Assessing Cardiac Functionality by Means of Interpretable AI and Myocardial Strain

Abstract

Cardiac Imaging is a powerful methodology for the accurate assessment of heart functionality. Among the possible approaches, Myocardial Strain assesses the functionality of the heart by tracking the movement and deformation of myocardium during the cardiac cycle. This information, that can be acquired also by means of Cardiac Magnetic Resonance, can pave the way to the development of predictive models using machine learning. In this work, we developed a predictive model of left ventricular ejection fraction, which is a measure of the heart’s function to pump oxygen-rich blood to the body, trained using strain data. Specifically, we developed a fully interpretable model based on a rule-based Fuzzy Inference System, coupled with a novel methodology for the disambiguation of the rules. Our results show that the developed model is able to accurately estimate the ejection fraction, and can provide physicians with additional insights about the role of strain features.

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