Enhanced detection of atrial fibrillation in single-lead electrocardiograms using a cloud-based artificial intelligence platform.

Abstract

Background: Although smartphone-based devices have been developed to record 1-lead ECG, existing solutions for automatic atrial fibrillation (AF) detection often has poor positive predictive value.

Objective: This study aimed to validate a cloud-based deep learning platform for automatic AF detection in a large cohort of patients using 1-lead ECG records.

Methods: We analyzed 8,528 patients with 30-second ECG records from a single-lead handheld ECG device. Ground truth for AF presence was established through a benchmark algorithm and expert manual labeling. The Willem Artificial Intelligence (AI) platform, not trained on these ECGs, was used for automatic arrhythmia detection, including AF. A rules-based algorithm was also used for comparison. An expert cardiology committee reviewed false positives and negatives and performance metrics were computed.

Results: The AI platform achieved an accuracy of 96.1% (initial labels) and 96.4% (expert review), with sensitivities of 83.3% and 84.2%, and specificities of 97.3% and 97.6%, respectively. The positive predictive value was 75.2% and 78.0%, and the negative predictive value was 98.4%. Performance of the AI platform largely exceeded the performance of the rules-based algorithm for all metrics. The AI also detected other arrhythmias, such as premature ventricular complexes, premature atrial complexes along with 1-degree atrioventricular blocks.

Conclusions: The result of this external validation indicates that the AI platform can match cardiologist-level accuracy in AF detection from 1-lead ECGs. Such tools are promising for AF screening and has the potential to improve accuracy in non-cardiology expert healthcare professional interpretation and trigger further tests for effective patient management.