A multimodal AI-driven framework for cardiovascular screening and risk assessment in diverse athletic populations: innovations in sports cardiology

Introduction The increasing complexity of athlete cardiovascular risk profiles, coupled with evolving demands in pre-participation screening, necessitates robust, interpretable, and physiologically grounded assessment tools. Current approaches to cardiovascular screening, typically reliant on binary ECG interpretations or risk scores, often fall short in accurately differentiating benign athletic heart adaptations from early-stage pathological conditions, particularly across diverse athletic populations. These conventional systems are limited by their inability to capture multi-modal clinical inputs, susceptibility to diagnostic ambiguity, and lack of structured integration between exertional physiology and latent cardiovascular risk. Methods To address these challenges, we propose a novel AI-driven framework that incorporates two key methodological innovations: CardioSpectra, a structured sparse inference model, and Risk-Stratified Exertional Embedding (RSEE), a domain-specific representation learning strategy. CardioSpectra formulates athlete profiles as multivariate probabilistic entities across latent diagnostic states, using sparsity-aware inference to generate interpretable risk predictions while optimizing a sensitivity-specificity trade-off tailored to clinical priorities. RSEE projects heterogeneous input data into an exertion-conditioned latent space, aligning model predictions with observed physiological variance and mitigating false positives by explicitly modeling the overlap between athletic remodeling and subclinical pathology. Results and Discussion Experimental evaluation across varied athlete cohorts demonstrates superior performance in risk stratification accuracy, diagnostic plausibility, and model transparency compared to traditional screening algorithms. This multimodal framework not only advances the fidelity of cardiovascular screening in athletic populations but also establishes a scalable and principled foundation for integrating computational diagnostics with real-world cardiological assessment practices.