Dataset related to the article 'A personalized computational framework for the diagnosis of cardiac perfusion defects'

This record contains raw data related to the article 'A personalized computational framework for the diagnosis of cardiac perfusion defects' Abstract Background and objectives: 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 computational framework designed to enhance coronary artery disease diagnosis by predicting MBF using data from routine CT images and clinical measurements. Methods: The computational framework 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. Results: Validation on 28 additional patients showed MBF predictions consistent with experimental and clinical measurements. Confusion matrix analysis assessed the twin’s ability to classify pathological (averaged MBF < 240 ml/min/100 g) versus healthy perfusion regions, yielding a recall of 0.81, with precision of 0.68 and accuracy at 0.7. Conclusions: This work represents the first attempt to predict and validate MBF on such a large cohort, paving the way for future clinical applications. This dataset is not public. It’s available upon reasonable requesto to the corresponding author.