AI-Powered ECG Analysis Promising for Early Detection of COPD

THURSDAY, Jan. 15, 2026 (HealthDay News) — Artificial intelligence (AI) electrocardiogram (ECG) data are promising for early detection of chronic obstructive pulmonary disease (COPD), according to a study published in the January issue of eBioMedicine.

Akhil Vaid, M.D., from the Icahn School of Medicine at Mount Sinai in New York City, and colleagues examined the effectiveness of ECGs analyzed via deep learning as a tool for early detection of COPD. Performance of the convolutional neural network model was assessed 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 U.K. BioBank (UKBB).

A total of 208,231 ECGs from 18,225 COPD cases were analyzed, matched to 49,356 controls by age, sex, and race. The researchers found that across diverse populations, the model exhibited robust performance, with an AUC of 0.80, 0.82, and 0.75, respectively, in internal testing, external validation, and the UKBB cohort. In subsequent analyses, ECG-derived model predictions were linked to spirometry data, and model explainability highlighted P-wave changes as indicating COPD.

“Using the model known as a convolutional neural network, we show that ECGs — a low-cost and widely available tool — can capture COPD-related physiological changes, including those that precede formal clinical diagnosis,” coauthor Monica Kraft, M.D., also from the Icahn School of Medicine at Mount Sinai, said in a statement.

Several authors disclosed ties to the biopharmaceutical industry.

Abstract/Full Text