TY - GEN
T1 - Assessing Cardiac Functionality by Means of Interpretable AI and Myocardial Strain
AU - Nobile, Marco S.
AU - Lupi, Amalia
AU - Bacciu, Leone
AU - Grazioso, Matteo
AU - Gallese, Chiara
AU - Quaia, Emilio
AU - Pepe, Alessia
AU - Ieee, null
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - Artificial Intelligence
KW - Interpretability
KW - Medical Image Analysis
KW - Myocardial Strain
UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=wosstart_imp_pure20230417&SrcAuth=WosAPI&KeyUT=WOS:001698711000037&DestLinkType=FullRecord&DestApp=WOS_CPL
U2 - 10.1109/CIBCB66090.2025.11177137
DO - 10.1109/CIBCB66090.2025.11177137
M3 - Conference contribution
T3 - Ieee Symposium On Computational Intelligence And Bioinformatics And Computational Biology Cibcb
SP - 266
EP - 274
BT - 2025 Ieee Conference On Computational Intelligence In Bioinformatics And Computational Biology, Cibcb
PB - IEEE
T2 - 22nd Conference on Computational Intelligence in Bioinformatics and Computational Biology-CIBCB-Annual
Y2 - 20 August 2025 through 22 August 2025
ER -