Informed-Exploration Reinforcement Learning for Automated Virtual Coronary Intervention Planning.

Abstract

Virtual coronary intervention planning (VCIP) aims to optimize the hemodynamic outcomes of percutaneous coronary intervention (PCI) in patients with coronary stenosis. However, its clinical adoption remains constrained by the computational burden associated with evaluating numerous combinatorial intervention strategies, leading to time-consuming workflows and potentially suboptimal decisions in the catheterization laboratory. While conventional deep reinforcement learning (DRL) offers a path to automated VCIP, it often explores state-action-reward space inefficiently. In this study, we propose an Informed-Exploration Reinforcement Learning framework that concentrates the search on clinically meaningful interventions by integrating historical intervention experience with patient-specific anatomical and physiological information to guide the generation of functionally informed stent strategies. Extensive experiments on 172 vessels from 146 patients show that IERL achieves high agreement (r = 0.815) with real interventions and excellent computational efficiency with an average run time of 2.1 seconds. By aligning exploration with both prior experience and patient context, IERL provides objective, reproducible, and near-real-time VCIP decision support, enabling timely and interpretable recommendations compatible with catheterization workflows. The code and models are available at: https://github.com/HIC-SYSU/IERL/tree/main.