Artificial intelligence model for perigastric blood vessel recognition during laparoscopic radical gastrectomy with D2 lymphadenectomy in locally advanced gastric cancer.

Abstract

Background: Radical gastrectomy with D2 lymphadenectomy is standard surgical protocol for locally advanced gastric cancer. The surgical experience and skill in recognizing blood vessels and performing lymph node dissection differ between surgeons, which may influence intraoperative safety and postoperative oncological outcomes. Hence, the aim of this study was to develop an accurate and real-time deep learning-based perigastric blood vessel recognition model to assist intraoperative performance.

Methods: This was a retrospective study assessing videos of laparoscopic radical gastrectomy with D2 lymphadenectomy. The model was developed based on DeepLabv3+. Static performance was evaluated using precision, recall, intersection over union, and F1 score. Dynamic performance was verified using 15 intraoperative videos.

Results: The study involved 2460 images captured from 116 videos. Mean(s.d.) precision, recall, intersection over union, and F1 score for the artery were 0.9442(0.0059), 0.9099(0.0163), 0.8635(0.0146), and 0.9267(0.0084) respectively. Mean(s.d.) precision, recall, intersection over union, and F1 score for the vein were 0.9349(0.0064), 0.8491(0.0259), 0.8015(0.0206), and 0.8897(0.0127) respectively. The model also performed well in recognizing perigastric blood vessels in 15 dynamic test videos. Intersection over union and F1 score in difficult image conditions, such as bleeding or massive surgical smoke in the field of view, were reduced, while images from obese patients resulted in satisfactory vessel recognition.

Conclusion: The model recognized the perigastric blood vessels with satisfactory predictive value in the test set and performed well in the dynamic videos. It therefore shows promise with regard to increasing safety and decreasing accidental bleeding during laparoscopic gastrectomy.