Hypertrophic cardiomyopathy (HCM) is a leading cause of sudden death in adolescents and initial detection is often difficult. A new UC San Francisco study finds that Artificial Intelligence-enhanced (AI)-Electrocardiograms (ECG) may help identify the condition in its earliest stages and monitor important disease-related changes over time.
The research led by Geoffrey Tison, MD, MPH, in the UCSF Division of Cardiology, was a collaboration between UCSF, the Mayo Clinic and Myokardia Inc. In their study, published in the March 7 issue of the Journal of the American Academy of Cardiology, the authors demonstrated that AI analysis of ECGs can not only accurately predict the diagnosis of HCM, but also that AI-ECG correlates longitudinally with cardiac pressures and lab measurements related to HCM.
