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 Pathology Tool SÉMIL Improves Stage II Bowel Cancer Risk Assessment
A La Trobe University-developed AI tool accurately predicts relapse risk in stage II bowel cancer using digital pathology images and descriptions.
•EurekAlert
AI-Guided Handheld Cardiac Ultrasound Reduces Referrals and Costs in Spain
AI-guided handheld cardiac ultrasound enables primary care physicians to detect heart failure, reducing specialist referrals and saving costs.
•EurekAlert
AI Tool Predicts Which Rectal Cancer Patients Benefit from Intensive Therapy
UCL researchers developed an AI that analyzes biopsy slides to identify rectal cancer patients who benefit from adding irinotecan to standard chemoradiotherapy.