Work

Prediction of Left Atrial Volume Parameters from Resting ECGs and Tabular Data Using Deep Learning in the UK Biobank

Year: 2026

Type: preprint

Abstract: We present a deep learning model that predicts left atrial (LA) volume from standard 12-lead ECG recordings and basic patient data. This approach offers a low-cost, scalable alternative to MRI-based LA volume measurement, which remains the clinical gold standard but is often inaccessible. Our model performs regression directly on LA volume targets and leverages Shapley values to provide interpretable feature importance. Results highlight the predictive value of ECG signals and demonstrate that patient features such as weight and height contribute meaningfully to the estimation.

Source: medRxiv

DOI: 10.64898/2026.02.13.26346205

Authors: Moritz Dieing, David Brüggemann, Zareen Farukhi, Olga V. Demler

Institutions: ETH Zurich, Swiss Data Science Center, Massachusetts General Hospital, Brigham and Women's Hospital

Language: English

Topic: ECG Monitoring and Analysis

Subfield: Cardiology and Cardiovascular Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funder: National Institutes of Health