
This week’s edition features AI-boosted ECGs that can successfully identify hypertrophic cardiomyopathy, a drug combo for the down-hearted, a link between pollution particulate and heart attacks, and more.
AI Gives a Boost to ECGs for Hypertrophic Cardiomyopathy Screening
Publishing in the JACC, this team developed a convolutional neural network (CNN) that was validated using 12-lead electrocardiograms (ECGs) from 2,448 patients with verified diagnoses of hypertrophic cardiomyopathy (as well as more than 51,000 age- and sex-matched cohorts without hypertrophic cardiomyopathy). According to the results of this study, the area under curve (AUC) of the CNN in the validation dataset was 0.95 (95% CI, 0.94 to 0.97) at the optimal probability threshold of 11% for having hypertrophic cardiomyopathy. When the 11% probability threshhold was applied to the testing dataset, the AUC for the CNN was 0.96, indicating a strong ability to detect patients with hypertrophic cardiomyopathy. “The good performance in patients with a normal ECG is fascinating,” Peter Noseworthy. MD, a study author and cardiologist at the Mayo Clinic, said in a press release. “It’s interesting to see that even a normal ECG can look abnormal to a convolutional neural network. This supports the concept that these networks find patterns that are hiding in plain sight.”