AI-Based Markerless Computer Vision Framework for Open Surgery Skill Assessment: A Prototype Assessment Framework

Background Artificial intelligence (AI) enables hand motion tracking from standard surgical video recordings; however, translating these data into meaningful performance metrics remains challenging. We evaluated the preliminary validity of a markerless, AI-driven system that generates interpretable technical skill scores from an open-surgery task. Methods Sixteen medical students and one instructor performed a one-handed knot-tying task recorded with a smartphone camera while wearing motion sensors beneath surgical gloves. A deep learning algorithm tracking 21 hand joints mapped wrist trajectories and generated visualization boundaries from which kinematic parameters were derived and grouped into three domains scored from 0 to 10: economy of motion (EM), flow of motion (FM), and spatial organization (SO). AI metrics were validated against sensor-based data. Parameters and domain scores were correlated with expert-rated product quality (PQ) and technical performance (TP) using validated checklists. Results Nineteen performances were analyzed. AI metrics demonstrated strong correlations with sensor-based measures (r = 0.79-0.88, P < 0.01). EM metrics (path length, number of movements, task time) were associated with PQ and TP (r = 0.59-0.67, P < 0.01). Smoothness within the FM domain correlated with PQ and TP (r = 0.56-0.57, P < 0.01), while the composite FM score moderately correlated with TP (r = 0.44, P = 0.057). Working area within the SO domain demonstrated a moderate association with TP (r = 0.41, P = 0.08). Conclusion This prototype AI framework translated hand kinematics into interpretable, cohort-normalized domain-level scores that aligned with expert assessment. The findings support the feasibility of video-based kinematic scoring and provide preliminary evidence of construct validity. Further studies are warranted to determine reliability and generalizability.