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AI / informaticsInternational journal of cardiology · 2026

Development and external validation of AI-ECG models in athlete pre-participation screening: Performance, limitations, and clinical implications.

Stefano Palermi, Boroumand Zeidaabadi, Marco Vecchiato … Andrea Saglietto

CardiologyAI / informatics

AI-ECG models show modest accuracy for detecting valvular heart disease in athletes.

Development and external validation of AI-ECG models in athlete pre-participation screening: Performance, limitations, and clinical implications.

Stefano Palermi et al. · International journal of cardiology · 2026

Background

Pre-participation screening (PPS) in competitive athletes aims to identify cardiovascular diseases associated with sudden cardiac death (SCD).

Purpose

To develop and externally validate a deep learning (DL)-based AI-ECG ensemble model for detecting imaging-confirmed structural heart disease in competitive athletes undergoing PPS.

Methods

A convolutional neural network (CNN) ensemble was trained using hospital-derived ECG images from Beth Israel Deaconess Medical Center (BIDMC, Boston, USA) and externally validated in the Italian Team for Athlete CARDiac evaluation and AI-based Risk prediction (ITACARD-AI) registry.

n = 1115 competitive athletes

Results

how accurately the AI-ECG could detect heart valve problems in athletes

AUROC 0.70 (95% CI 0.64 to 0.75)

CI excludes the null - significant

More results

The ITACARD-AI cohort included 1115 competitive athletes (mean age 26 ± 13 years; 70% male), including 48 athletes (4.3%) with VHD and 30 (2.7%) with CM.

External validation demonstrated substantial performance degradation compared with hospital-based internal validation.

More results

Threshold analyses showed high negative predictive values (~99%) but persistently low positive predictive values (≤8%), reflecting limited disease enrichment and strong prevalence dependence.

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Conclusion · 1 of 2

Hospital-trained AI-ECG models demonstrated limited transportability to real-world athlete screening populations.

Conclusion · 2 of 2

The marked reduction in external performance suggests that low disease prevalence, heterogeneous physiological remodeling, and incomplete ECG expression of structural abnormalities may substantially limit class separability in PPS environments.

Post

Development and external validation of AI-ECG models in athlete pre-participation screening: Performance, limitations, and clinical implications.

AI-ECG models show modest accuracy for detecting valvular heart disease in athletes.

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