Development of an Artificial Intelligence Platform for Surgical Tool Detection and Phase Recognition in Laparoscopic...

Surgical tool detection and phase recognition are the foundational areas for objective surgical assessment and future integration with intelligent operative systems. We aimed to develop and validate an artificial intelligence (AI) model for automated surgical tool detection and phase recognition in laparoscopic distal gastrectomy for gastric cancer.

In this retrospective, single-center study, we included 93 annotated laparoscopic distal gastrectomy videos from Seoul National University Hospital. A You Only Look Once version 8 (YOLOv8) model was trained to detect 14 surgical instruments across 60,102 annotated images using a refined tip-focused annotation strategy. The surgical workflow was divided into 12 predefined phases. Tool presence inferred from the YOLOv8 output was structured and used as input for a bidirectional long short-term memory (bi-LSTM) network for temporal phase recognition. Performance was evaluated using the mean average precision (mAP), precision, recall, F1 score, and accuracy.

The YOLOv8 model achieved a 94% mAP at an intersection over union threshold of 0.50 (mAP50), with a precision >95% for recognizing frequently used tools. The bi-LSTM model achieved a precision of 82%, recall of 84%, F1 score of 83%, and accuracy of 85%. Phase prediction was the highest for clear anatomical transitions and lowest for visually ambiguous phases.

This AI-based framework demonstrated reliable performance in surgical tool detection and phase recognition. Using the YOLO-inferred tool as input to the bi-LSTM phase model improved robustness and scalability. This approach offers a foundation for real-time surgical guidance, quality benchmarking, and structured resident training in gastric cancer surgery.