Detection of Hypertrophic Cardiomyopathy on Electrocardiogram Using Artificial Intelligence

Abstract

Background: Hypertrophic cardiomyopathy (HCM) is associated with significant morbidity and mortality, including sudden cardiac death in the young. Its prevalence is estimated to be 1 in 500, although many people are undiagnosed. The ability to screen electrocardiograms for its presence could improve detection and enable earlier diagnosis. This study evaluated the accuracy of an artificial intelligence device (Viz HCM) in detecting HCM based on a 12-lead electrocardiogram.

Methods: The device was previously trained using deep learning and provides a binary outcome (HCM suspected or not suspected). This study included 293 HCM-positive and 2912 HCM-negative cases, which were selected from 3 hospitals based on chart review incorporating billing diagnostic codes, cardiac imaging, and electrocardiogram features. The device produced an output for 291 (99.3%) HCM-positive and 2905 (99.8%) HCM-negative cases.

Results: The device identified HCM with sensitivity of 68.4% (95% CI, 62.8-73.5%), specificity of 99.1% (95% CI, 98.7-99.4%), and area under the curve of 0.975 (95% CI, 0.965-0.982). With assumed population prevalence of 0.002 (1 in 500), the positive predictive value was 13.7% (95% CI, 10.1-19.9%) and the negative predictive value was 99.9% (95% CI, 99.9-99.9%). The device demonstrated broadly consistent performance across demographic and technical subgroups.

Conclusions: The device identified HCM based on a 12-lead electrocardiogram with good performance. Coupled with clinical expertise, it has the potential to augment HCM detection and diagnosis.

Keywords: artificial intelligence; cardiomyopathies; deep learning; electrocardiography.