Motion Capture and Machine Learning-Based Evaluation of Surgical Skills in Laparoscopic Cadaver Training

To promote efficient laparoscopic surgery educa- tion, a system utilizing machine learning has been developed to quantify surgical skill levels based on the movement of surgical instruments. In this system, the movements of surgical instruments operated by surgeons during laparoscopic cadaver surgery training are recorded using an optical motion capture system, and kinematic features are extracted from the recorded data. These extracted features are then used as input for machine learning models, with expert-evaluated scores—based on the Global Operative Assessment of Laparoscopic Skills (GOALS)—serving as the training data. The entire nephrec- tomy procedure was divided into three distinct processes: colon mobilization (Process 1), renal vascular dissection (Process 2), and incision and removal of the remaining tissues (Process 3). In this study, interpretable kinematic features were extracted from instrument movements during the colon mobilization phase (Phase 1). These features were used to train three regression models: Principal Component Analysis followed by Support Vector Regression (PCA-SVR), Partial Least Squares regression (PLS), and Ridge Regression. The models aimed to predict GOALS scores across five key domains: depth perception, bimanual dexterity, efficiency, tissue handling, and autonomy. Model performance was evaluated using 5-fold nested cross- validation repeated 100 times. Among the models, Ridge Re- gression consistently demonstrated high accuracy, with median mean absolute errors (MAEs) below 0.82 in most domains. This system is expected to contribute to more effective surgical education by providing multidimensional, objective feedback on surgical performance.