Riccardo Miraglia
Artificial Intelligence-Based Solutions for Supporting Cardiovascular Disease Diagnosis.
Rel. Luca Ulrich, Francesca Giada Antonaci. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Biomedica, 2025
Abstract
Cardiovascular disease remains the leading cause of mortality worldwide, placing a significant burden on healthcare systems. Despite substantial advancements in medical diagnostics and therapeutic interventions, the clinical assessment of heart failure still relies on traditional imaging techniques, such as echocardiography. Among various diagnostic parameters, Left Ventricular Ejection Fraction (LVEF) is a crucial metric for evaluating systolic cardiac function and stratifying heart failure phenotypes. However, its manual estimation depends on operator expertise, leading to inter- and intra-observer variability that compromises diagnostic reproducibility. To address these limitations, the developed approach aims to improve the precision and reliability of LVEF quantification while reducing the dependency on manual interpretation.
This study proposes an automated framework for estimating LVEF from echocardiographic images using AI-driven methodologies
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