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This study aimed to develop an AI model for suprapancreatic lymph node dissection during robotic distal gastrectomy (RDG) and evaluate its utility for intraoperative decision-making by surgeons.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We developed a deep learning model using 67 RDG videos (54 for training and 13 for testing) to recognize the pancreas, common hepatic artery (CHA), left gastric artery (LGA), and left gastric vein (LGV). Model performance was evaluated using Intersection over Union (IoU). Twenty surgeons participated in two experiments: experiment 1 assessed peritoneal incision line selection, rated by three experts on a 5-point scale, and experiment 2 assessed the time to CHA identification in a crossover design.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The IoU values for the pancreas, CHA, LGA, and LGV were 0.66, 0.28, 0.216, and 0.232, respectively. In mixed-effects models, experiment 1 showed that AI assistance reduced the proportion of unsafe peritoneal incision lines (scores 1\u20132) compared with no assistance (odds ratio, 0.25; 95% confidence interval [CI], 0.15 to 0.41;\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.001) and improved the mean expert score by 0.64 points (95% CI, 0.39 to 0.89;\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.001). In experiment 2, AI assistance reduced the time to CHA identification by 9.5\u00a0s (95% CI, 2.20 to 16.81;\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.0124).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our AI system successfully supported surgeons\u2019 intraoperative decision-making by improving anatomical recognition and thus can potentially enhance surgical safety.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s10120-026-01756-5","type":"journal-article","created":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T07:27:04Z","timestamp":1779175624000},"page":"874-881","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Artificial intelligence\u2013based anatomical recognition improves surgeon decision-making during robotic gastrectomy"],"prefix":"10.1007","volume":"29","author":[{"given":"Kenichi","family":"Ishibayashi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4241-5015","authenticated-orcid":false,"given":"Noriyuki","family":"Inaki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jumpei","family":"Ikeda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kenta","family":"Doden","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masashi","family":"Takeuchi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kengo","family":"Hayashi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryota","family":"Matsui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Satoru","family":"Matsuda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Toshikatsu","family":"Tsuji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Kinoshita","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hirofumi","family":"Kawakubo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuko","family":"Kitagawa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,19]]},"reference":[{"key":"1756_CR1","first-page":"229","volume":"74","author":"F Bray","year":"2024","unstructured":"Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. 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Yuko Kitagawa holds equity in Direava, Inc. Masashi Takeuchi is the chief executive officer of Direava, Inc. Noriyuki Inaki and Hirofumi Kawakubo received research funding from Direava, Inc. All other authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The study protocol was approved by the Ethics Committee of Kanazawa University Hospital (814725-1) and Keio University School of Medicine (20231207). This study was conducted in accordance with the 1964 Declaration of Helsinki and its later amendments and comparable ethical standards. All patients preoperatively provided written informed consent before participating in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}