A new AI algorithm rapidly detects coronary microvascular dysfunction using ECGs, with validation incorporating PET imaging.
Key Details
- 1Researchers developed a deep learning model to identify coronary microvascular dysfunction (CMVD) from 10-second ECG strips.
- 2The model was trained on over 800,000 unlabeled ECGs and fine-tuned with PET imaging and clinical reports.
- 3Performance ranged from an AUC of 0.763 for myocardial flow reserve impairment to 0.955 for left ventricular ejection fraction impairment.
- 4The study was published in NEJM AI and led by cardiology researchers from U-M Health.
Why It Matters
The integration of imaging data with ECG-based AI holds promise for faster, more accessible diagnosis of subtle heart conditions often missed in clinical practice. This could lead to earlier interventions and improved patient outcomes in cardiovascular care.
Source
Cardiovascular Business
Related News
•AuntMinnie
Study: Patients Prefer AI in Radiology as Assistive, Not Standalone Tool
Survey finds patients support AI-assisted radiology but not AI-only interpretations.
•AuntMinnie
AI-Enhanced MRI Boosts Return-to-Play Predictions for Athlete Muscle Injuries
Adding AI to MRI-based classification systems improves return-to-play predictions for professional athletes with muscle injuries.
•HealthExec
AI's Expanding Role in Healthcare and Implications for Radiology
A series of thought leaders and institutions weigh in on AI's transformative potential in healthcare, with emphasis on radiology adoption and responsible use.