Echocardiogram Vector Embeddings Via R3D Transformer for the Improvement of Ejection Fraction Estimation Instruments

Somin Mindy Lee, Daniel Chung, Vasu Kaker, Yongyi Zhao, Irbaz Riaz, Sudheesha Perera, Prabhu Sasankan, George Tang, Po-Chih Kuo, Jacques Kpodonu, Brigitte Kazzi, Leo Anthony Celi

Abstract: The estimation of ejection fraction (EF) is critical to intensive care. Doctors commonly use estimated EF to create plans for ICU patients, and a low EF can indicate ventricular systolic dysfunction, which increases the risk of adverse events including heart failure. Recently, interest has grown in deep learning (DL) instruments that can measure cardiac activity to estimate EF automatically. In particular, the vector embeddings learned by DL-based EF estimation models provide valuable mappings of echocardiograms within latent space, which can be utilized to understand the error patterns of DL-based EF estimators and thus improve these instruments. In this work, we provide those embeddings. To this end we repurpose an R3D transformer, a state-of-the-art deep learning model for Video Action Recognition, to classify whether patients have ventricular dysfunction or not (ejection fraction below or above 50%) using echocardiogram data. Our R3D model achieves a test AUC of 0.916 and a test accuracy of 87.5%, approaching the performance of previous comparable studies with a fraction of the training time. Most importantly, the vector embeddings learned by this model will enable future analysis of the errors of DL-based EF estimators, democratizing the improvement of these instruments. The quality of these embeddings, furthermore, is evidenced by the strong results of the model that learned them.