Clinical cardiology
Diagnostic Performance of a Deep Learning Model Using Phonocardiogram and Single-Lead ECG Signals for Detecting Moderate-or-Greater Aortic Stenosis: Multicenter External Validation
By Saito S, Ishii M, Ogawa S and 21 others.
Abstract
Background
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.
Methods
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).