Mount Sinai researchers show that deep learning applied to ECGs can detect COPD early and accurately across diverse populations.
Key Details
- 1Study analyzed over 208,000 ECGs from more than 18,000 COPD cases and 49,000 matched controls spanning 2006-2023.
- 2A convolutional neural network achieved AUC 0.80 (internal), 0.82 (external validation), and 0.75 (UK Biobank).
- 3Model explainability linked predictions to clinically relevant ECG features (notably P-wave changes).
- 4Validation included distinct hospitals in New York City and patients from the UK Biobank.
- 5ECG-based AI tool uses standard 10-second, 12-lead data, enabling scalability and low cost for broad COPD screening.
Why It Matters
This work demonstrates that AI-driven ECG interpretation can provide scalable, non-invasive early detection for COPD, a major global health burden. Such AI models could expand access to screening in resource-limited settings, improving patient outcomes through earlier intervention.
Source
EurekAlert
Related News
•EurekAlert
AI-Driven Decision Support Systems Reviewed for Heart Care Impact
A systematic review explores how AI-based clinical decision support systems can enhance cardiovascular disease management while facing adoption and equity challenges.
•EurekAlert
Stanford Study: Physicians Plus AI Chatbots Improve Clinical Decision-Making
Stanford-led studies show that physicians supported by AI chatbots match or exceed chatbot-alone performance in nuanced clinical decision-making.
•EurekAlert
AI Transforms Early Detection and Prediction in Kidney Disease
Artificial intelligence is increasingly enabling earlier detection and improved prediction of kidney disease progression by leveraging complex clinical and imaging data.