{"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":1789517496594,"version":"build-2803163510"},"reference-count":24,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T00:00:00Z","timestamp":1765756800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T00:00:00Z","timestamp":1765756800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["JP21K07969"],"award-info":[{"award-number":["JP21K07969"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Digestive Endoscopy"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Rapid on\u2010site evaluation enhances the diagnostic yield of endoscopic ultrasound\u2010guided fine needle aspiration (EUS\u2010FNA), but the cytopathologists are limited. This study aimed to assess the diagnostic capability of an artificial intelligence\u2010assisted ROSE (AI\u2010ROSE) for EUS\u2010FNA.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      The study included 137 patients who underwent EUS\u2010FNA of a pancreatic mass between April 2019 and August 2021. Participants were divided into training (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200996), validation (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200915), and test cohorts (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200926). From the training\/validation cohort, 5157\/615 digital images of cell clusters were extracted and divided into 288\u2009\u00d7\u2009288\u2010pixel patches. These cell clusters were annotated and labeled into five classes. A semantic segmentation architecture was developed. From the test cohort, 120 cell clusters were extracted to compare diagnostic performance between AI\u2010ROSE and 21 endosonographers and 5 cytotechnologists with varying experience levels in ROSE.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In total, 1,097,840 training, 31,817 validation, and 1920 test regions were extracted. In the test cohort, AI\u2010ROSE accuracy for three\u2010category classification (class 1\/2, class 3, and class 4\/5) was 89.8%. For two\u2010category classification (class 1\/2\/3 and class 4\/5), sensitivity, specificity, and accuracy were 89.3%, 98.1%, and 95.1%, respectively. In the comparison cohort, AI\u2010ROSE accuracy for two\u2010category classification was 93.3%, significantly higher than all endosonographers (68.3%; range, 45.8%\u201386.7%) and cytotechnologists (76.3%; range, 72.5%\u201378.3%). The AI\u2010ROSE evaluation time for 120 cell clusters was 6.04\u2009s, much shorter than that of all endosonographers (1800; 480\u20136000\u2009s) and cytotechnologists (2160; 1020\u20133600\u2009s).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The AI\u2010ROSE model shows remarkable speed and accuracy in diagnosing pancreatic cell clusters, enabling rapid decision\u2010making during EUS\u2010FNA. UMIN\u2010CTR; No. 000042212.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/den.70081","type":"journal-article","created":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T10:47:51Z","timestamp":1765795671000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["<scp>AI<\/scp>\n                    \u2010Assisted Real\u2010Time Cytologic Diagnosis During\n                    <scp>EUS<\/scp>\n                    \u2010\n                    <scp>FNA<\/scp>\n                    of Pancreatic Masses (With Video)"],"prefix":"10.1111","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6918-3066","authenticated-orcid":false,"given":"Reiko","family":"Ashida","sequence":"first","affiliation":[{"name":"Second Department of Internal Medicine Wakayama Medical University  Wakayama 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