Artificial Intelligence-Enhanced Auscultation to Identify Left Ventricular Dysfunction - Development and External...

Left ventricular dysfunction (LVD) is common and associated with adverse prognosis, even in asymptomatic individuals. Early treatment improves outcomes, underscoring the need for efficient strategies to identify patients who may benefit from echocardiographic evaluation. This study aimed to develop and externally validate an AI auscultation method to detect LVD among patients referred for clinically indicated echocardiography.

In this prospective, multicenter study, adult patients referred for echocardiography were enrolled to undergo digital auscultation at four standard auscultation sites with simultaneous single-lead ECGs. LVD was defined as a left ventricular ejection fraction (LVEF) less than or equal to 40% on concurrent echocardiography. An ensemble model combining a transformer-based network and a one-dimensional convolutional neural network was trained on multicenter data and externally validated. The model's performance was compared with a seven-cardiologist reader substudy and clinical logistic regression models incorporating clinical variables, single-lead ECG-derived features, and B-type natriuretic peptide levels.

The multicenter training cohort included 2359 patients (mean age, 69.7±13.5 years; 40.4% female; 8.9% with reduced LVEF). The external validation cohort of 964 patients (mean age, 68.6±15.4 years; 38.9% female; 7.6% with reduced LVEF) was obtained from another independent multicenter registry. The four-site AI auscultation outperformed ECG-only models and the clinical logistic models, achieving an area under the receiver operator curve (AUROC) of 0.926 (95% confidence interval [CI], 0.911 to 0.939) in the training cohort. In the external validation cohort, AUROC was 0.904 (95% CI, 0.876 to 0.930) and sensitivity, specificity, and accuracy were 86.3%, 74.2%, and 75.1%, respectively. In a 100-patient reader substudy, AI auscultation outperformed cardiologist consensus in accuracy (82.0% vs. 62.0%; P=0.004). Model performance was consistent across prespecified subgroups. The simplified two-site auscultation model retained high accuracy in the external validation cohort (AUROC, 0.903; 95% CI, 0.878 to 0.927).

AI auscultation accurately detected LVD among patients referred for clinically indicated echocardiography, supporting its potential as a scalable and accessible pre-echocardiographic risk-enrichment tool. Further validation is warranted before broader screening applications. (Funded by AMI Inc. and the Japan Society for the Promotion of Science KAKENHI program [grant number 25K19371].).