{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T00:11:36Z","timestamp":1789517496822,"version":"build-2803163510"},"reference-count":25,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2024,11,13]],"date-time":"2024-11-13T00:00:00Z","timestamp":1731456000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2024,11,13]],"date-time":"2024-11-13T00:00:00Z","timestamp":1731456000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100003975","name":"Israel Cancer Association","doi-asserted-by":"publisher","award":["CRM:0008000001271"],"award-info":[{"award-number":["CRM:0008000001271"]}],"id":[{"id":"10.13039\/501100003975","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001738","name":"Ministry of Science, Technology and Space","doi-asserted-by":"publisher","award":["0005080"],"award-info":[{"award-number":["0005080"]}],"id":[{"id":"10.13039\/501100001738","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":[[2025,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and Aim<\/jats:title>\n                    <jats:p>Endoscopic ultrasound (EUS) is the most sensitive method for evaluation of pancreatic lesions but is limited by significant operator dependency. Artificial intelligence (AI), in the form of computer\u2010aided detection (CADe) systems, has shown potential in increasing accuracy and bridging operator dependency in several endoscopic domains. However, the complexity of integrating AI into EUS is far more challenging. This aims to develop and test the basis for a CADe system for real\u2010time detection and segmentation of all pancreatic lesions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this single\u2010center study EUS studies of pancreatic findings were included. Lesions were outlined by two expert (&gt;5\u00a0years performing EUS) endoscopists, and the two leading types of models were benchmarked. The models' performance was evaluated through per\u2010pixel intersection over union (IoU).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 1497 EUS images from 165 patients were evaluated. The dataset included malignancies, neuroendocrine tumors, benign cysts, chronic and acute pancreatitis, normal fatty pancreas, and benign lesions. The best model demonstrated detection and segmentation on the test set with a mean IoU of 0.73, achieving a PPV, NPV, total accuracy, and ROC of 0.82, 0.96, 0.95, and 0.95, respectively. The algorithm is adaptable for real\u2010time processing.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>We developed and tested deep learning models for real\u2010time detection and segmentation of pancreatic lesions during EUS with promising results. This constitutes the basis for a CADe system for EUS, which could be valuable in future detection and evaluation of pancreatic lesions. Further studies for validation and generalization are underway.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/jgh.16814","type":"journal-article","created":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T00:44:59Z","timestamp":1731545099000},"page":"235-240","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Enhancing detection of various pancreatic lesions on endoscopic ultrasound through artificial intelligence: a basis for computer\u2010aided detection systems"],"prefix":"10.1111","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4915-111X","authenticated-orcid":false,"given":"Tom","family":"Konikoff","sequence":"first","affiliation":[{"name":"Division of Gastroenterology Rabin Medical Center  Petach\u2010Tikva Israel"},{"name":"Faculty of Medical and Health Sciences Tel Aviv University  Tel Aviv Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nadav","family":"Loebl","sequence":"additional","affiliation":[{"name":"Rabin Medical Center Innovation Lab Rabin Medical Center  Petah Tikva Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ariel A.","family":"Benson","sequence":"additional","affiliation":[{"name":"Hadassah Medical Center and Faculty of Medicine Hebrew University of Jerusalem, Institute of Gastroenterology and Liver Diseases  Jerusalem Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Orr","family":"Green","sequence":"additional","affiliation":[{"name":"Faculty of Medical and Health Sciences Tel Aviv University  Tel Aviv Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hunter","family":"Sandler","sequence":"additional","affiliation":[{"name":"Faculty of Medical and Health Sciences Tel Aviv University  Tel Aviv Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2831-4402","authenticated-orcid":false,"given":"Rachel","family":"Gingold\u2010Belfer","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology Rabin Medical Center  Petach\u2010Tikva Israel"},{"name":"Faculty of Medical and Health Sciences Tel Aviv University  Tel Aviv Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zohar","family":"Levi","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology Rabin Medical Center  Petach\u2010Tikva Israel"},{"name":"Faculty of Medical and Health Sciences Tel Aviv University  Tel Aviv Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leor","family":"Perl","sequence":"additional","affiliation":[{"name":"Rabin Medical Center Innovation Lab Rabin Medical Center  Petah Tikva Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Iris","family":"Dotan","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology Rabin Medical Center  Petach\u2010Tikva Israel"},{"name":"Faculty of Medical and Health Sciences Tel Aviv University  Tel Aviv Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steven","family":"Shamah","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology Rabin Medical Center  Petach\u2010Tikva Israel"},{"name":"Faculty of Medical and Health Sciences Tel Aviv University  Tel Aviv Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,11,13]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1097\/MPA.0000000000000368"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pan.2017.07.007"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1053\/j.gastro.2023.01.022"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ctrv.2017.08.007"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(20)30974-0"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/ajg.2010.122"},{"key":"e_1_2_8_8_1","article-title":"Imaging modalities for characterising focal pancreatic lesions","volume":"4","author":"Best LM","year":"2017","journal-title":"Cochrane Database Syst. 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