Does explainable AI-ECG heart age differentiate pathological from physiological LV remodeling? A multi-cohort analysis including young elite athletes

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 of 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. 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). 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). 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.