{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T22:29:46Z","timestamp":1793312986814,"version":"4.5.0"},"reference-count":15,"publisher":"Georg Thieme Verlag KG","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Endoscopy"],"published-print":{"date-parts":[[2022,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>\n          Background\u2002For eosinophilic esophagitis (EoE), a substantial diagnostic delay is still a clinically relevant phenomenon. Deep learning-based algorithms have demonstrated potential in medical image analysis. Here we establish a convolutional neuronal network (CNN)-based approach that can distinguish the appearance of EoE from normal findings and candida esophagitis.<\/jats:p><jats:p>\n          Methods\u2002We trained and tested a CNN using 484 real-world endoscopic images from 134 subjects consisting of three classes (normal, EoE, and candidiasis). Images were split into two completely independent datasets. The proposed approach was evaluated against three trainee endoscopists using the test set. Model-explainability was enhanced by deep Taylor decomposition.<\/jats:p><jats:p>\n          Results\u2002Global accuracy (0.915 [95\u200a% confidence interval (CI) 0.880\u20130.940]), sensitivity (0.871 [95\u200a%CI 0.819\u20130.910]), and specificity (0.936 [95\u200a%CI 0.910\u20130.955]) were significantly higher than for the endoscopists on the test set. Global area under the receiver operating characteristic curve was 0.966 [95\u200a%CI 0.954\u20130.975]. Results were highly reproducible. Explainability analysis found that the algorithm identified the characteristic signs also used by endoscopists.<\/jats:p><jats:p>\n          Conclusions\u2002Complex endoscopic classification tasks including more than two classes can be solved by CNN-based algorithms. Therefore, our algorithm may assist clinicians in making the diagnosis of EoE.<\/jats:p>","DOI":"10.1055\/a-1520-8116","type":"journal-article","created":{"date-parts":[[2021,5,31]],"date-time":"2021-05-31T23:25:17Z","timestamp":1622503517000},"page":"299-304","source":"Crossref","is-referenced-by-count":21,"title":["Deep learning-based detection of eosinophilic esophagitis"],"prefix":"10.1055","volume":"54","author":[{"given":"Pedro","family":"Guimar\u00e3es","sequence":"additional","affiliation":[{"name":"Department of Clinical Bioinformatics, Saarland University, Saarbr\u00fccken, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Keller","sequence":"additional","affiliation":[{"name":"Department of Clinical Bioinformatics, Saarland University, Saarbr\u00fccken, Germany"},{"name":"Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford University, Stanford, California, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tobias","family":"Fehlmann","sequence":"additional","affiliation":[{"name":"Department of Clinical Bioinformatics, Saarland University, Saarbr\u00fccken, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frank","family":"Lammert","sequence":"additional","affiliation":[{"name":"Department of Medicine II, Saarland University Medical Center, Saarland University, Homburg, Germany"},{"name":"Hannover Health Sciences Campus, Hannover Medical School, Hannover, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1146-288X","authenticated-orcid":false,"given":"Markus","family":"Casper","sequence":"additional","affiliation":[{"name":"Department of Medicine II, Saarland University Medical Center, Saarland University, Homburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"194","published-online":{"date-parts":[[2021,8,4]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"1022","DOI":"10.1053\/j.gastro.2018.07.009","article-title":"Updated International Consensus Diagnostic Criteria for Eosinophilic Esophagitis: Proceedings of the AGREE Conference","volume":"155","author":"E S Dellon","year":"2018","journal-title":"Gastroenterology"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1136\/gutjnl-2011-301817","article-title":"Endoscopic assessment of the oesophageal features of eosinophilic oesophagitis: validation of a novel classification and grading system","volume":"62","author":"I Hirano","year":"2013","journal-title":"Gut"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.cgh.2015.08.040","article-title":"Accuracy of the Eosinophilic Esophagitis Endoscopic Reference Score in diagnosis and determining 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Gastroenterol"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1159\/000357009","article-title":"Diagnostics of eosinophilic esophagitis: clinical, endoscopic, and histologic pitfalls","volume":"32","author":"E S Dellon","year":"2014","journal-title":"Dig Dis"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"1428","DOI":"10.1007\/s10620-018-4972-0","article-title":"Management of esophageal food impaction varies among gastroenterologists and affects identification of eosinophilic esophagitis","volume":"63","author":"G Hiremath","year":"2018","journal-title":"Dig Dis 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