoul National University, Seoul, Korea.
| | - Joo Sung Kim
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea; Department of Internal Medicine, Liver Research Institute, Seoul National University College of Medicine, Seoul, Korea.
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33
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Byrne MF. Hype or Reality? Will Artificial Intelligence Actually Make Us Better at Performing Optical Biopsy of Colon Polyps? Gastroenterology 2020; 158:2049-2051. [PMID: 32222397 DOI: 10.1053/j.gastro.2020.03.038] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 03/20/2020] [Indexed: 12/27/2022]
Affiliation(s)
- Michael F Byrne
- Division of Gastroenterology, Vancouver General Hospital, University of British Columbia, Vancouver, British Columbia, Canada.
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34
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Shahidi N, Vosko S, van Hattem WA, Sidhu M, Bourke MJ. Optical evaluation: the crux for effective management of colorectal neoplasia. Therap Adv Gastroenterol 2020; 13:1756284820922746. [PMID: 32523625 PMCID: PMC7235649 DOI: 10.1177/1756284820922746] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Accepted: 04/06/2020] [Indexed: 02/04/2023] Open
Abstract
Advances in minimally invasive tissue resection techniques now allow for the majority of early colorectal neoplasia to be managed endoscopically. To optimize their respective risk-benefit profiles, and, therefore, appropriately select between endoscopic mucosal resection, endoscopic submucosal dissection, and surgery, the endoscopist must accurately predict the risk of submucosal invasive cancer and estimate depth of invasion. Herein, we discuss the evidence and our approach for optical evaluation of large (⩾ 20 mm) colorectal laterally spreading lesions.
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Affiliation(s)
- Neal Shahidi
- Department of Gastroenterology and Hepatology, Westmead Hospital, Sydney, NSW, Australia
- Westmead Clinical School, University of Sydney, Sydney, NSW, Australia
- Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Sergei Vosko
- Department of Gastroenterology and Hepatology, Westmead Hospital, Sydney, NSW, Australia
| | - W. Arnout van Hattem
- Department of Gastroenterology and Hepatology, Westmead Hospital, Sydney, NSW, Australia
| | - Mayenaaz Sidhu
- Department of Gastroenterology and Hepatology, Westmead Hospital, Sydney, NSW, Australia
- Westmead Clinical School, University of Sydney, Sydney, NSW, Australia
| | - Michael J. Bourke
- Clinical Professor of Medicine, Department of Gastroenterology and Hepatology, Westmead Hospital, University of Sydney, Westmead Clinical School, Suite 106a 151-155 Hawkesbury Road, Sydney, NSW 2145, Australia
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