Surgical Instrument Recognition in Laparoscopic Cholecystectomy Using YOLOv8

Le résumé fourni par la source

The recognition of surgical instruments using Artificial Intelligence (AI) in Minimally Invasive Surgery (MIS) offers significant opportunities for data-driven improvements in surgical training and patient safety, with surgical instrument recognition being a critical component. MIS remains challenging due to complex intraoperative conditions that limit conventional real-time object detection AI algorithms. This paper optimises the state-of-the-art YOLOv8 object detection architecture for surgical instrument recognition and takes a novel approach to deal with imbalance of the dataset. A large-scale dataset of 25 surgical videos, consolidated from CholecTrack20, CholecT50, and Cholec80, underwent custom cleaning and strategic partitioning to address severe class imbalance. A systematic tournament identified YOLOv8l as the best-performing variant of the YOLOv8 versions, achieving Mean Average Precision (mAP)@0.5 of 64.4% on the test set. Despite hyperparameter tuning, the attempt led to overfitting, while the data-balancing strategy, despite a slight reduction in overall mAP@0.5 to 60.4%, the approach significantly improved per-class accuracy, notably doubling performance for the rarely used instruments such as Scissors and Clippers. This study establishes a new performance baseline for surgical instrument recognition using a carefully configured YOLOv8l model, underscoring that data imbalance, rather than architecture, is the primary limitation. Future progress for surgical instrument recognition will hinge on data-centric strategies for robust and clinically reliable models.

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