An AI algorithm uses ECG data to accurately predict risk of future regurgitant heart valve diseases before symptoms or ultrasound changes appear.
Key Details
- 1International team trained AI on nearly 1 million ECG and echocardiogram records from 400,000+ patients in China.
- 2The model predicted risk of mitral, tricuspid, or aortic valvular regurgitation within years, with 69-79% accuracy.
- 3High-risk patients identified by AI were up to 10 times more likely to develop valve disease.
- 4Validation was conducted on 34,000+ US patients, supporting generalizability across populations.
- 5Trials with the NHS are scheduled for late 2025 to evaluate real-world performance.
- 6Research funded by British Heart Foundation, published in European Heart Journal.
Why It Matters
This research demonstrates how AI can leverage routinely acquired ECGs to flag patients at high risk for heart valve disease before traditional imaging reveals abnormalities, potentially transforming screening and management. It highlights the potential of integrating imaging and non-imaging clinical data in AI models to enable earlier and more proactive cardiac care.
Source
EurekAlert
Related News
•EurekAlert
AI-Powered OCT Enables Rapid 'Optical Biopsy' for Early Endometrial Cancer Detection
A team at Washington University has developed a catheter-based 3D OCT system with AI to quickly and noninvasively detect early endometrial cancers.
•EurekAlert
AI Clinical Reasoning in Diagnostics and Digital Fatigue in Healthcare
Recent JMIR features explore large language models in clinical diagnostics and digital fatigue among healthcare professionals.
•EurekAlert
KAIST, MIT, Microsoft Develop Efficient AI Image Upsampling for Robotics
KAIST, MIT, and Microsoft have created 'Upsample Anything,' a training-free AI method to restore high-resolution visual data from compressed images with up to 16x improved GPU memory efficiency.