Abstract 18640: Universal Interpretation of Electrocardiograms as Plain Language Using Deep Learning

Abstract

Introduction: Deep learning models have shown promise in diagnosing diseases using electrocardiogram (ECG) data. However, each condition currently requires a separate model, which increases computational burden and operational complexity while decreasing overall clinical utility. Objective: Development of a unified modeling approach that allows for the interpretation of any ECG as plain language. Methods: We extracted ECG data from five facilities within the Mount Sinai Health System in New York City using the GE MUSE system. These data contained raw ECG waveforms, as well as cardiologist-confirmed diagnoses situated as plain language. Waveform data were plotted to images, and available diagnoses were allotted unique identifiers using Natural Language Processing. We also considered echocardiographically derived parameters such as left ventricular ejection fraction, valvular disease, and hypertrophic cardiomyopathy which were available for a subset of these patients as additional diagnostic endpoints to create a list of diagnoses per ECG.We created an end-to-end transformer based neural network consisting of a pre-trained vision transformer for extracting features from the ECG joined to a generative transformer for predicting associated diagnostic text. Model performance was evaluated by checking for concordance between predicted and actual diagnoses. Results: A total of 8.5 million ECGs were used for pre-training the feature extracting vision transformer. A total of 152 diagnoses were embedded with this corpus of data within the ECG file. We analyzed 406,000 echocardiogram reports to generate additional diagnostic labels for pathologies mentioned above. The model correctly predicted all diagnoses for 86.7% cases, and at least one correct diagnosis for 96.2% cases. Conclusions: A unified deep learning model can effectively interpret a broad range of disease conditions from ECG and echo data. Such an approach can speed up physician workflows, and allow for easier screening for conditions not normally diagnosed from the ECG. Future work will focus on extending the vocabulary of the model.