ECG-based (electrocardiogram) deep learning is a scalable, reproducible, and biologically grounded approach for COPD detection, according to a study published in eBioMedicine.
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. Researchers assessed the effectiveness of electrocardiograms (ECGs) analyzed via artificial intelligence-based deep learning as a tool for early COPD detection.
Mount Sinai researchers utilized a Convolutional Neural Network model to analyze 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).
Mount Sinai researchers analyzed 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.
Researchers concluded that 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.
