{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T02:25:36Z","timestamp":1788143136139,"version":"build-2803163510"},"reference-count":16,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2019,5,1]],"date-time":"2019-05-01T00:00:00Z","timestamp":1556668800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2019,5,1]],"date-time":"2019-05-01T00:00:00Z","timestamp":1556668800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Dutch Cancer Society and Technology Foundation STW","award":["This research is supported by the Dutch Cancer Soc"],"award-info":[{"award-number":["This research is supported by the Dutch Cancer Soc"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["UEG Journal"],"published-print":{"date-parts":[[2019,5]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Computer\u2010aided detection (CAD) systems might assist endoscopists in the recognition of Barrett's neoplasia.<\/jats:p><\/jats:sec><jats:sec><jats:title>Aim<\/jats:title><jats:p>To develop a CAD system using endoscopic images of Barrett's neoplasia.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>White light endoscopy (WLE) overview images of 40 neoplastic Barrett's lesions and 20 non\u2010dysplastic Barret's oesophagus (NDBO) patients were prospectively collected. Experts delineated all neoplastic images.<\/jats:p><jats:p>The overlap area of at least four delineations was labelled as the \u2018sweet spot\u2019. The area with at least one delineation was labelled as the \u2018soft spot\u2019. The CAD system was trained on colour and texture features. Positive features were taken from the sweet spot and negative features from NDBO images. Performance was evaluated using leave\u2010one\u2010out cross\u2010validation. Outcome parameters were diagnostic accuracy of the CAD system per image, and localization of the expert soft spot by CAD delineation (localization score) and its indication of preferred biopsy location (red\u2010flag indication score).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Accuracy, sensitivity and specificity for detection were 92, 95 and 85%, respectively. The system localized and red\u2010flagged the soft spot in 100 and 90%, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This uniquely trained and validated CAD system detected and localized early Barrett's neoplasia on WLE images with high accuracy. This is an important step towards real\u2010time automated detection of Barrett's neoplasia.<\/jats:p><\/jats:sec>","DOI":"10.1177\/2050640619837443","type":"journal-article","created":{"date-parts":[[2019,3,7]],"date-time":"2019-03-07T01:27:50Z","timestamp":1551922070000},"page":"538-547","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":112,"title":["The Argos project: The development of a computer\u2010aided detection system to improve detection of Barrett's neoplasia on white light endoscopy"],"prefix":"10.1002","volume":"7","author":[{"given":"Jeroen","family":"de Groof","sequence":"first","affiliation":[{"name":"Department of Gastroenterology and Hepatology University of Amsterdam Amsterdam The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fons","family":"van der Sommen","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering Eindhoven University of 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