{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T11:05:42Z","timestamp":1783508742269,"version":"3.55.0"},"reference-count":34,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2021,8,16]],"date-time":"2021-08-16T00:00:00Z","timestamp":1629072000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100010418","name":"Institute for Information and Communications Technology Promotion","doi-asserted-by":"publisher","award":["2019\u20100\u201001906"],"award-info":[{"award-number":["2019\u20100\u201001906"]}],"id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001321","name":"National Research Foundation","doi-asserted-by":"publisher","award":["2020R1A2C1009744"],"award-info":[{"award-number":["2020R1A2C1009744"]}],"id":[{"id":"10.13039\/501100001321","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Gastro and Hepatol"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background and Aim<\/jats:title><jats:p>We aimed to develop a convolutional neural network (CNN)\u2010based object detection model for the discrimination of gastric subepithelial tumors, such as gastrointestinal stromal tumors (GISTs), and leiomyomas, in endoscopic ultrasound (EUS) images.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We used 376 images from 114 patients with histologically confirmed gastric GIST or leiomyoma to train the EUS\u2010CNN. We constructed the EUS\u2010CNN using an EfficientNet CNN model for feature extraction and a weighted bi\u2010directional feature pyramid network for object detection. We assessed the performance of our EUS\u2010CNN by calculating its accuracy, sensitivity, specificity, and area under receiver operating characteristic curve (AUC) using a validation set of 170 images from 54 patients. Four EUS experts and 15 EUS trainees were asked to judge the same validation dataset, and the diagnostic yields were compared between the EUS\u2010CNN and human assessments.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In the per\u2010image analysis, the sensitivity, specificity, accuracy, and AUC of our EUS\u2010CNN were 95.6%, 82.1%, 91.2%, and 0.9234, respectively. In the per\u2010patient analysis, the sensitivity, specificity, accuracy, and AUC for our object detection model were 100.0%, 85.7%, 96.3%, and 0.9929, respectively. The EUS\u2010CNN outperformed human assessment in terms of accuracy, sensitivity, and negative predictive value.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>We developed the EUS\u2010CNN system, which demonstrated high diagnostic ability for gastric GIST prediction. This EUS\u2010CNN system can be helpful not only for less\u2010experienced endoscopists but also for experienced ones. Additional EUS image accumulation and prospective studies are required alongside validation in a large multicenter trial.<\/jats:p><\/jats:sec>","DOI":"10.1111\/jgh.15653","type":"journal-article","created":{"date-parts":[[2021,8,9]],"date-time":"2021-08-09T03:44:30Z","timestamp":1628480670000},"page":"3387-3394","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Convolutional neural network\u2010based object detection model to identify gastrointestinal stromal tumors in endoscopic ultrasound images"],"prefix":"10.1111","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3401-1133","authenticated-orcid":false,"given":"Chang Kyo","family":"Oh","sequence":"first","affiliation":[{"name":"Division of Gastroenterology, Department of Internal Medicine, College of Medicine The Catholic University of Korea  Seoul South 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