, known as HeartBEiT, forms a foundation upon which specialized diagnostic models can be created. The team noted that in comparison tests, models created using HeartBEiT surpassed established methods for ECG analysis.[CNNs], which are commonly used machine learning algorithms for computer vision tasks.
Mount Sinai is taking the field in a bold new direction by building on the intense interest in so-called generative AI systems such as ChatGPT, which are built on transformers—deep learning models that are trained on massive datasets of text to generate human-like responses to prompts from users on almost any topic. Researchers are using a related image-generating model to create discrete representations of small parts of the ECG, enabling analysis of the ECG as language.
, if they have a genetic disorder called hypertrophic cardiomyopathy, and how effectively their heart is functioning. In each case, our model performed better than all other tested baselines."on 8.5 million ECGs from 2.1 million patients collected over four decades from four hospitals within the Mount Sinai Health System. Then they tested its performance against standard CNN architectures in the three cardiac diagnostic areas.