{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T03:15:57Z","timestamp":1766546157749,"version":"build-2065373602"},"reference-count":95,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T00:00:00Z","timestamp":1727654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Artificial intelligence (AI) has emerged as a transformative tool across several specialties, namely gastroenterology, where it has the potential to optimize both diagnosis and treatment as well as enhance patient care. Coloproctology, due to its highly prevalent pathologies and tremendous potential to cause significant mortality and morbidity, has drawn a lot of attention regarding AI applications. In fact, its application has yielded impressive outcomes in various domains, colonoscopy being one prominent example, where it aids in the detection of polyps and early signs of colorectal cancer with high accuracy and efficiency. With a less explored path but equivalent promise, AI-powered capsule endoscopy ensures accurate and time-efficient video readings, already detecting a wide spectrum of anomalies. High-resolution anoscopy is an area that has been growing in interest in recent years, with efforts being made to integrate AI. There are other areas, such as functional studies, that are currently in the early stages, but evidence is expected to emerge soon. According to the current state of research, AI is anticipated to empower gastroenterologists in the decision-making process, paving the way for a more precise approach to diagnosing and treating patients. This review aims to provide the state-of-the-art use of AI in coloproctology while also reflecting on future directions and perspectives.<\/jats:p>","DOI":"10.3390\/jcm13195842","type":"journal-article","created":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T07:19:37Z","timestamp":1727680777000},"page":"5842","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Artificial Intelligence in Coloproctology: A Review of Emerging Technologies and Clinical Applications"],"prefix":"10.3390","volume":"13","author":[{"given":"Joana","family":"Mota","sequence":"first","affiliation":[{"name":"Precision Medicine Unit, Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, 4200-427 Porto, Portugal"},{"name":"WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal"}]},{"given":"Maria Jo\u00e3o","family":"Almeida","sequence":"additional","affiliation":[{"name":"Precision Medicine Unit, Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, 4200-427 Porto, Portugal"},{"name":"WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0484-4804","authenticated-orcid":false,"given":"Miguel","family":"Martins","sequence":"additional","affiliation":[{"name":"Precision Medicine Unit, Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, 4200-427 Porto, Portugal"},{"name":"WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5890-7049","authenticated-orcid":false,"given":"Francisco","family":"Mendes","sequence":"additional","affiliation":[{"name":"Precision Medicine Unit, Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, 4200-427 Porto, Portugal"},{"name":"WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal"}]},{"given":"Pedro","family":"Cardoso","sequence":"additional","affiliation":[{"name":"Precision Medicine Unit, Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, 4200-427 Porto, Portugal"},{"name":"WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal"}]},{"given":"Jo\u00e3o","family":"Afonso","sequence":"additional","affiliation":[{"name":"Precision Medicine Unit, Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, 4200-427 Porto, Portugal"},{"name":"WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal"}]},{"given":"Tiago","family":"Ribeiro","sequence":"additional","affiliation":[{"name":"Precision Medicine Unit, Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, 4200-427 Porto, Portugal"},{"name":"WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4310-2915","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Ferreira","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Faculty of Engineering, University of Porto, 4200-065 Porto, Portugal"},{"name":"DigestAID\u2014Digestive Artificial Intelligence Development, Rua Alfredo Allen n.\u00b0 455\/461, 4200-135 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9003-3871","authenticated-orcid":false,"given":"Filipa","family":"Fonseca","sequence":"additional","affiliation":[{"name":"Instituto Portugu\u00eas de Oncologia de Lisboa Francisco Gentil (IPO Lisboa), 1099-023 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4645-1574","authenticated-orcid":false,"given":"Manuel","family":"Limbert","sequence":"additional","affiliation":[{"name":"Instituto Portugu\u00eas de Oncologia de Lisboa Francisco Gentil (IPO Lisboa), 1099-023 Lisboa, Portugal"},{"name":"Artificial Intelligence Group of the Portuguese Society of Coloproctology, 1050-117 Lisboa, Portugal"}]},{"given":"Susana","family":"Lopes","sequence":"additional","affiliation":[{"name":"Precision Medicine Unit, Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, 4200-427 Porto, Portugal"},{"name":"WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal"},{"name":"Artificial Intelligence Group of the Portuguese Society of Coloproctology, 1050-117 Lisboa, Portugal"},{"name":"Faculty of Medicine, University of Porto, 4200-047 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9387-9872","authenticated-orcid":false,"given":"Guilherme","family":"Macedo","sequence":"additional","affiliation":[{"name":"Precision Medicine Unit, Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, 4200-427 Porto, Portugal"},{"name":"WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal"},{"name":"Faculty of Medicine, University of Porto, 4200-047 Porto, Portugal"}]},{"given":"Fernando","family":"Castro Po\u00e7as","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Group of the Portuguese Society of Coloproctology, 1050-117 Lisboa, Portugal"},{"name":"Department of Gastroenterology, Santo Ant\u00f3nio University Hospital, 4099-001 Porto, Portugal"},{"name":"Abel Salazar Biomedical Sciences Institute (ICBAS), 4050-313 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0340-0830","authenticated-orcid":false,"given":"Miguel","family":"Mascarenhas","sequence":"additional","affiliation":[{"name":"Precision Medicine Unit, Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, 4200-427 Porto, Portugal"},{"name":"WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal"},{"name":"Artificial Intelligence Group of the Portuguese Society of Coloproctology, 1050-117 Lisboa, Portugal"},{"name":"Faculty of Medicine, University of Porto, 4200-047 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Biol. Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1136\/flgastro-2019-101239","article-title":"Power of big data to improve patient care in gastroenterology","volume":"13","author":"Catlow","year":"2022","journal-title":"Front. Gastroenterol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"17562848211017730","DOI":"10.1177\/17562848211017730","article-title":"Artificial intelligence in gastrointestinal endoscopy for inflammatory bowel disease: A systematic review and new horizons","volume":"14","author":"Tontini","year":"2021","journal-title":"Ther. Adv. Gastroenterol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2748","DOI":"10.1038\/s41598-022-06726-2","article-title":"Artificial intelligence enabled automated diagnosis and grading of ulcerative colitis endoscopy images","volume":"12","author":"Sutton","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1111\/den.14531","article-title":"Benefits and challenges in implementation of artificial intelligence in colonoscopy: World endoscopy organization position statement","volume":"35","author":"Mori","year":"2023","journal-title":"Dig. Endosc."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1298","DOI":"10.1056\/NEJMoa1309086","article-title":"Adenoma detection rate and risk of colorectal cancer and death","volume":"370","author":"Corley","year":"2014","journal-title":"N. Engl. J. Med."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"949","DOI":"10.1136\/gutjnl-2012-303796","article-title":"Colorectal cancers soon after colonoscopy: A pooled multicohort analysis","volume":"63","author":"Robertson","year":"2014","journal-title":"Gut"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1661","DOI":"10.1053\/j.gastro.2019.01.260","article-title":"Magnitude, risk factors, and factors associated with adenoma miss rate of tandem colonoscopy: A systematic review and me-ta-analysis","volume":"156","author":"Zhao","year":"2019","journal-title":"Gastroenterology"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1016\/S2468-1253(19)30411-X","article-title":"Effect of a deep-learning com-puter-aided detection system on adenoma detection during colonoscopy (cade-db trial): A double-blind randomised study","volume":"5","author":"Wang","year":"2020","journal-title":"Lancet Gastroenterol. Hepatol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"13","DOI":"10.4103\/sjg.SJG_377_19","article-title":"Study on detection rate of polyps and adenomas in artificial-intelligence-aided colonoscopy","volume":"26","author":"Huang","year":"2020","journal-title":"Saudi J. Gastroenterol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1053\/j.gastro.2020.04.062","article-title":"Efficacy of real-time computer-aided detection of colorectal neoplasia in a randomized trial","volume":"159","author":"Repici","year":"2020","journal-title":"Gastroenterology"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1136\/gutjnl-2021-324471","article-title":"Artificial intelligence and colonoscopy experience: Lessons from two randomised trials","volume":"71","author":"Repici","year":"2022","journal-title":"Gut"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"746","DOI":"10.1007\/s00535-021-01808-w","article-title":"Reducing adenoma miss rate of colonoscopy assisted by artificial intelligence: A multicenter randomized controlled trial","volume":"56","author":"Kamba","year":"2021","journal-title":"J. Gastroenterol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1016\/j.cgh.2021.09.009","article-title":"Deep learning computer-aided polyp detection reduces adenoma miss rate: A united states multi-center randomized tandem colonoscopy study (cadet-cs trial)","volume":"20","author":"Brown","year":"2022","journal-title":"Clin. Gastroenterol. Hepatol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.cgh.2022.07.006","article-title":"Artificial intelligence\u2013assisted co-lonoscopy for colorectal cancer screening: A multicenter randomized controlled trial","volume":"21","author":"Xu","year":"2023","journal-title":"Clin. Gastroenterol. Hepatol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"732","DOI":"10.1053\/j.gastro.2022.05.028","article-title":"Computer-aided detection improves adenomas per colonoscopy for screening and surveillance colonoscopy: A randomized trial","volume":"163","author":"Shaukat","year":"2022","journal-title":"Gastroenterology"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/S2468-1253(19)30413-3","article-title":"Detection of colorectal adenomas with a real-time computer-aided system (endoangel): A randomised controlled study","volume":"5","author":"Gong","year":"2020","journal-title":"Lancet Gastroenterol. Hepatol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.gie.2019.08.026","article-title":"Impact of a real-time auto-matic quality control system on colorectal polyp and adenoma detection: A prospective randomized controlled study (with videos)","volume":"91","author":"Su","year":"2020","journal-title":"Gastrointest. Endosc."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1252","DOI":"10.1053\/j.gastro.2020.06.023","article-title":"Lower adenoma miss rate of computer-aided detection-assisted colonoscopy vs routine white-light colonoscopy in a prospective tandem study","volume":"159","author":"Wang","year":"2020","journal-title":"Gastroenterology"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1813","DOI":"10.1136\/gutjnl-2018-317500","article-title":"Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: A prospective randomised controlled study","volume":"68","author":"Wang","year":"2019","journal-title":"Gut"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1016\/j.gie.2022.09.023","article-title":"Computer-aided detection, mucosal exposure device, their combination, and standard colonoscopy for adenoma detection: A randomized controlled trial","volume":"97","author":"Aniwan","year":"2023","journal-title":"Gastrointest. Endosc."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1136\/gutjnl-2017-314547","article-title":"Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model","volume":"68","author":"Byrne","year":"2019","journal-title":"Gut"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1016\/j.cgh.2005.12.021","article-title":"Prevalence of clinically important histology in small adenomas","volume":"4","author":"Butterly","year":"2006","journal-title":"Clin. Gastroenterol. Hepatol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1016\/j.gie.2011.01.023","article-title":"The american society for gastrointestinal endoscopy pivi (preservation and incorporation of valuable endoscopic innovations) on real-time endoscopic assessment of the histology of diminutive colorectal polyps","volume":"73","author":"Rex","year":"2011","journal-title":"Gastrointest. Endosc."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1002\/ueg2.12108","article-title":"Current status and limitations of artificial intelligence in colonoscopy","volume":"9","author":"Hann","year":"2021","journal-title":"United Eur. Gastroenterol. J."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Kim, H.J., Parsa, N., and Byrne, M.F. (2024). The role of artificial intelligence in colonoscopy. Seminars in Colon and Rectal Surgery, Elsevier.","DOI":"10.1016\/j.scrs.2024.101007"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1053\/j.gastro.2017.10.010","article-title":"Accurate classification of diminutive colorectal polyps using computer-aided analysis","volume":"154","author":"Chen","year":"2018","journal-title":"Gastroenterology"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1016\/j.cgh.2010.05.018","article-title":"A resect and discard strategy would improve cost-effectiveness of colorectal cancer screening","volume":"8","author":"Hassan","year":"2010","journal-title":"Clin. Gastroenterol. Hepatol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"239","DOI":"10.5946\/ce.2018.136","article-title":"Should we resect and discard low risk diminutive colon polyps","volume":"52","author":"Kandel","year":"2019","journal-title":"Clin. Endosc."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1055\/s-0030-1256381","article-title":"A quantitative assessment of the risks and cost savings of forgoing histologic examination of diminutive polyps","volume":"43","author":"Kessler","year":"2011","journal-title":"Endoscopy"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.1177\/2050640618769731","article-title":"Leaving colorectal polyps in place can be achieved with high accuracy using blue light imaging (bli)","volume":"6","author":"Neumann","year":"2018","journal-title":"United Eur. Gastroenterol. J."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"706","DOI":"10.1016\/j.cgh.2017.11.036","article-title":"Simplifying resect and discard strategies for real-time assessment of diminutive colorectal polyps","volume":"16","author":"Kaltenbach","year":"2018","journal-title":"Clin. Gastroenterol. Hepatol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2505","DOI":"10.1016\/j.cgh.2022.04.045","article-title":"Artificial intelligence allows leaving-in-situ colorectal polyps","volume":"20","author":"Hassan","year":"2022","journal-title":"Clin. Gastroenterol. Hepatol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"138","DOI":"10.14309\/ajg.0000000000000429","article-title":"Prediction of polyp pathology using convolutional neural networks achieves \u201cresect and discard\u201d thresholds","volume":"115","author":"Zachariah","year":"2019","journal-title":"Off. J. Am. Coll. Gastroenterol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1159\/000481230","article-title":"Magnifying narrow band imaging (nbi) for the diagnosis of localized colorectal lesions using the japan nbi expert team (jnet) classification","volume":"93","author":"Komeda","year":"2017","journal-title":"Oncology"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"6627","DOI":"10.1007\/s00464-023-10223-6","article-title":"Computer-aided diagnosis in predicting the invasion depth of early colorectal cancer: A systematic review and meta-analysis of diagnostic test accuracy","volume":"37","author":"Bai","year":"2023","journal-title":"Surg. Endosc."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.vgie.2018.10.006","article-title":"Simultaneous detection and characterization of diminutive polyps with the use of artificial intelligence during colonoscopy","volume":"4","author":"Mori","year":"2019","journal-title":"VideoGIE"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"S48","DOI":"10.1016\/S0016-5085(19)36900-8","article-title":"256\u2013artificial intelligence for real-time multiple polyp detection with identification, tracking, and optical biopsy during colonoscopy","volume":"156","author":"Guizard","year":"2019","journal-title":"Gastroenterology"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1756284820910659","DOI":"10.1177\/1756284820910659","article-title":"Automated endoscopic detection and classification of colorectal polyps using convolutional neural networks","volume":"13","author":"Ozawa","year":"2020","journal-title":"Ther. Adv. Gastroenterol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"50","DOI":"10.3748\/wjg.v30.i1.50","article-title":"Treat to target in Crohn\u2019s disease: A practical guide for clinicians","volume":"30","author":"Srinivasan","year":"2024","journal-title":"World J. Gastroenterol."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Tong, Y., Lu, K., Yang, Y., Li, J., Lin, Y., Wu, D., Yang, A., Li, Y., Yu, S., and Qian, J. (2020). Can natural language processing help differentiate inflammatory intestinal diseases in China? Models applying random forest and convolutional neural network approaches. BMC Med. Inform. Decis. Mak., 20.","DOI":"10.1186\/s12911-020-01277-w"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1046\/j.1443-1661.2003.00246.x","article-title":"Computer-aided grading system for endoscopic severity in patients with ulcerative colitis","volume":"15","author":"Sasaki","year":"2003","journal-title":"Dig. Endosc."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/j.gie.2018.10.020","article-title":"Novel computer-assisted diagnosis system for endoscopic disease activity in patients with ulcerative colitis","volume":"89","author":"Ozawa","year":"2019","journal-title":"Gastrointest. Endosc."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2631774521990623","DOI":"10.1177\/2631774521990623","article-title":"Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data","volume":"14","author":"Becker","year":"2021","journal-title":"Ther. Adv. Gastrointest. Endosc."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2150","DOI":"10.1053\/j.gastro.2020.02.012","article-title":"Development and validation of a deep neural network for accurate evaluation of endoscopic images from patients with ulcerative colitis","volume":"158","author":"Takenaka","year":"2020","journal-title":"Gastroenterology"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/S2468-1253(21)00372-1","article-title":"Deep neural network for video colonoscopy of ulcerative colitis: A cross-sectional study","volume":"7","author":"Takenaka","year":"2022","journal-title":"Lancet Gastroenterol. Hepatol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1111\/den.14659","article-title":"Role of ultra-high definition endoscopy (endomicroscopy and endocytoscopy) and real-time histologic examination in inflammatory bowel disease: Scoping review","volume":"36","author":"Pal","year":"2024","journal-title":"Dig. Endosc."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1016\/j.gie.2018.09.024","article-title":"Fully automated diagnostic system with artificial intelligence using endocytoscopy to identify the presence of histologic inflammation associated with ulcerative colitis (with video)","volume":"89","author":"Maeda","year":"2019","journal-title":"Gastrointest. Endosc."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1180","DOI":"10.1053\/j.gastro.2023.02.031","article-title":"Artificial intelligence enabled histological prediction of remission or activity and clinical outcomes in ulcerative colitis","volume":"164","author":"Iacucci","year":"2023","journal-title":"Gastroenterology"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1409","DOI":"10.1093\/ibd\/izac233","article-title":"Computer-aided imaging analysis of probe-based confocal laser endomicroscopy with molecular labeling and gene expression identifies markers of response to biological therapy in ibd patients: The endo-omics study","volume":"29","author":"Iacucci","year":"2023","journal-title":"Inflamm. Bowel Dis."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1080\/21681163.2021.1997644","article-title":"Automatic estimation of ulcerative colitis severity from endoscopy videos using ordinal multi-instance learning","volume":"10","author":"Schwab","year":"2021","journal-title":"Comput. Methods Biomech. Biomed. Eng. Imaging Vis."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"E539","DOI":"10.1055\/a-1790-6201","article-title":"Rapid development of accurate artificial intelligence scoring for colitis disease activity using applied data science techniques","volume":"10","author":"Patel","year":"2022","journal-title":"Endosc. Int. Open"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1016\/j.gie.2018.08.006","article-title":"Quantitative assessment of mucosal architecture using computer-based analysis of confocal laser endomicroscopy in inflammatory bowel diseases","volume":"89","author":"David","year":"2019","journal-title":"Gastrointest. Endosc."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"e193963","DOI":"10.1001\/jamanetworkopen.2019.3963","article-title":"Performance of a deep learning model vs human reviewers in grading endoscopic disease severity of patients with ulcerative colitis","volume":"2","author":"Stidham","year":"2019","journal-title":"JAMA Netw. Open"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"710","DOI":"10.1053\/j.gastro.2020.10.024","article-title":"Central reading of ulcerative colitis clinical trial videos using neural networks","volume":"160","author":"Gottlieb","year":"2021","journal-title":"Gastroenterology"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1097\/MEG.0000000000001952","article-title":"Deep learning enabled classification of Mayo endoscopic subscore in patients with ulcerative colitis","volume":"33","author":"Bhambhvani","year":"2021","journal-title":"Eur. J. Gastroenterol. Hepatol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"11351","DOI":"10.1038\/s41598-023-38206-6","article-title":"Deep learning model for distinguishing Mayo endoscopic subscore 0 and 1 in patients with ulcerative colitis","volume":"13","author":"Kim","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"936","DOI":"10.1007\/s10620-012-2433-8","article-title":"Automated identification of surveillance colonoscopy in inflammatory bowel disease using natural language processing","volume":"58","author":"Hou","year":"2013","journal-title":"Dig. Dis. Sci."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"e13960","DOI":"10.1111\/eci.13960","article-title":"Artificial-intelligence-based decision support tools for the differential diagnosis of colitis","volume":"53","author":"Finkler","year":"2023","journal-title":"Eur. J. Clin. Investig."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Gavrilescu, O., Popa, I.V., Dranga, M., Mihai, R., Prelipcean, C.C., and Mihai, C. (2023). Laboratory data and ibdq-effective predictors for the non-invasive machine-learning-based prediction of endoscopic activity in ulcerative colitis. J. Clin. Med., 12.","DOI":"10.3390\/jcm12113609"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1610","DOI":"10.1111\/jgh.15904","article-title":"The diagnostic ability to classify neoplasias occurring in inflammatory bowel disease by artificial intelligence and endoscopists: A pilot study","volume":"37","author":"Yamamoto","year":"2022","journal-title":"J. Gastroenterol. Hepatol."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"854677","DOI":"10.3389\/fmed.2022.854677","article-title":"Development and validation of a deep neural network for accurate identification of endoscopic images from patients with ulcer-ative colitis and crohn\u2019s disease","volume":"9","author":"Ruan","year":"2022","journal-title":"Front. Med."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"789862","DOI":"10.3389\/fmed.2022.789862","article-title":"Development of a convolutional neural network-based colonoscopy image assessment model for differentiating crohn\u2019s disease and ulcerative colitis","volume":"9","author":"Wang","year":"2022","journal-title":"Front. Med."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1111\/jgh.16430","article-title":"Artificial intelligence for discrimination of Crohn\u2019s disease and gastrointestinal tuberculosis: A systematic review","volume":"39","author":"Sachan","year":"2024","journal-title":"J. Gastroenterol. Hepatol."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Lu, Y., Chen, Y., Peng, X., Yao, J., Zhong, W., Li, C., and Zhi, M. (2021). Development and validation of a new algorithm model for differential diagnosis between Crohn\u2019s disease and intestinal tuberculosis: A combination of laboratory, imaging and endoscopic characteristics. BMC Gastroenterol., 21.","DOI":"10.1186\/s12876-021-01838-x"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Lu, K., Tong, Y., Yu, S., Lin, Y., Yang, Y., Xu, H., Li, Y., and Yu, S. (2023). Building a trustworthy AI differential diagnosis application for Crohn\u2019s disease and intestinal tuberculosis. BMC Med. Inform. Decis. Mak., 23.","DOI":"10.1186\/s12911-023-02257-6"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"105142","DOI":"10.1016\/j.ijmedinf.2023.105142","article-title":"Deep learning-based prediction model for diagnosing gastrointestinal diseases using endoscopy images","volume":"177","author":"Sharma","year":"2023","journal-title":"Int. J. Med. Inform."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"2141","DOI":"10.1111\/jgh.15433","article-title":"Deep-learning system for real-time differentiation between Crohn\u2019s disease, intestinal Beh\u00e7et\u2019s disease, and intestinal tuberculosis","volume":"36","author":"Kim","year":"2021","journal-title":"J. Gastroenterol. Hepatol."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Yao, H., Stidham, R.W., Soroushmehr, R., Gryak, J., and Najarian, K. (2019, January 23\u201327). Automated Detection of Non-Informative Frames for Colonoscopy through a Combination of Deep Learning and Feature Extraction. Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Berlin, Germany.","DOI":"10.1109\/EMBC.2019.8856625"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"2273","DOI":"10.1056\/NEJMoa2201048","article-title":"Treatment of anal high-grade squamous intraepithelial lesions to prevent anal cancer","volume":"386","author":"Palefsky","year":"2022","journal-title":"N. Engl. J. Med."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1097\/LGT.0000000000000505","article-title":"Early detection of anal high-grade squamous intraepithelial lesion: Do we have an impact on progression to invasive anal carcinoma?","volume":"24","author":"Maugin","year":"2020","journal-title":"J. Low. Genit. Tract Dis."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1097\/MOG.0000000000000661","article-title":"The importance of anal cancer screening and high-resolution anoscopy to gastroenterology practice","volume":"36","author":"Morris","year":"2020","journal-title":"Curr. Opin. Gastroenterol."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Albuquerque, A., Rios, E., and Schmitt, F. (2019). Recommendations favoring anal cytology as a method for anal cancer screening: A systematic review. Cancers, 11.","DOI":"10.3390\/cancers11121942"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s10151-019-02001-1","article-title":"Progression of lsil to hsil or scc: Is anoscopy and biopsy good enough?","volume":"23","author":"Mistrangelo","year":"2019","journal-title":"Tech. Coloproctol."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1262","DOI":"10.1093\/cid\/ciy273","article-title":"Performance of anal cytology compared with high-resolution anoscopy and histology in women with lower anogenital tract neoplasia","volume":"67","author":"Albuquerque","year":"2018","journal-title":"Clin. Infect. Dis."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1055\/s-0038-1668103","article-title":"History of high-resolution anoscopy","volume":"31","author":"Cho","year":"2018","journal-title":"Clin. Colon Rectal Surg."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1694","DOI":"10.1002\/ijc.34850","article-title":"International anal neoplasia society\u2019s consensus guidelines for anal cancer screening","volume":"154","author":"Stier","year":"2024","journal-title":"Int. J. Cancer"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1007\/s10151-022-02684-z","article-title":"Artificial intelligence and high-resolution anoscopy: Automatic identification of anal squamous cell carcinoma precursors using a convolutional neural network","volume":"26","author":"Saraiva","year":"2022","journal-title":"Tech. Coloproctol."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"e00681","DOI":"10.14309\/ctg.0000000000000681","article-title":"Deep learning in high-resolution anoscopy: Assessing the impact of staining and therapeutic manipulation on au-tomated detection of anal cancer precursors","volume":"15","author":"Saraiva","year":"2022","journal-title":"Clin. Transl. Gastroenterol."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"e14335","DOI":"10.1111\/nmo.14335","article-title":"Review of the indications, methods, and clinical utility of anorectal manometry and the rectal balloon expulsion test","volume":"34","author":"Bharucha","year":"2022","journal-title":"Neurogastroenterol. Motil."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"e00555","DOI":"10.14309\/ctg.0000000000000555","article-title":"Artificial intelligence and anorectal manometry: Automatic detection and differentiation of anorectal motility patterns\u2014A proof-of-concept study","volume":"14","author":"Saraiva","year":"2022","journal-title":"Clin. Transl. Gastroenterol."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"2015","DOI":"10.1007\/s10620-022-07759-3","article-title":"Video-based deep learning to detect dyssynergic defecation with 3d high-definition anorectal manometry","volume":"68","author":"Levy","year":"2022","journal-title":"Dig. Dis. Sci."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"13","DOI":"10.3393\/ac.2023.00892.0127","article-title":"Performance reporting design in artificial intelligence studies using image-based TNM staging and prognostic parameters in rectal cancer: A systematic review","volume":"40","author":"Kim","year":"2024","journal-title":"Ann. Coloproctol."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"105183","DOI":"10.1016\/j.ebiom.2024.105183","article-title":"Deep learning for colorectal cancer detection in contrast-enhanced CT without bowel preparation: A retrospective, multicentre study","volume":"104","author":"Yao","year":"2024","journal-title":"EBioMedicine"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"1117","DOI":"10.1080\/17474124.2023.2274926","article-title":"Small bowel imaging in inflammatory bowel disease: Updates for 2023","volume":"17","author":"Hameed","year":"2023","journal-title":"Expert Rev. Gastroenterol. Hepatol."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1093\/ibd\/izz196","article-title":"Assessing small bowel stricturing and morphology in crohn\u2019s disease using semi-automated image analysis","volume":"26","author":"Stidham","year":"2020","journal-title":"Inflamm. Bowel Dis."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1016\/j.acra.2017.12.024","article-title":"Semiautomatic assessment of the terminal ileum and colon in patients with crohn disease using mri (the vigor++ project)","volume":"25","author":"Puylaert","year":"2018","journal-title":"Acad. Radiol."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"6446","DOI":"10.1007\/s00464-021-08993-y","article-title":"Development and validation of a deep learning-based algorithm for colonoscopy quality assessment","volume":"36","author":"Chang","year":"2022","journal-title":"Surg. Endosc."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1016\/j.gie.2021.11.041","article-title":"Artificial intelligence for the assessment of bowel preparation","volume":"95","author":"Lee","year":"2022","journal-title":"Gastrointest. Endosc."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"e697","DOI":"10.1016\/S2589-7500(21)00109-6","article-title":"Multi-step validation of a deep learning-based system for the quantification of bowel preparation: A prospective, observational study","volume":"3","author":"Zhou","year":"2021","journal-title":"Lancet Digit. Health"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1055\/a-1706-6174","article-title":"Effect of an artificial intelligence-based quality improvement system on efficacy of a computer-aided detection system in colonoscopy: A four-group parallel study","volume":"54","author":"Yao","year":"2022","journal-title":"Endoscopy"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"972","DOI":"10.1055\/a-1799-8297","article-title":"Artificial intelligence-based assess-ments of colonoscopic withdrawal technique: A new method for measuring and enhancing the quality of fold examination","volume":"54","author":"Liu","year":"2022","journal-title":"Endoscopy"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"e436","DOI":"10.1016\/S2589-7500(22)00042-5","article-title":"Cost-effectiveness of artificial intelligence for screening colonoscopy: A modelling study","volume":"4","author":"Areia","year":"2022","journal-title":"Lancet Digit. Health"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"AB241","DOI":"10.1016\/j.gie.2022.04.636","article-title":"Impact of artificial intelligence on colonoscopy surveillance after polyp removal: A pooled analysis of randomized trials","volume":"95","author":"Mori","year":"2022","journal-title":"Gastrointest. Endosc."},{"key":"ref_95","first-page":"114","article-title":"Current and future implications of artificial intelligence in colonoscopy","volume":"36","author":"Antonelli","year":"2023","journal-title":"Ann. Gastroenterol."}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/13\/19\/5842\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:07:23Z","timestamp":1760112443000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/13\/19\/5842"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,30]]},"references-count":95,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["jcm13195842"],"URL":"https:\/\/doi.org\/10.3390\/jcm13195842","relation":{},"ISSN":["2077-0383"],"issn-type":[{"type":"electronic","value":"2077-0383"}],"subject":[],"published":{"date-parts":[[2024,9,30]]}}}