Journal article
Automated diagnosis of chronic obstructive pulmonary disease using deep learning applied to electrocardiograms
EBioMedicine, v 123, 106066
Jan 2026
PMID: 41485457
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Abstract
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.
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Details
- Title
- Automated diagnosis of chronic obstructive pulmonary disease using deep learning applied to electrocardiograms
- Creators
- Akhil VaidJiya Sharma - Seton Hall UniversityJoy JiangJoshua Lampert - Helmsley Electrophysiology Center, Mount Sinai Hospital, New York, NY, USAAshwin SawantEdgar Argulian - Icahn School of Medicine at Mount SinaiStamatios Lerakis - Icahn School of Medicine at Mount SinaiPranai Tandon - Icahn School of Medicine at Mount SinaiPatricia Kovatch - Icahn School of Medicine at Mount SinaiCharles Powell - Icahn School of Medicine at Mount SinaiCharles B Cairns - Drexel UniversityGirish N NadkarniMonica Kraft (Corresponding Author) - Icahn School of Medicine at Mount Sinai
- Publication Details
- EBioMedicine, v 123, 106066
- Publisher
- Elsevier
- Number of pages
- 14
- Grant note
- Clinical and Translational Science Awards (CTSA) from the National Center for Advancing Translational Sciences: UL1TR004419 National Heart, Lung, and Blood Institute: R01HL167050-02
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.
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- College of Medicine; Emergency Medicine
- Web of Science ID
- WOS:001659886800001
- Other Identifier
- 991022150006904721