{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:35:48Z","timestamp":1778603748629,"version":"3.51.4"},"reference-count":96,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,1,29]],"date-time":"2024-01-29T00:00:00Z","timestamp":1706486400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>The role of capsule endoscopy and enteroscopy in managing various small-bowel pathologies is well-established. However, their broader application has been hampered mainly by their lengthy reading times. As a result, there is a growing interest in employing artificial intelligence (AI) in these diagnostic and therapeutic procedures, driven by the prospect of overcoming some major limitations and enhancing healthcare efficiency, while maintaining high accuracy levels. In the past two decades, the applicability of AI to gastroenterology has been increasing, mainly because of the strong imaging component. Nowadays, there are a multitude of studies using AI, specifically using convolutional neural networks, that prove the potential applications of AI to these endoscopic techniques, achieving remarkable results. These findings suggest that there is ample opportunity for AI to expand its presence in the management of gastroenterology diseases and, in the future, catalyze a game-changing transformation in clinical activities. This review provides an overview of the current state-of-the-art of AI in the scope of small-bowel study, with a particular focus on capsule endoscopy and enteroscopy.<\/jats:p>","DOI":"10.3390\/diagnostics14030291","type":"journal-article","created":{"date-parts":[[2024,1,29]],"date-time":"2024-01-29T08:54:03Z","timestamp":1706518443000},"page":"291","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["From Data to Insights: How Is AI Revolutionizing Small-Bowel Endoscopy?"],"prefix":"10.3390","volume":"14","author":[{"given":"Joana","family":"Mota","sequence":"first","affiliation":[{"name":"Precision Medicine Unit, Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"},{"name":"WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"}]},{"given":"Maria Jo\u00e3o","family":"Almeida","sequence":"additional","affiliation":[{"name":"Precision Medicine Unit, Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"},{"name":"WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 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, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"},{"name":"WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"}]},{"given":"Miguel","family":"Martins","sequence":"additional","affiliation":[{"name":"Precision Medicine Unit, Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"},{"name":"WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"}]},{"given":"Tiago","family":"Ribeiro","sequence":"additional","affiliation":[{"name":"Precision Medicine Unit, Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"},{"name":"WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"}]},{"given":"Jo\u00e3o","family":"Afonso","sequence":"additional","affiliation":[{"name":"Precision Medicine Unit, Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"},{"name":"WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9427-5635","authenticated-orcid":false,"given":"Pedro","family":"Cardoso","sequence":"additional","affiliation":[{"name":"Precision Medicine Unit, Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"},{"name":"WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"}]},{"given":"Helder","family":"Cardoso","sequence":"additional","affiliation":[{"name":"Precision Medicine Unit, Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"},{"name":"WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"},{"name":"Faculty of Medicine, University of Porto, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"}]},{"given":"Patr\u00edcia","family":"Andrade","sequence":"additional","affiliation":[{"name":"Precision Medicine Unit, Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"},{"name":"WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"},{"name":"Faculty of Medicine, University of Porto, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"}]},{"given":"Jo\u00e3o","family":"Ferreira","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Faculty of Engineering, University of Porto, R. Dr. Roberto Frias, 4200-465 Porto, Portugal"},{"name":"Digestive Artificial Intelligence Development, R. Alfredo Allen 455-461, 4200-135 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, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"},{"name":"WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"},{"name":"Faculty of Medicine, University of Porto, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"},{"name":"ManopH Gastroenterology Clinic, R. de S\u00e1 da Bandeira 752, 4000-432 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, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"},{"name":"WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"},{"name":"Faculty of Medicine, University of Porto, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5812499","DOI":"10.1155\/2021\/5812499","article-title":"Influential Usage of Big Data and Artificial Intelligence in Healthcare","volume":"2021","author":"Yang","year":"2021","journal-title":"Comput. Math. Methods Med."},{"key":"ref_2","first-page":"300","article-title":"Artificial intelligence and capsule endoscopy: Unravelling the future","volume":"34","author":"Mascarenhas","year":"2021","journal-title":"Ann. Gastroenterol."},{"key":"ref_3","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":"Frontline Gastroenterol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1016\/j.vgie.2020.08.013","article-title":"Artificial intelligence in gastrointestinal endoscopy","volume":"5","author":"Pannala","year":"2020","journal-title":"VideoGIE"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1553","DOI":"10.1007\/s10620-021-07086-z","article-title":"Artificial Intelligence in Endoscopy","volume":"67","author":"Okagawa","year":"2022","journal-title":"Dig. Dis. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3","DOI":"10.5217\/ir.2022.00108","article-title":"Use of device-assisted enteroscopy in small bowel disease: An expert consensus statement by the Korean Association for the Study of Intestinal Diseases","volume":"21","author":"Lee","year":"2023","journal-title":"Intest. Res."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Cortegoso Valdivia, P., Skonieczna-Zydecka, K., Elosua, A., Sciberras, M., Piccirelli, S., Rullan, M., Tabone, T., Gawel, K., Stachowski, A., and Leminski, A. (2022). Indications, Detection, Completion and Retention Rates of Capsule Endoscopy in Two Decades of Use: A Systematic Review and Meta-Analysis. Diagnostics, 12.","DOI":"10.3390\/diagnostics12051105"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ciaccio, E.J., Tennyson, C.A., Bhagat, G., Lewis, S.K., and Green, P.H. (2010). Classification of videocapsule endoscopy image patterns: Comparative analysis between patients with celiac disease and normal individuals. Biomed. Eng. Online, 9.","DOI":"10.1186\/1475-925X-9-44"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"E1361","DOI":"10.1055\/a-1507-4980","article-title":"A deep learning framework for autonomous detection and classification of Crohn\u2019s disease lesions in the small bowel and colon with capsule endoscopy","volume":"9","author":"Majtner","year":"2021","journal-title":"Endosc. Int. Open"},{"key":"ref_10","unstructured":"Mascarenhas, M., Cardoso, H., and Macedo, G. (2023). Artificial Intelligence in Capsule Endoscopy: A Gamechanger for a Groundbreaking Technique, Elsevier."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"16253","DOI":"10.20452\/pamw.16253","article-title":"Obscure-overt gastrointestinal bleeding: A review","volume":"132","author":"Awadie","year":"2022","journal-title":"Pol. Arch. Intern. Med."},{"key":"ref_12","first-page":"e27137","article-title":"Obscure Gastrointestinal Bleeding and Capsule Endoscopy: A Win-Win Situation or Not?","volume":"14","author":"Patel","year":"2022","journal-title":"Cureus"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.giec.2016.08.012","article-title":"Gastrointestinal Angiodysplasia: Diagnosis and Management","volume":"27","author":"Jackson","year":"2017","journal-title":"Gastrointest. Endosc. Clin. N. Am."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1016\/j.dld.2021.01.025","article-title":"Performance of a convolutional neural network for automatic detection of blood and hematic residues in small bowel lumen","volume":"53","author":"Afonso","year":"2021","journal-title":"Dig. Liver Dis."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1477","DOI":"10.1007\/s10916-009-9424-0","article-title":"Bleeding detection in Wireless Capsule Endoscopy based on Probabilistic Neural Network","volume":"35","author":"Pan","year":"2011","journal-title":"J. Med. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"636","DOI":"10.1109\/JBHI.2013.2257819","article-title":"Computer-aided bleeding detection in WCE video","volume":"18","author":"Fu","year":"2014","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"ref_17","first-page":"639","article-title":"A deep convolutional neural network for bleeding detection in Wireless Capsule Endoscopy images","volume":"2016","author":"Xiao","year":"2016","journal-title":"Annu. Int. Conf. IEEE Eng. Med. Biol. Soc."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Fan, S., Xu, L., Fan, Y., Wei, K., and Li, L. (2018). Computer-aided detection of small intestinal ulcer and erosion in wireless capsule endoscopy images. Phys. Med. Biol., 63.","DOI":"10.1088\/1361-6560\/aad51c"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.gie.2018.10.027","article-title":"Automatic detection of erosions and ulcerations in wireless capsule endoscopy images based on a deep convolutional neural network","volume":"89","author":"Aoki","year":"2019","journal-title":"Gastrointest. Endosc."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, S., Xing, Y., Zhang, L., Gao, H., and Zhang, H. (2019). A systematic evaluation and optimization of automatic detection of ulcers in wireless capsule endoscopy on a large dataset using deep convolutional neural networks. Phys. Med. Biol., 64.","DOI":"10.1088\/1361-6560\/ab5086"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1111\/den.13517","article-title":"Clinical usefulness of a deep learning-based system as the first screening on small-bowel capsule endoscopy reading","volume":"32","author":"Aoki","year":"2020","journal-title":"Dig. Endosc."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1111\/jgh.14941","article-title":"Automatic detection of blood content in capsule endoscopy images based on a deep convolutional neural network","volume":"35","author":"Aoki","year":"2020","journal-title":"J. Gastroenterol. Hepatol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1007\/s10278-021-00428-3","article-title":"Deep Transfer Learning for Automated Intestinal Bleeding Detection in Capsule Endoscopy Imaging","volume":"34","author":"Ghosh","year":"2021","journal-title":"J. Digit. Imaging"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Mascarenhas Saraiva, M.J., Afonso, J., Ribeiro, T., Ferreira, J., Cardoso, H., Andrade, A.P., Parente, M., Natal, R., Mascarenhas Saraiva, M., and Macedo, G. (2021). Deep learning and capsule endoscopy: Automatic identification and differentiation of small bowel lesions with distinct haemorrhagic potential using a convolutional neural network. BMJ Open Gastroenterol., 8.","DOI":"10.1136\/bmjgast-2021-000753"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1446","DOI":"10.1007\/s10439-019-02248-7","article-title":"Automatic Segmentation and Detection of Small Bowel Angioectasias in WCE Images","volume":"47","author":"Vieira","year":"2019","journal-title":"Ann. Biomed. Eng."},{"key":"ref_26","first-page":"1184","article-title":"Segmentation of angiodysplasia lesions in WCE images using a MAP approach with Markov Random Fields","volume":"2016","author":"Vieira","year":"2016","journal-title":"Annu. Int. Conf. IEEE Eng. Med. Biol. Soc."},{"key":"ref_27","first-page":"3158","article-title":"Automated angiodysplasia detection from wireless capsule endoscopy","volume":"2017","author":"Noya","year":"2017","journal-title":"Annu. Int. Conf. IEEE Eng. Med. Biol. Soc."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.gie.2018.06.036","article-title":"A neural network algorithm for detection of GI angiectasia during small-bowel capsule endoscopy","volume":"89","author":"Leenhardt","year":"2019","journal-title":"Gastrointest. Endosc."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1111\/den.13507","article-title":"Artificial intelligence using a convolutional neural network for automatic detection of small-bowel angioectasia in capsule endoscopy images","volume":"32","author":"Tsuboi","year":"2020","journal-title":"Dig. Endosc."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"879","DOI":"10.3748\/wjg.v29.i5.879","article-title":"Convolutional neural network-based segmentation network applied to image recognition of angiodysplasias lesion under capsule endoscopy","volume":"29","author":"Chu","year":"2023","journal-title":"World J. Gastroenterol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"13","DOI":"10.4253\/wjge.v7.i1.13","article-title":"Small bowel capsule endoscopy: Where are we after almost 15 years of use?","volume":"7","author":"Hindryckx","year":"2015","journal-title":"World J. Gastrointest. Endosc."},{"key":"ref_32","first-page":"75","article-title":"Artificial intelligence and capsule endoscopy: Automatic detection of enteric protruding lesions using a convolutional neural network","volume":"115","author":"Afonso","year":"2023","journal-title":"Rev. Esp. Enferm. Dig."},{"key":"ref_33","first-page":"3012","article-title":"Detection of small bowel tumors in capsule endoscopy frames using texture analysis based on the discrete wavelet transform","volume":"2008","author":"Barbosa","year":"2008","journal-title":"Annu. Int. Conf. IEEE Eng. Med. Biol. Soc."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Barbosa, D.C., Roupar, D.B., Ramos, J.C., Tavares, A.C., and Lima, C.S. (2012). Automatic small bowel tumor diagnosis by using multi-scale wavelet-based analysis in wireless capsule endoscopy images. Biomed. Eng. Online, 11.","DOI":"10.1186\/1475-925X-11-3"},{"key":"ref_35","first-page":"3731","article-title":"A comparative study of shape features for polyp detection in wireless capsule endoscopy images","volume":"2009","author":"Li","year":"2009","journal-title":"Annu. Int. Conf. IEEE Eng. Med. Biol. Soc."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2463","DOI":"10.1007\/s10916-011-9713-2","article-title":"Comparison of several texture features for tumor detection in CE images","volume":"36","author":"Li","year":"2012","journal-title":"J. Med. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1109\/TITB.2012.2185807","article-title":"Tumor recognition in wireless capsule endoscopy images using textural features and SVM-based feature selection","volume":"16","author":"Li","year":"2012","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1002\/mp.13709","article-title":"Automatic detection of small bowel tumors in wireless capsule endoscopy images using ensemble learning","volume":"47","author":"Vieira","year":"2020","journal-title":"Med. Phys."},{"key":"ref_39","first-page":"3025","article-title":"Automatic detection of small bowel tumors in endoscopic capsule images by ROI selection based on discarded lightness information","volume":"2015","author":"Vieira","year":"2015","journal-title":"Annu. Int. Conf. IEEE Eng. Med. Biol. Soc."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.1002\/mp.12147","article-title":"Deep learning for polyp recognition in wireless capsule endoscopy images","volume":"44","author":"Yuan","year":"2017","journal-title":"Med. Phys."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.gie.2020.01.054","article-title":"Automatic detection and classification of protruding lesions in wireless capsule endoscopy images based on a deep convolutional neural network","volume":"92","author":"Saito","year":"2020","journal-title":"Gastrointest. Endosc."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1111\/den.13787","article-title":"Improved classification and localization approach to small bowel capsule endoscopy using convolutional neural network","volume":"33","author":"Hwang","year":"2021","journal-title":"Dig. Endosc."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1044","DOI":"10.1053\/j.gastro.2019.06.025","article-title":"Gastroenterologist-Level Identification of Small-Bowel Diseases and Normal Variants by Capsule Endoscopy Using a Deep-Learning Model","volume":"157","author":"Ding","year":"2019","journal-title":"Gastroenterology"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1055\/a-1167-8157","article-title":"Automatic detection of different types of small-bowel lesions on capsule endoscopy images using a newly developed deep convolutional neural network","volume":"52","author":"Otani","year":"2020","journal-title":"Endoscopy"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.gie.2020.04.080","article-title":"Automatic detection of various abnormalities in capsule endoscopy videos by a deep learning-based system: A multicenter study","volume":"93","author":"Aoki","year":"2021","journal-title":"Gastrointest. Endosc."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"102141","DOI":"10.1016\/j.artmed.2021.102141","article-title":"Multi-pathology detection and lesion localization in WCE videos by using the instance segmentation approach","volume":"119","author":"Vieira","year":"2021","journal-title":"Artif. Intell. Med."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3090341","DOI":"10.1155\/2018\/3090341","article-title":"Organic Boundary Location Based on Color-Texture of Visual Perception in Wireless Capsule Endoscopy Video","volume":"2018","author":"Wang","year":"2018","journal-title":"J. Healthc. Eng."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"154","DOI":"10.3748\/wjg.v28.i1.154","article-title":"Current guidelines for the management of celiac disease: A systematic review with comparative analysis","volume":"28","author":"Raiteri","year":"2022","journal-title":"World J. Gastroenterol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compbiomed.2017.03.031","article-title":"Quantitative analysis of patients with celiac disease by video capsule endoscopy: A deep learning method","volume":"85","author":"Zhou","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.future.2018.07.044","article-title":"Automated diagnosis of celiac disease using DWT and nonlinear features with video capsule endoscopy images","volume":"90","author":"Koh","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Wang, X., Qian, H., Ciaccio, E.J., Lewis, S.K., Bhagat, G., Green, P.H., Xu, S., Huang, L., Gao, R., and Liu, Y. (2020). Celiac disease diagnosis from videocapsule endoscopy images with residual learning and deep feature extraction. Comput. Methods Programs Biomed., 187.","DOI":"10.1016\/j.cmpb.2019.105236"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"4071","DOI":"10.1038\/s41598-022-07199-z","article-title":"Automated detection of celiac disease using Machine Learning Algorithms","volume":"12","author":"Stoleru","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_53","first-page":"1455","article-title":"Quantification of Celiac Disease Severity Using Video Capsule Endoscopy: A Comparison of Human Experts and Machine Learning Algorithms","volume":"19","author":"McAlindon","year":"2023","journal-title":"Curr. Med. Imaging"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"184","DOI":"10.4253\/wjge.v10.i9.184","article-title":"Capsule endoscopy: Current status and role in Crohn\u2019s disease","volume":"10","author":"Goran","year":"2018","journal-title":"World J. Gastrointest. Endosc."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"s1","DOI":"10.1136\/gutjnl-2019-318484","article-title":"British Society of Gastroenterology consensus guidelines on the management of inflammatory bowel disease in adults","volume":"68","author":"Lamb","year":"2019","journal-title":"Gut"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1016\/j.gie.2019.11.012","article-title":"Deep learning algorithms for automated detection of Crohn\u2019s disease ulcers by video capsule endoscopy","volume":"91","author":"Klang","year":"2020","journal-title":"Gastrointest. Endosc."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"165","DOI":"10.5217\/ir.2021.00079","article-title":"Artificial intelligence for endoscopy in inflammatory bowel disease","volume":"20","author":"Takenaka","year":"2022","journal-title":"Intest. Res."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.gie.2020.05.066","article-title":"Ulcer severity grading in video capsule images of patients with Crohn\u2019s disease: An ordinal neural network solution","volume":"93","author":"Barash","year":"2021","journal-title":"Gastrointest. Endosc."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1093\/ecco-jcc\/jjaa234","article-title":"Automated Detection of Crohn\u2019s Disease Intestinal Strictures on Capsule Endoscopy Images Using Deep Neural Networks","volume":"15","author":"Klang","year":"2021","journal-title":"J. Crohns Colitis"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"17706","DOI":"10.1038\/s41598-020-74668-8","article-title":"Automatic evaluation of degree of cleanliness in capsule endoscopy based on a novel CNN architecture","volume":"10","author":"Noorda","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"e32883","DOI":"10.1097\/MD.0000000000032883","article-title":"Clean mucosal area detection of gastroenterologists versus artificial intelligence in small bowel capsule endoscopy","volume":"102","author":"Ju","year":"2023","journal-title":"Medicine"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"E802","DOI":"10.1055\/a-1372-4051","article-title":"Scoring systems in clinical small-bowel capsule endoscopy: All you need to know!","volume":"9","author":"Rosa","year":"2021","journal-title":"Endosc. Int. Open"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1055\/s-0030-1256228","article-title":"Description of a novel grading system to assess the quality of bowel preparation in video capsule endoscopy","volume":"43","author":"Mulder","year":"2011","journal-title":"Endoscopy"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"709","DOI":"10.17235\/reed.2016.4366\/2016","article-title":"Validation of the computed assessment of cleansing score with the Mirocam\u00ae system","volume":"108","author":"Ponte","year":"2016","journal-title":"Rev. Esp. Enferm. Dig."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"E646","DOI":"10.1055\/a-0581-8758","article-title":"Development and validation of a computed assessment of cleansing score for evaluation of quality of small-bowel visualization in capsule endoscopy","volume":"6","author":"Histace","year":"2018","journal-title":"Endosc. Int. Open"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"E944","DOI":"10.1055\/a-0918-5883","article-title":"Multi-criterion, automated, high-performance, rapid tool for assessing mucosal visualization quality of still images in small bowel capsule endoscopy","volume":"7","author":"Oumrani","year":"2019","journal-title":"Endosc. Int. Open"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"932","DOI":"10.1055\/a-1301-3841","article-title":"A neural network-based algorithm for assessing the cleanliness of small bowel during capsule endoscopy","volume":"53","author":"Leenhardt","year":"2021","journal-title":"Endoscopy"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"4417","DOI":"10.1038\/s41598-021-81686-7","article-title":"Development of a deep learning-based software for calculating cleansing score in small bowel capsule endoscopy","volume":"11","author":"Nam","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Ju, J.W., Jung, H., Lee, Y.J., Mun, S.W., and Lee, J.H. (2022). Semantic Segmentation Dataset for AI-Based Quantification of Clean Mucosa in Capsule Endoscopy. Medicina, 58.","DOI":"10.3390\/medicina58030397"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Ribeiro, T., Mascarenhas Saraiva, M.J., Afonso, J., Cardoso, P., Mendes, F., Martins, M., Andrade, A.P., Cardoso, H., Mascarenhas Saraiva, M., and Ferreira, J. (2023). Design of a Convolutional Neural Network as a Deep Learning Tool for the Automatic Classification of Small-Bowel Cleansing in Capsule Endoscopy. Medicina, 59.","DOI":"10.3390\/medicina59040810"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Houdeville, C., Leenhardt, R., Souchaud, M., Velut, G., Carbonell, N., Nion-Larmurier, I., Nuzzo, A., Histace, A., Marteau, P., and Dray, X. (2022). Evaluation by a Machine Learning System of Two Preparations for Small Bowel Capsule Endoscopy: The BUBS (Burst Unpleasant Bubbles with Simethicone) Study. J. Clin. Med., 11.","DOI":"10.3390\/jcm11102822"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1741","DOI":"10.1109\/TMI.2016.2527736","article-title":"Automatic Hookworm Detection in Wireless Capsule Endoscopy Images","volume":"35","author":"Wu","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_73","first-page":"5682288","article-title":"Automatic Detection of Small Intestinal Hookworms in Capsule Endoscopy Images Based on a Convolutional Neural Network","volume":"2021","author":"Gan","year":"2021","journal-title":"Gastroenterol. Res. Pr."},{"key":"ref_74","first-page":"161","article-title":"Anisotropic feature extraction from endoluminal images for detection of intestinal contractions","volume":"9","author":"Spyridonos","year":"2006","journal-title":"Med. Image Comput. Comput. Assist. Interv."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.1053\/j.gastro.2008.06.084","article-title":"New insight into intestinal motor function via noninvasive endoluminal image analysis","volume":"135","author":"Malagelada","year":"2008","journal-title":"Gastroenterology"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"796","DOI":"10.1111\/j.1440-1746.2010.06530.x","article-title":"Double balloon enteroscopy and capsule endoscopy for obscure gastrointestinal bleeding: An updated meta-analysis","volume":"26","author":"Teshima","year":"2011","journal-title":"J. Gastroenterol. Hepatol."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1055\/a-1973-3796","article-title":"Small-bowel capsule endoscopy and device-assisted enteroscopy for diagnosis and treatment of small-bowel disorders: European Society of Gastrointestinal Endoscopy (ESGE) Guideline-Update 2022","volume":"55","author":"Pennazio","year":"2023","journal-title":"Endoscopy"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1111\/j.1572-0241.2006.00664.x","article-title":"Diagnostic yield and therapeutic impact of double-balloon enteroscopy in a large cohort of patients with obscure gastrointestinal bleeding","volume":"101","author":"Sun","year":"2006","journal-title":"Am. J. Gastroenterol."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"2720","DOI":"10.3748\/wjg.v25.i22.2720","article-title":"Diagnosis and therapeutic strategies for small bowel vascular lesions","volume":"25","author":"Sakai","year":"2019","journal-title":"World J. Gastroenterol."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Mascarenhas Saraiva, M., Ribeiro, T., Afonso, J., Andrade, P., Cardoso, P., Ferreira, J., Cardoso, H., and Macedo, G. (2021). Deep Learning and Device-Assisted Enteroscopy: Automatic Detection of Gastrointestinal Angioectasia. Medicina, 57.","DOI":"10.3390\/medicina57121378"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"417","DOI":"10.5946\/ce.2017.142","article-title":"Balloon-Assisted Enteroscopy and Capsule Endoscopy in Suspected Small Bowel Crohn\u2019s Disease","volume":"50","author":"Yen","year":"2017","journal-title":"Clin. Endosc."},{"key":"ref_82","first-page":"1292","article-title":"Clinical usefulness of double balloon enteroscopy in suspected Crohn\u2019s disease: The KASID multi-center trial","volume":"61","author":"Jang","year":"2014","journal-title":"Hepatogastroenterology"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.gie.2014.12.039","article-title":"Double-balloon enteroscopy in Crohn\u2019s disease: Findings and impact on management in a multicenter retrospective study","volume":"82","author":"Rahman","year":"2015","journal-title":"Gastrointest. Endosc."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1055\/s-0029-1214790","article-title":"Role of small-bowel endoscopy in the management of patients with inflammatory bowel disease: An international OMED-ECCO consensus","volume":"41","author":"Bourreille","year":"2009","journal-title":"Endoscopy"},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Martins, M., Mascarenhas, M., Afonso, J., Ribeiro, T., Cardoso, P., Mendes, F., Cardoso, H., Andrade, P., Ferreira, J., and Macedo, G. (2023). Deep-Learning and Device-Assisted Enteroscopy: Automatic Panendoscopic Detection of Ulcers and Erosions. Medicina, 59.","DOI":"10.3390\/medicina59010172"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"e00514","DOI":"10.14309\/ctg.0000000000000514","article-title":"Artificial Intelligence and Device-Assisted Enteroscopy: Automatic Detection of Enteric Protruding Lesions Using a Convolutional Neural Network","volume":"13","author":"Cardoso","year":"2022","journal-title":"Clin. Transl. Gastroenterol."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Mendes, F., Mascarenhas, M., Ribeiro, T., Afonso, J., Cardoso, P., Martins, M., Cardoso, H., Andrade, P., Ferreira, J.P.S., and Mascarenhas Saraiva, M. (2024). Artificial Intelligence and Panendoscopy\u2014Automatic Detection of Clinically Relevant Lesions in Multibrand Device-Assisted Enteroscopy. Cancers, 16.","DOI":"10.3390\/cancers16010208"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"17562848221132683","DOI":"10.1177\/17562848221132683","article-title":"Key research questions for implementation of artificial intelligence in capsule endoscopy","volume":"15","author":"Leenhardt","year":"2022","journal-title":"Ther. Adv. Gastroenterol."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"6039","DOI":"10.1038\/s41467-022-33128-9","article-title":"Technology readiness levels for machine learning systems","volume":"13","author":"Lavin","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1055\/s-0040-1717824","article-title":"Artificial intelligence in small bowel endoscopy: Current perspectives and future directions","volume":"11","author":"Meher","year":"2020","journal-title":"J. Dig. Endosc."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Leenhardt, R., Fernandez-Urien Sainz, I., Rondonotti, E., Toth, E., Van de Bruaene, C., Baltes, P., Rosa, B.J., Triantafyllou, K., Histace, A., and Koulaouzidis, A. (2021). PEACE: Perception and Expectations toward Artificial Intelligence in Capsule Endoscopy. J. Clin. Med., 10.","DOI":"10.3390\/jcm10235708"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.1055\/a-1950-5694","article-title":"Expected value of artificial intelligence in gastrointestinal endoscopy: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement","volume":"54","author":"Messmann","year":"2022","journal-title":"Endoscopy"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1007\/s11894-021-00810-9","article-title":"State of the Art: The Impact of Artificial Intelligence in Endoscopy 2020","volume":"23","author":"Lee","year":"2021","journal-title":"Curr. Gastroenterol. Rep."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Mascarenhas, M., Ribeiro, T., Afonso, J., Mendes, F., Cardoso, P., Martins, M., Ferreira, J., and Macedo, G. (2023). Smart Endoscopy Is Greener Endoscopy: Leveraging Artificial Intelligence and Blockchain Technologies to Drive Sustainability in Digestive Health Care. Diagnostics, 13.","DOI":"10.3390\/diagnostics13243625"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1080\/17474124.2020.1779058","article-title":"Utilizing artificial intelligence in endoscopy: A clinician\u2019s guide","volume":"14","author":"Namikawa","year":"2020","journal-title":"Expert. Rev. Gastroenterol. Hepatol."},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Mascarenhas, M., Afonso, J., Ribeiro, T., Andrade, P., Cardoso, H., and Macedo, G. (2023). The Promise of Artificial Intelligence in Digestive Healthcare and the Bioethics Challenges It Presents. Medicina, 59.","DOI":"10.3390\/medicina59040790"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/3\/291\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:51:07Z","timestamp":1760104267000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/3\/291"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,29]]},"references-count":96,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["diagnostics14030291"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics14030291","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,29]]}}}