Development of a digital twin for the diagnosis of cardiac perfusion defects

Myocardial Blood Flow (MBF) is a key indicator of myocardial perfusion, typically assessed through additional clinical tests like dynamic CT perfusion under stress. This study introduces a digital twin designed to enhance coronary artery disease diagnosis by predicting MBF using data from routine CT images and clinical measurements. The digital twin employs AI methods to reconstruct coronary and myocardial geometries and integrates a computational model, featuring 3D coronary arteries and a three-compartment myocardial model, blindly calibrated with data from six representative patients. Validation on 28 additional patients showed MBF predictions consistent with experimental and clinical measurements. Confusion matrix analysis assessed the twin's ability to classify at-risk patients (averaged MBF < 230 ml/min/100g) versus non-at-risk patients, yielding a recall of 0.77, with precision and accuracy at 0.72. This work represents the first attempt to predict and validate MBF on such a large cohort, paving the way for future clinical applications.