Real-time Generalizable Heart Valve Mechanics for Clinical Disease Assessment via a Physics-Conditioned Neural Operator

Abstract

Mitral regurgitation is the most common heart valve disorder worldwide, affecting over 2% of the global population, rising to at least 10% in adults over 75, and causing approximately 15% of valvular heart disease-related deaths. Yet only a minority of patients with severe disease undergo corrective surgery. Rapid assessment of valve mechanics could enable earlier, more precise intervention, but traditional finite element simulations remain too slow for clinical timelines and parameter sweeps. We introduce the Physics-Conditioned Neural Operator (PCNO), a transformer-based surrogate that predicts leaflet displacement, strain, and stress fields across mitral and tricuspid geometries, conditioned on systolic blood pressure and tissue properties. Trained on functional, regurgitated, and pathological valves, including tethering, P2 prolapse, and annular dilation, PCNO achieves up to a 15,260x speedup over fine mesh finite element simulations with comparable accuracy, identifies pathology class, and resolves diagnostic metrics within 3.5% error under out-of-distribution extrapolation.