AI-enhanced smartwatch ECG detected LVSD with AUROC of 0.915 using all ECGs and 0.864 using three daily ECGs in real-world monitoring.
Does an AI-enhanced smartwatch ECG reliably detect left ventricular systolic dysfunction compared to echocardiography in real-world settings?
An AI-enhanced single-lead ECG model applied to self-recorded smartwatch data can reliably detect left ventricular systolic dysfunction with high accuracy.
Absolute Event Rate: 0% vs 0%
Abstract Background Artificial intelligence-enhanced electrocardiogram (AI-ECG) using single-lead ECG can detect Left Ventricular Systolic Dysfunction (LVSD). However, most models are trained on Lead I from 12-lead ECGs, which differs fundamentally from smartwatch ECGs. In real-world settings, smartwatch ECGs not only differ structurally and physically from standard ECGs but are also affected by noise and user-related artifacts. Furthermore, validation remains limited due to the scarcity of data from self-recorded smartwatch ECGs. Purpose This study investigates whether self-recorded smartwatch ECGs can reliably monitor LVSD in real-world settings by comparing AI-ECG scores with ejection fraction from echocardiography. Methods From July to October 2024, we enrolled participants who had recently undergone or were scheduled for echocardiography. Eligible individuals were instructed to record ECGs using their smartwatches (Samsung Galaxy or Apple Watch) at least twice daily for over a week, with a paired echocardiogram performed within 14 days. We adapted our previously developed convolutional neural network-based AI-ECG model to analyze smartwatch ECGs. The model was fine-tuned using smartwatch ECG data, leveraging the foundation model architecture with an integrated preprocessing module to manage signal noise inherent to smartwatch-derived data. It outputs a score between 0 and 100, with higher scores indicating a greater likelihood of LVSD. We evaluated model performance using two approaches: (1) Approach 1: All available ECGs were analyzed individually to generate scores and assess overall performance (2) Approach 2: Three ECGs per day were randomly selected for each participant, and their median score was used as the representative value for performance evaluation. All ECGs were processed without explicit adjustment for signal noise. Results A total of 27 participants were included, with 77.4% using Samsung Galaxy Watches and 22.6% using Apple Watches. Echocardiography, performed at a median interval of 6 days, identified 7 participants (36.5%) with LVSD. The median AI-ECG score was 55.0 in the LVSD group and 6.5 in the non-LVSD group. Overall, 1,497 ECGs were collected, including 866 from the LVSD group. When analyzing all available ECGs, the area under the receiver operating characteristic curve (AUROC) was 0.915 (95% confidence interval: 0.900–0.927). When analyzing only three randomly selected ECGs per day, the AUROC was 0.864. Conclusion Our study demonstrates that an AI-based single-lead ECG approach can reliably monitor LVSD when applied to self-recorded smartwatch data in real-world settings. These findings provide important evidence supporting the extension of our AI-ECG model to analyze smartwatch ECGs.Schematic illustration of study flow Model performance
Son et al. (Sat,) reported a other. AI-enhanced smartwatch ECG detected LVSD with AUROC of 0.915 using all ECGs and 0.864 using three daily ECGs in real-world monitoring.