{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T02:43:30Z","timestamp":1792205010789,"version":"4.4.0"},"reference-count":53,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T00:00:00Z","timestamp":1787270400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Reliable identification of the inferior mesenteric artery (IMA) and inferior mesenteric vein (IMV) is essential for safe vascular control during laparoscopic rectal resection (LRR). We developed and internally evaluated a near-real-time semantic segmentation model using standard white-light laparoscopic video to support intraoperative anatomical recognition.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This single-center retrospective proof-of-concept study included operative videos from 16 patients who underwent LRR between February 2024 and May 2025. Frames were sampled at 1 frame\/s, yielding 2,720 expert-annotated RGB images (1,758 IMA images and 962 IMV images). Data were divided at the patient level into a training\/development cohort (13 patients, 2,260 frames) and an internal holdout test cohort (3 patients, 460 frames). A frame-wise two-dimensional U-Net was configured using nnU-Net v2. Multiclass segmentation was compared with vessel-specific binary segmentation. Twenty gastrointestinal surgeons who had not participated in model development assessed preliminary usability and acceptance using three 0\u20134 Likert items.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Vessel-specific binary segmentation produced more balanced performance, with the clearest improvement for the IMV. In the internal holdout test cohort, the IMA Dice coefficient, precision, and recall were 0.940 +\/\u2212 0.025, 0.945 +\/\u2212 0.023, and 0.940 +\/\u2212 0.020, respectively; the corresponding IMV values were 0.980 +\/\u2212 0.010, 0.982 +\/\u2212 0.017, and 0.978 +\/\u2212 0.018. On an NVIDIA RTX 3090 GPU, inference reached 12.7 frames\/s, with approximately 0.08 s of network processing per frame. Surgeons rated perceived recognition accuracy at 3.41 +\/\u2212 0.09 and future clinical potential at 3.39 +\/\u2212 0.09 on the 0\u20134 scale.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The nnU-Net-configured two-dimensional U-Net achieved high segmentation scores on selected internal test data and supported near-real-time visualization. These findings provide early evidence of technical feasibility rather than clinical effectiveness. Multicenter external validation, testing in difficult operative fields, objective human-factors experiments, and prospective clinical evaluation are required before clinical use.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1891914","type":"journal-article","created":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T04:23:14Z","timestamp":1787286194000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep learning-assisted near-real-time identification of the inferior mesenteric artery and vein during laparoscopic rectal resection: a single-center proof-of-concept study"],"prefix":"10.3389","volume":"13","author":[{"given":"Yan","family":"Gao","sequence":"first","affiliation":[{"name":"Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao","place":["Shandong, 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