{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T09:38:12Z","timestamp":1768988292325,"version":"3.49.0"},"reference-count":21,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,1,15]],"date-time":"2023-01-15T00:00:00Z","timestamp":1673740800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FUNDA\u00c7\u00c3O PARA A CI\u00caNCIA E TECNOLOGIA (FCT)","award":["CPCA\/A0\/7363\/2020"],"award-info":[{"award-number":["CPCA\/A0\/7363\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Medicina"],"abstract":"<jats:p>Background and Objectives: Device-assisted enteroscopy (DAE) has a significant role in approaching enteric lesions. Endoscopic observation of ulcers or erosions is frequent and can be associated with many nosological entities, namely Crohn\u2019s disease. Although the application of artificial intelligence (AI) is growing exponentially in various imaged-based gastroenterology procedures, there is still a lack of evidence of the AI technical feasibility and clinical applicability of DAE. This study aimed to develop and test a multi-brand convolutional neural network (CNN)-based algorithm for automatically detecting ulcers and erosions in DAE. Materials and Methods: A unicentric retrospective study was conducted for the development of a CNN, based on a total of 250 DAE exams. A total of 6772 images were used, of which 678 were considered ulcers or erosions after double-validation. Data were divided into a training and a validation set, the latter being used for the performance assessment of the model. Our primary outcome measures were sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and an area under the curve precision\u2013recall curve (AUC-PR). Results: Sensitivity, specificity, PPV, and NPV were respectively 88.5%, 99.7%, 96.4%, and 98.9%. The algorithm\u2019s accuracy was 98.7%. The AUC-PR was 1.00. The CNN processed 293.6 frames per second, enabling AI live application in a real-life clinical setting in DAE. Conclusion: To the best of our knowledge, this is the first study regarding the automatic multi-brand panendoscopic detection of ulcers and erosions throughout the digestive tract during DAE, overcoming a relevant interoperability challenge. Our results highlight that using a CNN to detect this type of lesion is associated with high overall accuracy. The development of binary CNN for automatically detecting clinically relevant endoscopic findings and assessing endoscopic inflammatory activity are relevant steps toward AI application in digestive endoscopy, particularly for panendoscopic evaluation.<\/jats:p>","DOI":"10.3390\/medicina59010172","type":"journal-article","created":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T02:29:55Z","timestamp":1673836195000},"page":"172","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Deep-Learning and Device-Assisted Enteroscopy: Automatic Panendoscopic Detection of Ulcers and Erosions"],"prefix":"10.3390","volume":"59","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0484-4804","authenticated-orcid":false,"given":"Miguel","family":"Martins","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, 4200-427 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, 4200-427 Porto, Portugal"},{"name":"Faculty of Medicine of University of Porto, 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, 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, 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, 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, 4200-427 Porto, Portugal"}]},{"given":"H\u00e9lder","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, 4200-427 Porto, Portugal"},{"name":"Faculty of Medicine of 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, 4200-427 Porto, Portugal"},{"name":"Faculty of Medicine of 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 of the University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal"},{"name":"DigestAID\u2014Digestive Artificial Intelligence Development, Rua Alfredo Allen, 455\/461, 4200-135 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, 4200-427 Porto, Portugal"},{"name":"Faculty of Medicine of University of Porto, Alameda 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Gastroenterol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1381","DOI":"10.1016\/j.cgh.2012.08.035","article-title":"Factors associated with positive findings from capsule endoscopy in patients with obscure gastrointestinal bleeding","volume":"10","author":"Shahidi","year":"2012","journal-title":"Clin. Gastroenterol. Hepatol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1038\/35013140","article-title":"Wireless capsule endoscopy","volume":"405","author":"Iddan","year":"2000","journal-title":"Nature"},{"key":"ref_4","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_5","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\u2014Update 2022","volume":"55","author":"Pennazio","year":"2023","journal-title":"Endoscopy"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1055\/a-0576-0566","article-title":"Small-bowel capsule endoscopy and device-assisted enteroscopy for diagnosis and treatment of small-bowel disorders: European Society of Gastrointestinal Endoscopy (ESGE) Technical Review","volume":"50","author":"Rondonotti","year":"2018","journal-title":"Endoscopy"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Marlicz, W., Koulaouzidis, G., and Koulaouzidis, A. (2020). Artificial Intelligence in Gastroenterology-Walking into the Room of Little Miracles. J. Clin. Med., 9.","DOI":"10.3390\/jcm9113675"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.gie.2021.09.017","article-title":"Real-time artificial intelligence for detecting focal lesions and diagnosing neoplasms of the stomach by white-light endoscopy (with videos)","volume":"95","author":"Wu","year":"2022","journal-title":"Gastrointest. Endosc."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1053\/j.gastro.2019.08.058","article-title":"Application of Artificial Intelligence to Gastroenterology and Hepatology","volume":"158","author":"Sandborn","year":"2020","journal-title":"Gastroenterology"},{"key":"ref_11","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_12","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_13","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: Application do AI for detection of protruding lesions in enteroscopy","volume":"13","author":"Cardoso","year":"2022","journal-title":"Clin. Transl. Gastroenterol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1111\/j.1365-2036.2007.03556.x","article-title":"Development of a capsule endoscopy scoring index for small bowel mucosal inflammatory change","volume":"27","author":"Gralnek","year":"2008","journal-title":"Aliment. Pharmacol. Ther."},{"key":"ref_15","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019, January 8\u201314). PyTorch: An Imperative Style, High-Performance Deep Learning Library. Proceedings of the Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, Vancouver, BC, Canada."},{"key":"ref_16","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1002\/bimj.201800148","article-title":"Tuning model parameters in class-imbalanced learning with precision-recall curve","volume":"61","author":"Fu","year":"2019","journal-title":"Biom. J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"662","DOI":"10.1016\/j.gie.2020.09.018","article-title":"Real-time artificial intelligence-based histologic classification of colorectal polyps with augmented visualization","volume":"93","author":"Baffy","year":"2021","journal-title":"Gastrointest. Endosc."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1007\/s11517-021-02486-9","article-title":"Automated detection of ulcers and erosions in capsule endoscopy images using a convolutional neural network","volume":"60","author":"Afonso","year":"2022","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2282","DOI":"10.1111\/jgh.16011","article-title":"Artificial intelligence and colon capsule endoscopy: Automatic detection of ulcers and erosions using a convolutional neural network","volume":"37","author":"Ribeiro","year":"2022","journal-title":"J. Gastroenterol. Hepatol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1016\/j.gie.2011.03.1239","article-title":"Indications, detectability, positive findings, total enteroscopy, and complications of diagnostic double-balloon endoscopy: A systematic review of data over the first decade of use","volume":"74","author":"Xin","year":"2011","journal-title":"Gastrointest. Endosc."}],"container-title":["Medicina"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1648-9144\/59\/1\/172\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:06:27Z","timestamp":1760119587000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1648-9144\/59\/1\/172"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,15]]},"references-count":21,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["medicina59010172"],"URL":"https:\/\/doi.org\/10.3390\/medicina59010172","relation":{},"ISSN":["1648-9144"],"issn-type":[{"value":"1648-9144","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,15]]}}}