Artificial Intelligence for the Detection of Hypertrophic Cardiomyopathy From Standard Electrocardiogram.

BACKGROUND

Hypertrophic cardiomyopathy (HCM) is often diagnosed late, increasing avoidable risk and delaying treatment. Artificial intelligence (AI) for 12-lead electrocardiograms (ECGs) may identify undetected HCM earlier.

OBJECTIVES

This study aimed to evaluate the performance of an AI algorithm for HCM detection and identify predictors of correct classification.

METHODS

Of 314 patients with cardiac magnetic resonance imaging (cMRI) for suspected HCM, 150 had analyzable ECGs and confirmed HCM by physician review of medical records and cMRI (ground truth). Eighty-three control patients without cardiomyopathy were included. A proprietary algorithm, Viz HCM (Viz.ai, Inc), labeled ECGs as HCM-positive or HCM-negative; the ECG closest to each patient's cMRI date was compared to ground truth. Diagnostic performance was evaluated by the area under the curve of the receiver-operating characteristic curve and at the prespecified threshold. Predictors of correct detection were determined by multivariable logistic regression for age, sex, race, maximal wall thickness, and hypertrophy subtype.

RESULTS

The mean age of all 233 patients was 56 years, and 62% were male. The algorithm identified HCM with an area under the curve of 0.946 (95% CI: 0.916-0.970), sensitivity of 58% (95% CI: 50.0%-65.6%), and specificity of 100% (95% CI: 95.6%-100%). Apical subtype was a significant predictor of correct detection (adjusted OR: 4.71; 95% CI: 1.71-15.48; P = 0.005). In 9 of 28 patients with ECGs available at least 1 year prior to cMRI, the algorithm detected HCM 2.6 years (median) before clinical diagnosis.

CONCLUSIONS

The AI ECG algorithm demonstrated highly specific HCM detection confirmed by cMRI and may improve early identification of unrecognized HCM.