Aim: Artificial intelligence applied to electrocardiography (AI-ECG) can derive a heart age or ECG-age, potentially reflecting waveform patterns that indicate cumulative myocardial stress. The heart age gap (HA-gap, Δage) is defined as the difference between a person′s ECG-age and chronological age. Former studies suggest a threshold Δage > 8 yrs as a biomarker for accelerated biological age, associated with higher risk for cardiovascular events. In this study, we investigate whether Δage differentiates training-induced physiological from pathological left ventricular remodeling. Methods: An AI-ECG was applied to 162 resting 12-lead ECGs of each professional footballers, population controls without cardiovascular disease, and patients with systolic heart failure (HF). Explainable AI identified contributing leads and waveforms, and results were compared with established ECG voltage criteria for left ventricular hypertrophy (Sokolow-Lyon, Cornell) and low QRS voltage (LQRSV). Results: Accelerated HA (Δage,+) was present in 38.9% of athletes, 35.8% of community controls, and 96.9% of HF patients. As a diagnostic criterion, accelerated HA achieved 96.9% sensitivity and 62.7% specificity for distinguishing diseased from healthy cohorts. In contrast, classical ECG voltage criteria showed lower sensitivity (6−17%) but higher specificity (85−100%). Correlation analyses confirmed significant associations of HA-gap with Cornell voltage (ρ = 0.25, p < 0.001) and LQRSV (limb: ρ = −0.43, p < 0.001; precordial:ρ = −0.32, p < 0.001). Conclusions: The AI-based HA-gap is a multi-factorial marker of ventricular remodeling beyond mass and can separate benign athletic hypertrophy from pathological remodeling with high sensitivity. Incorporating athlete and youth cohorts into model development could further improve specificity to enable future application in preventive and sports cardiology.

The authors have declared no competing interest.

This work was partially funded by the Lower Saxony Vorab of the Volkswagen Foundation and the Ministry for Science and Culture of Lower Saxony (Grant 76211-12-1/21). The Study of Health in Pomerania (SHIP) was supported by the German Federal Ministry for Education and Research, the Ministry for Education, Research and Cultural Affairs, and the Ministry for Social Affairs of Mecklenburg-Western Pomerania (01ZZ9603, 01ZZ0103, 01ZZ0403).

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No new data were collected for this study; we performed secondary analyses of previously collected datasets in accordance with the Declaration of Helsinki and cohort-specific consents/approvals: SHIP: Ethics committee of University Medicine Greifswald (Germany) gave ethical approval for the SHIP cohort used in this analysis (approval BB 39/08); all SHIP participants provided written informed consent. LaLiga: Autonomous Community of Andalusia Ethics Committee (Spain) gave ethical approval for the PF12RED professional footballer ECG dataset (Protocol 1573-N-19, December 2019). MIMIC: Institutional Review Boards of Beth Israel Deaconess Medical Center (BIDMC) and the Massachusetts Institute of Technology (MIT) approved the creation and sharing of the de-identified MIMIC-IV database and waived the requirement for individual patient consent.

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