Back to search

Article

Prospective Validation of a Deep Learning Model to Detect Structural Heart Disease from Apple Watch ECGs: The WATCH-SHD Study

2026-08-17

Abstract excerpt

<h4>ABSTRACT</h4> <h4>Importance</h4> Consumer wearables such as the Apple Watch can record single-lead electrocardiograms (ECGs) but are used mainly to detect rhythm disorders. Artificial intelligence–enhanced ECG (AI-ECG) could extend these real-world recordings for detecting structural heart disease (SHD), yet prospective validation remains limited. <h4>Objective</h4> To prospectively validate a previously d...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
e78fcdaf-6b8a-5582-9396-d3596981c714
DOI
10.64898/2026.08.13.26360395
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Prospective Validation of a Deep Learning Model to Detect Structural Heart Disease from Apple Watch ECGs: The WATCH-SHD StudyDOI 10.64898/2026.08.13.26360395
An Ensemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images: PRESENT SHDSemantic proximity
Advances in Interpretation of Electrocardiogram by Artificial IntelligenceSemantic proximity
Artificial intelligence–enabled electrocardiogram for mortality and cardiovascular risk estimation: An actionable, explainable and biologically plausible platformSemantic proximity
Study protocol for The Heart Watch Study - Prognostic performance of smartwatch 12-lead ECG with advanced ECG analysis in consumer self-screening for cardiovascular diseaseSemantic proximity
Machine Learning, Deep Learning, and Hybrid Approaches for ECG-Based Cardiac Arrhythmia Detection: Techniques, Challenges, and Future PerspectivesSemantic proximity
Artificial Intelligence-Enhanced Electrocardiogram Models for Detection of Left Ventricular Dysfunction: A Comparison StudySemantic proximity
Composite Artificial Intelligence–Enabled Electrocardiogram for Detection and Prediction of Structural Heart DiseaseSemantic proximity
Foundation models for generalizable electrocardiogram interpretation: comparison of supervised and self-supervised electrocardiogram foundation modelsSemantic proximity
Deep learning enables diagnosis of atrial cardiomyopathy from routine 12-lead electrocardiogramSemantic proximity
Electrocardiogram-Based Deep Learning for Time-Resolved Prediction of Heart Failure With Reduced Ejection Fraction: A Multinational StudySemantic proximity
Time-Dependent ECG-AI Prediction of Fatal Coronary Heart DiseaseSemantic proximity
An Artificial Intelligence Model for Detection of Heart Failure with Preserved Ejection Fraction: A Report from HeartShare StudySemantic proximity
Select a neighboring publication to make it the new centre.