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

作者：Akhil Vaid, Jiya Sharma, Joy Jiang, Joshua Lampert, Ashwin Sawant, Edgar Argulian, Stamatios Lerakis, Pranai Tandon, Patricia Kovatch, Charles A. Powell, Charles B. Cairns, Girish N. Nadkarni, Monica Kraft · 发表于：EBioMedicine · 年份：2026 · DOI：10.1016/j.ebiom.2025.106066 · 被引用次数：2 · 研究领域：ECG Monitoring and Analysis、COVID-19 diagnosis using AI、Phonocardiography and Auscultation Techniques

BACKGROUND: 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. METHODS: 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). FINDINGS: 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. INTERPRETATION: 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...