ECG-Derived Synthetic Tissue Doppler Waveforms Differentiate Physiological Adaptations in Healthy Athletes from Pathological Patterns Associated with Mortality in Young Individuals
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Background
Sudden cardiac death (SCD) in young individuals—especially athletes—remains difficult to diagnose due to overlapping physiological and pathological electrocardiogram (ECG)-related changes. Black-box artificial intelligence (AI) models can detect abnormal ECGs but often lack physiological interpretability. Tissue Doppler Imaging (TDI) provides functional insight into myocardial mechanics but requires echocardiographic imaging. This study investigates whether synthetic TDI features derived from standard ECGs can link electrical patterns to myocardial function, helping distinguish elite athletes, high-risk individuals with abnormal ECGs who died, and long-term survivors with normal ECGs.
Methods
Synthetic systolic, early, and late diastolic myocardial velocities (s′, e′, a′) and mitral annular displacement were generated from resting 12-lead ECGs using a previously validated Generative Adversarial Network (GAN) model. Data came from 28 Norwegian endurance athletes and a 233,770-person open-source community dataset, from which we identified high-risk (n = 21) and controls (n = 29) subjects. Logistic regression quantified the discriminative value of synthetic TDI parameters; model performance was assessed via area under the receiver operating characteristic curve (AUC).
Results
Athletes exhibited higher e′, e′/a′ ratios, and septal displacement than high-risk, consistent with enhanced diastolic compliance. High-risk showed reduced velocities and displacement, consistent with impaired relaxation and subclinical systolic dysfunction. Synthetic e′, e′/a′ ratio, and average maximum displacement differentiated athletes from high-risk with AUCs of 0.75 (Confidence Interval, CI: 1.04 – 2.20), 0.74, (CI: 1.32 – 23.14) and 0.78 (CI: 4.24 – 197.18), respectively; a multivariate model achieved an AUC of 0.80 (CI: 3.23 – 188.25).
Conclusion
ECG-derived synthetic TDI offers a physiologically interpretable link between ECG patterns and myocardial function, distinguishing benign athletic remodeling from patterns associated with mortality risk. As an explainability tool, it may provide functional context for AI-based ECG interpretation, supporting scalable, imaging-free screening in athletic and general populations.
