Explainable AI Approach for Cardiac Involvement Detection in Anderson-Fabry Disease

Chiara Verdone, Matteo Gravina, Grazia Casavecchia, Rodolfo Belfiore, Benedetta Di Millo

2025

Abstract

Anderson-Fabry Disease (AFD) is a rare X-linked hereditary disorder caused by a deficiency of the enzyme alpha-galactosidase A, leading to the accumulation of globotriaosylceramide (Gb3) in multiple organs, including kidneys and the cardiovascular system. This study explores the role of deep learning techniques in the analysis of cardiac imaging data for the early detection and monitoring of AFD-related cardiac involvement. Using advanced image processing algorithms, we aim to improve diagnostic accuracy, assess myocardial fibrosis progression, and facilitate personalized patient management. Our findings highlight the potential of artificial intelligence in enhancing diagnostic workflows, reducing variability in interpretation, and aiding clinicians in making more informed decisions. Furthermore, the use of non-invasive imaging techniques and Native T1 sequences for mapping studies in cardiac magnetic resonance imaging (CMR) could reduce the need for contrast.

Paper Citation

in Harvard Style

Verdone C., Gravina M., Casavecchia G., Belfiore R. and Di Millo B. (2025). Explainable AI Approach for Cardiac Involvement Detection in Anderson-Fabry Disease. In Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DMDH; ISBN 978-989-758-758-0, SciTePress, pages 811-818. DOI: 10.5220/0013653700003967

in Bibtex Style

@conference{dmdh25,
author={Chiara Verdone and Matteo Gravina and Grazia Casavecchia and Rodolfo Belfiore and Benedetta Di Millo},
title={Explainable AI Approach for Cardiac Involvement Detection in Anderson-Fabry Disease},
booktitle={Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DMDH},
year={2025},
pages={811-818},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013653700003967},
isbn={978-989-758-758-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DMDH
TI - Explainable AI Approach for Cardiac Involvement Detection in Anderson-Fabry Disease
SN - 978-989-758-758-0
AU - Verdone C.
AU - Gravina M.
AU - Casavecchia G.
AU - Belfiore R.
AU - Di Millo B.
PY - 2025
SP - 811
EP - 818
DO - 10.5220/0013653700003967
PB - SciTePress