Diagnostic Performance of a Deep Learning Model Using Phonocardiogram and Single-Lead ECG Signals for Detecting...

Valvular heart disease, particularly aortic stenosis (AS), often remains asymptomatic until advanced stages, making early detection challenging. Although cardiac auscultation is a traditional screening tool, its diagnostic accuracy is limited. We aimed to externally validate a deep learning-based model (eAS model) using phonocardiogram (PCG) and single-lead electrocardiogram (ECG) signals to detect moderate-or-greater AS.

PCG and ECG recordings were acquired using a PCG device (Cardio-EGG). Transthoracic echocardiography served as the reference standard. The external validation cohort consisted of 951 patients from 3 medical institutions, including 70 with moderate-or-greater AS. The primary outcome was sensitivity for detecting moderate-or-greater AS; secondary outcomes included the area under the receiver operating characteristic curve (AUROC), specificity, predictive values, and likelihood ratios (LR).

The external validation cohort (n = 951, median age 73 years, male 65%) exhibited more advanced valvular disease than the training/development cohort. The eAS model achieved an AUROC of 0.9632 (95% CI: 0.9471-0.9774). Using the pre-determined threshold, sensitivity was 92.9% and specificity 87.9%, yielding a positive LR of 7.65 and negative LR of 0.08. Model-derived probabilities showed strong associations with echocardiographic measures of AS severity (adjusted R² = 0.5051 for peak transvalvular velocity). Exploratory subgroup analyses showed generally consistent performance, although estimates were imprecise in small subgroups.

The eAS model demonstrated high diagnostic performance for detecting moderate-or-greater AS in a multicenter external validation cohort. AI-based PCG/ECG analysis may support the decision to refer patients from auscultation to echocardiography for AS evaluation.