Author:
Kumazu Yuta,Kobayashi Nao,Kitamura Naoki,Rayan Elleuch,Neculoiu Paul,Misumi Toshihiro,Hojo Yudai,Nakamura Tatsuro,Kumamoto Tsutomu,Kurahashi Yasunori,Ishida Yoshinori,Masuda Munetaka,Shinohara Hisashi
Abstract
AbstractThe prediction of anatomical structures within the surgical field by artificial intelligence (AI) is expected to support surgeons’ experience and cognitive skills. We aimed to develop a deep-learning model to automatically segment loose connective tissue fibers (LCTFs) that define a safe dissection plane. The annotation was performed on video frames capturing a robot-assisted gastrectomy performed by trained surgeons. A deep-learning model based on U-net was developed to output segmentation results. Twenty randomly sampled frames were provided to evaluate model performance by comparing Recall and F1/Dice scores with a ground truth and with a two-item questionnaire on sensitivity and misrecognition that was completed by 20 surgeons. The model produced high Recall scores (mean 0.606, maximum 0.861). Mean F1/Dice scores reached 0.549 (range 0.335–0.691), showing acceptable spatial overlap of the objects. Surgeon evaluators gave a mean sensitivity score of 3.52 (with 88.0% assigning the highest score of 4; range 2.45–3.95). The mean misrecognition score was a low 0.14 (range 0–0.7), indicating very few acknowledged over-detection failures. Thus, AI can be trained to predict fine, difficult-to-discern anatomical structures at a level convincing to expert surgeons. This technology may help reduce adverse events by determining safe dissection planes.
Funder
Japan Society for the Promotion of Science
Publisher
Springer Science and Business Media LLC
Cited by
34 articles.
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