Frontiers in medicine · 2026
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.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Gao Yan, Hao Di, Yang Yu, Jing Han-Hui, Sun Zong-Sheng, Liu Xiao-Dong, Jiang Zheng, Li Dong-Rui, Zhang Hai-Qiang, Feng Wen-Feng, Liu Gang, Tian Guang-Ye, Liu Shang-Long
Аннотация
BACKGROUND: 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.
METHODS: 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 0-4 Likert items.
RESULTS: 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 +/- 0.025, 0.945 +/- 0.023, and 0.940 +/- 0.020, respectively; the corresponding IMV values were 0.980 +/- 0.010, 0.982 +/- 0.017, and 0.978 +/- 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 +/- 0.09 and future clinical potential at 3.39 +/- 0.09 on the 0-4 scale.
CONCLUSION: 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.
METHODS: 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 0-4 Likert items.
RESULTS: 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 +/- 0.025, 0.945 +/- 0.023, and 0.940 +/- 0.020, respectively; the corresponding IMV values were 0.980 +/- 0.010, 0.982 +/- 0.017, and 0.978 +/- 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 +/- 0.09 and future clinical potential at 3.39 +/- 0.09 on the 0-4 scale.
CONCLUSION: 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.