Computer methods and programs in biomedicine · 2025
A personalized computational framework for the diagnosis of cardiac perfusion defects.
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Criseo Elisabetta, Baggiano Andrea, Montino Pelagi Giovanni, Nannini Guido, Cusumano Viola, Redaelli Alberto, Pontone Gianluca, Vergara Christian
Аннотация
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.
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.