Abstract 17037: Validation of Deep Learning System for Comprehensive 12-Lead ECG Interpretation

Introduction: The electrocardiogram (ECG) is a widely available diagnostic tool for evaluating cardiac patients. Although automated ECG interpretation has made significant progress, it has yet to match the accuracy demonstrated by physicians.

Hypothesis: In this study, we hypothesized that an artificial intelligence based ECG system can achieve comparable performance to physicians in accurately identifying 20 essential ECG patterns.

Methods: An AI-powered system comprising six deep neural networks (DNNs) was trained to identify 20 diagnostic patterns from 12-lead ECGs categorized into six groups: rhythm, infarction, conduction abnormalities, ectopy, chamber enlargement, and axis. An independent test set with the consensus of two expert cardiologists was used as a reference standard. We compared the system's performance to that of three General Practitioners (GPs) and six individual cardiologists, using F1 scores as the evaluation metric.

Results: The AI system was trained on 932,711 standard 12-lead ECGs from 173,949 patients. The independent test set comprised 11,932 annotated ECG labels. Figure 1 shows the respective F1 scores of the DNNs, average GP and average cardiologist as follows: Rhythm: 0.957 vs. 0.771 vs. 0.905; Infarction: 0.925 vs. 0.780 vs. 0.852; Conduction abnormalities: 0.893 vs. 0.714 vs. 0.851; Ectopy: 0.966 vs. 0.896 vs. 0.951; Chamber enlargement: 0.972 vs. 0.562 vs. 0.773; Axis: 0.897 vs. 0.601 vs. 0.685. The AI system's diagnostic performance exceeded that of GPs and was on par with cardiologists for all individual diagnostic patterns.

Conclusions: The AI-powered ECG system is able to accurately identify electrocardiographic abnormalities from the 12-lead ECG, highlighting its potential as a clinical tool for healthcare professionals.