HeartBEiT: Vision Transformer for Electrocardiogram Data Improves Diagnostic Performance at Low Sample Sizes

The electrocardiogram (ECG) is a ubiquitous diagnostic modality. Convolutional neural networks (CNNs) applied towards ECG analysis require large sample sizes, and transfer learning approaches result in suboptimal performance when pre-training is done on natural images. We leveraged masked image modeling to create the first vision-based transformer model, HeartBEiT, for electrocardiogram waveform analysis. We pre-trained this model on 8.5 million ECGs and then compared performance vs. standard CNN architectures for diagnosis of hypertrophic cardiomyopathy, low left ventricular ejection fraction and ST elevation myocardial infarction using differing training sample sizes and independent validation datasets. We show that HeartBEiT has significantly higher performance at lower sample sizes compared to other models. Finally, we also show that HeartBEiT improves explainability of diagnosis by highlighting biologically relevant regions of the EKG vs. standard CNNs. Thus, we present the first vision-based waveform transformer that can be used to develop specialized models for ECG analysis especially at low sample sizes

Misconceptions and Facts About Aortic Stenosis.

Aortic stenosis is the most common valvular heart disease leading to intervention, and it is typically a disease of the elderly. Recent clinical advances have expanded the role of transcatheter aortic valve intervention in patients with severe aortic stenosis, making aortic valve intervention feasible and effective even in patients at intermediate, high, and prohibitive surgical risk. With the rapid advances in treatment, proper diagnosis becomes crucial for a wide range of patients with aortic stenosis: from "concordant" high-gradient aortic stenosis to "discordant" low-gradient aortic stenosis. The latter group commonly presents a clinical challenge requiring thoughtful and comprehensive evaluation to determine eligibility for aortic valve intervention. Providers at all levels should be familiar with basic diagnostic caveats and misconceptions when evaluating patients with possible aortic stenosis