AI-based ECG models detected left ventricular hypertrophy with AUROC 0.87, sensitivity 69%, and specificity 87%, outperforming traditional criteria (AUROC 0.68).
Systematic Review and Meta-Analysis (n=66,479)
Yes
Does artificial intelligence improve the diagnostic accuracy and sensitivity of cardiovascular screening for conditions like left ventricular hypertrophy and channelopathies compared to traditional criteria?
AI-enhanced screening tools, particularly for ECG interpretation, demonstrate superior sensitivity and diagnostic accuracy compared to traditional criteria, offering a promising adjunct for early detection of occult cardiac disease in athletes.
Effect estimate: AUROC 0.87
Absolute Event Rate: 0.87% vs 0.68%
Sudden cardiac death in athletes, though uncommon, remains a major concern in sports cardiology. Many responsible cardiovascular conditions, including cardiomyopathies, inherited channelopathies, valvular disease, and congenital coronary anomalies, may remain asymptomatic until intense physical exertion. Current pre-participation screening relies on clinical history, physical examination, electrocardiography, and selective cardiac imaging. While effective, these tools are limited by interobserver variability, dependence on specialist expertise, and difficulty distinguishing physiological athletic remodeling from pathological disease. These limitations have prompted growing interest in artificial intelligence (AI) as an adjunct to cardiovascular screening in athletes. This review summarizes current evidence on AI applications in sports cardiology, with a focus on electrocardiography, digital auscultation, transthoracic echocardiography, and selected imaging modalities. AI-enhanced electrocardiographic analysis has demonstrated improved sensitivity compared with traditional criteria for detecting left ventricular hypertrophy, long QT syndrome, including concealed forms, Brugada syndrome, electrolyte abnormalities, and aortic stenosis. Several deep learning models identify disease patterns even when conventional electrocardiographic parameters appear normal, addressing a key limitation of standard screening approaches. AI-assisted digital auscultation improves the detection of pathological murmurs and differentiation from benign flow murmurs, supporting earlier identification of valvular disease. In echocardiography, AI-guided image acquisition and automated analysis improve access, workflow efficiency, and measurement consistency, with diagnostic performance approaching expert interpretation. This review proposes a pragmatic AI-integrated screening framework that complements clinician judgment rather than replacing it. Although promising, limitations remain, including limited athlete-specific training data, false-positive risk related to physiological remodeling, and the need for external validation. When thoughtfully integrated into clinical workflows, AI may enhance early detection of occult cardiac disease and improve cardiovascular risk stratification in athletes.
Jalkh et al. (Tue,) conducted a systematic review and meta-analysis in Left ventricular hypertrophy detection (n=66,479). AI-based ECG models for LVH detection vs. Traditional ECG criteria (Sokolow-Lyon, Cornell) was evaluated on Diagnostic accuracy of LVH detection by ECG (AUROC 0.87). AI-based ECG models detected left ventricular hypertrophy with AUROC 0.87, sensitivity 69%, and specificity 87%, outperforming traditional criteria (AUROC 0.68).