Detection of Severe Structural Heart Disease Using an AI-Enhanced Portable 1-Lead ECG: The ACCESS-SHD Study

Aims: Structural heart disease (SHD) often remains undetected until symptoms develop. Portable 1-lead ECG devices return an automated rhythm-based interpretation, but whether AI-ECG adds diagnostic value beyond this interpretation is unknown. We prospectively evaluated a noise-adapted 1-lead AI-ECG algorithm for detecting severe SHD from portable KardiaMobile 6L recordings, and its relative diagnostic value beyond the device's rhythm interpretation. Methods and Results: Adults undergoing outpatient echocardiography between June 2024 and January 2025 recorded a 30-second, 1-lead ECG with real-time AI-ECG inference. The primary endpoint was discrimination for echocardiography-defined severe SHD. Secondary analyses assessed net reclassification improvement (NRI) versus the native interpretation and the number needed to test (NNT). Among 597 participants (median age 61.7 years; 51.4% women), 30 (5.1%) had severe SHD. AI-ECG achieved an AUROC of 0.872 (95% CI: 0.806-0.938), meeting the prespecified endpoint, with 86.7% (70.3-94.7) sensitivity, 72.5% (68.7-76.0) specificity, 99.0% (97.5-99.6) negative predictive value, and 14.4% (10.1-20.3) positive predictive value, with comparable performance across subgroups. AI-ECG increased sensitivity by 34.6 percentage points (95% CI: 13.0-56.0) over the native interpretation and yielded a categorical NRI of 24.3% (95% CI: 2.8-45.9), with 76.9% sensitivity and 80.0% specificity among tracings the device read as normal. An AI-ECG-guided strategy for detecting severe SHD reduced the NNT from 19.7 to 6.9 (a 64.8% reduction). Conclusions: A noise-adapted AI-ECG algorithm detected severe SHD from real-world portable 1-lead ECGs and substantially improved case finding beyond the device's rhythm interpretation, supporting AI-ECG-guided triage as a potential scalable screening strategy.