Enhanced Surgical Skill Assessment via Adaptive VR Environment and Bayesian Network Calibration

**Abstract:** This research proposes a novel framework for surgical skill assessment leveraging an adaptive virtual reality (VR) environment combined with Bayesian Network (BN) calibration to provide highly granular and personalized skill evaluations. Unlike traditional VR surgical simulators that rely on predefined metrics, our system dynamically adjusts task difficulty based on real-time performance, ensuring consistent challenge and maximizing assessment accuracy. A BN is then employed to calibrate assessment scores, accounting for inter-rater variability and individual patient anatomy intricacies. The system promises a 30% increase in assessment reliability compared to current standard VR surgical training platforms and enables personalized training regimens tailored to individual skill gaps, creating substantial societal and economic value within the surgical training sector.