Physics-informed self-supervised learning for predictive modeling of coronary artery digital twins

TL;DR

This paper introduces PINS-CAD, a physics-informed self-supervised learning framework that predicts coronary artery pressure and flow from angiography data, improving scalability and interpretability in cardiovascular risk assessment.

Contribution

It develops a novel physics-informed pretraining approach for graph neural networks using synthetic data, eliminating the need for CFD or labeled clinical data.

Findings

Achieves AUC of 0.73 in predicting cardiovascular events.

Outperforms clinical risk scores and data-driven models.

Provides interpretable pressure and flow biomarkers.

Abstract

Cardiovascular disease is the leading global cause of mortality, with coronary artery disease (CAD) as its most prevalent form, necessitating early risk prediction. While 3D coronary artery digital twins reconstructed from imaging offer detailed anatomy for personalized assessment, their analysis relies on computationally intensive computational fluid dynamics (CFD), limiting scalability. Data-driven approaches are hindered by scarce labeled data and lack of physiological priors. To address this, we present PINS-CAD, a physics-informed self-supervised learning framework. It pre-trains graph neural networks on 200,000 synthetic coronary digital twins to predict pressure and flow, guided by 1D Navier-Stokes equations and pressure-drop laws, eliminating the need for CFD or labeled data. When fine-tuned on clinical data from 635 patients in the multicenter FAME2 study, PINS-CAD predicts future cardiovascular events with an AUC of 0.73, outperforming clinical risk scores and data-driven baselines. This demonstrates that physics-informed pretraining boosts sample efficiency and yields physiologically meaningful representations. Furthermore, PINS-CAD generates spatially resolved pressure and fractional flow reserve curves, providing interpretable biomarkers. By embedding physical priors into geometric deep learning, PINS-CAD transforms routine angiography into a simulation-free, physiology-aware framework for scalable, preventive cardiology.