Logo image
Back
Automated diagnosis of chronic obstructive pulmonary disease using deep learning applied to electrocardiograms
Journal article   Open access   Peer reviewed

Automated diagnosis of chronic obstructive pulmonary disease using deep learning applied to electrocardiograms

Akhil Vaid, Jiya Sharma, Joy Jiang, Joshua Lampert, Ashwin Sawant, Edgar Argulian, Stamatios Lerakis, Pranai Tandon, Patricia Kovatch, Charles Powell, …
EBioMedicine, v 123, 106066
Jan 2026
DOI: https://doi.org/10.1016/j.ebiom.2025.106066
PMID: 41485457
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://doi.org/10.1016/j.ebiom.2025.106066View
Published, Version of Record (VoR) Open

Abstract

Copd Pulmonology Spirometry Electrocardiogram Ai-ecg Artificial intelligence Machine Learning
Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of morbidity and mortality globally. Effective management hinges on early diagnosis, which is often impeded by non-specific symptoms and resource-intensive diagnostic methods. This study assesses the effectiveness of electrocardiograms (ECGs) analysed via deep learning as a tool for early COPD detection. We utilised a Convolutional Neural Network model to analyse ECGs for detecting COPD. The primary outcome was the accuracy of a new clinical COPD diagnosis as determined by ICD codes. Performance was evaluated using Area-Under-the-Curve (AUC) metrics derived by testing against ECGs from a set of holdout patients, ECGs from patients from another hospital, and ECGs of patients with COPD within the UK BioBank (UKBB). We analysed a total of 208,231 ECGs from 18,225 COPD cases, matched to 49,356 controls by age, sex, and race. The model exhibited robust performance across diverse populations with an AUC of 0⋅80 (0⋅80-0⋅80) in internal testing, 0⋅82 (0⋅81-0⋅82) in external validation and 0⋅75 (0⋅71-0⋅78) in the UKBB cohort. Subsequent analyses linked ECG-derived model predictions with spirometry data, and model explainability highlighted P-wave changes as indicative of COPD. AI-powered ECG analysis offers a promising path for early COPD detection, potentially facilitating earlier and more effective management. Implementing such tools in clinical settings could significantly enhance COPD screening and diagnostic accuracy, thereby improving patient outcomes and addressing the global health burden of the disease. This work was supported in part through the computational and data resources and staff expertise provided by Scientific Computing and Data at the Icahn School of Medicine at Mount Sinai and supported by the Clinical and Translational Science Awards (CTSA) grant UL1TR004419 from the National Center for Advancing Translational Sciences; and R01HL167050-02 from the National Heart, Lung, and Blood Institute.

Metrics

6 Record Views

Details

UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#3 Good Health and Well-Being

InCites Highlights

Data related to this publication, from InCites Benchmarking & Analytics tool:

Collaboration types
Domestic collaboration
Web of Science research areas
Medicine, Research & Experimental
Logo image