Radiomic Analysis to predict Arrhythmias in Athletes by Echocardiography: Artificial Intelligence vs. Formal Methods

The diagnosis and management of premature ventricular beats (PVBs) in athletes remain challenging due to the potential for underlying cardiac pathology. While the electrocardiogram is essential for detecting electrical abnormalities, echocardiography is crucial for evaluating structural heart disease. This study explores the use of radiomics analysis applied to apical 4-chamber echocardiography to automatically and preemptively identify PVB risk, better characterize athlete cardiac remodeling, and enable early detection of pathological changes.We evaluated and compared the limitations and potential of Artificial Intelligence (AI) and Formal Methods (FM) for this task. Using data from 723 athletes, we processed echocardiography videos and extracted over 100 features per athlete to develop robust classifiers.Our findings demonstrate that radiomics can power automated decision-support systems for diagnosis. While AI-based models offer powerful predictive capabilities, FM presents a compelling, mathematically rigorous, and explainable alternative. Both modalities are useful for the early detection of structural substrates underlying PVBs. The collaboration between physicians and computer scientists is crucial to unlocking new frontiers in radiomics and advancing personalized medicine.