Design and Implementation of a Plaque Detachment Prediction System Based on Guidewire Curvature Analysis

The increasing prevalence of cardiovascular and cerebrovascular diseases has led to a growing demand for safe and effective interventional procedures. While catheter-based surgeries have become standard, limitations such as unpredictable plaque behavior and a lack of personalized training environments have hindered their success. Robotic-assisted interventions offer enhanced precision, yet the absence of detailed lesion-level simulation models has compromised the quality of preoperative planning and surgical training. To address these challenges, we proposed a novel simulation frame-work for predicting plaque detachment events based on real-time guidewire curvature analysis. A 3D bifurcated vascular model with embedded calcified plaques was constructed, and guidewire navigation was simulated using the SOFA frame-work. The system continuously analyzed the guidewire's total curvature to detect mechanical thresholds indicative of plaque instability. Once the curvature exceeded a predefined limit, the simulation predicted potential detachment events. Experimental results confirmed the feasibility and accuracy of our method in replicating clinically relevant rupture behavior, demonstrating its potential as a foundation for future extensions such as AI-auided interventions and VR-based surgical education.