AI-Enhanced ECGs Detect Early COPD, With Monica Kraft, MD

Recent research suggests that routine electrocardiograms (ECGs) analyzed through artificial intelligence (AI) may offer a scalable approach to identifying early COPD. In an interview with HCPLive, Monica Kraft, MD, of the Icahn School of Medicine at Mount Sinai, discussed key findings from a new study that assessed the accuracy of an automated COPD diagnosis using deep learning on ECGs.

“A lot of patients, especially as the years go by, undergo cardiology evaluation and don't necessarily have the lungs evaluated similarly,” Kraft said. “[This tool] may allow us to bring other issues to light.”

Chronic obstructive pulmonary disease (COPD) remains a leading cause of morbidity and mortality worldwide. The non-specific symptoms and reliance on time- and resource-intensive testing often delay a diagnosis.

Kraft and colleagues analyzed ≥ 760,000 ECGs across multiple hospital systems and the UK Biobank using a convolutional neural network, demonstrating robust performance in detecting COPD (area under the curve, 0.80–0.82). Kraft emphasized the role of subtle P wave changes as early indicators of COPD.

“The lungs and the heart are very interrelated,” she explained. “The heart is very subject to pressure changes that go on in the lung. COPD starts in [the] smallest [lung branches]. They can be a millimeter in size. The air that we breathe can get into those little airways, but has trouble getting out because those small airways are closed off. And so, those little airways don't empty as well. They hold on to this air, and the lungs get a little bit bigger than they should. That size change…can be very subtle, [and] early on, alters the pressure between the lungs and the heart and also the lungs and the ribs, the chest wall and the diaphragm, all these relationships get altered. Those…subtle pressure changes, we think, actually have an impact on the P wave.”

The model performed well in patients with documented smoking histories, underscoring its relevance to populations at greater risk. Kraft noted potential future applications for other obstructive airway diseases, such as asthma, cystic fibrosis, and bronchiectasis, although further study is needed.

If applied across a health system, Kraft said the algorithm could flag possible COPD on routine ECGs. Cardiologists could note this, prompting primary care providers to initiate further evaluation or pulmonology referral. This could lead to earlier intervention and improved outcomes.

She cautioned that the model is not perfectly specific. Only about 70% of flagged ECGs corresponded to confirmed COPD. Kraft said that a prospective evaluation will be important to define disease severity and guide clinical response before widespread deployment.

Despite limitations, Kraft highlighted the promise of AI-enhanced ECGs, which are inexpensive and easily accessible. Early detection of COPD could slow lung function decline, reduce hospitalizations, and improve survival. This study supports AI-based ECG as a tool for early COPD recognition, complementing traditional diagnostic methods such as spirometry.

“We need to remember that we also are at the early stages of this approach,” Kraft said. “We can identify COPD early, but we need to now understand what is the extent of the disease that we're identifying. We know a little bit about that from the chart review, but what we want to do is bring those patients in live and do an evaluation so that we can describe the type of COPD that we are identifying. Those are all pieces that are in the works…before it becomes a decision support tool at the ECG level, we would need to sort all of that out.”

Part 1 of our interview with Kraft can be viewed here.

A relevant disclosure for Kraft includes AstraZeneca Pharmaceuticals LP.

Reference

Vaid A, Sharma J, Jiang J, et al. Automated diagnosis of chronic obstructive pulmonary disease using deep learning applied to electrocardiograms. EBioMedicine. 2026;123:106066. doi:10.1016/j.ebiom.2025.106066