Echocardiogram Vector Embeddings Via R3D Transformer for the Advancement of Automated Echocardiography
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1-s2.0-S2772963X24004277-main.pdf
Description
Published version
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1.34 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Chung, Daniel J
Lee, Somin Mindy
Kaker, Vasu
Zhao, Yongyi
Bin, Irbaz
Perera, Sudheesha
Sasankan, Prabhu
Tang, George
Kazzi, Brigitte
Kuo, Po-Chih
Date Issued
September 2024
Journal
JACC: Advances
Publisher
Elsevier BV
Citation
Chung, Daniel J, Lee, Somin Mindy, Kaker, Vasu, Zhao, Yongyi, Bin, Irbaz et al. 2024. "Echocardiogram Vector Embeddings Via R3D Transformer for the Advancement of Automated Echocardiography." JACC: Advances, 3 (9).
Version
Final published version
Abstract
BACKGROUND: Ejection fraction (EF) estimation informs patient plans in the ICU, and low EF can indicate ventricular systolic dysfunction, which increases the risk of adverse events including heart failure. Automated echocardiography models are an attractive solution for high-variance human EF estimation, and key to this goal are echocardiogram vector embeddings, which are a critical resource for computational researchers. OBJECTIVES: The authors aimed to extract the vector embeddings from each echocardiogram in the EchoNet dataset using a classifier trained to classify EF as healthy (>50%) or unhealthy (<= 50%) to create an embeddings dataset for computational researchers. METHODS: We repurposed an R3D transformer to classify whether patient EF is below or above 50%. Training, validation, and testing were done on the EchoNet dataset of 10,030 echocardiograms, and the resulting model generated embeddings for each of these videos. RESULTS: We extracted 400-dimensional vector embeddings for each of the 10,030 EchoNet echocardiograms using the trained R3D model, which achieved a test AUC of 0.916 and 87.5% accuracy, approaching the performance of comparable studies. CONCLUSIONS: We present 10,030 vector embeddings learned by this model as a resource to the cardiology research community, as well as the trained model itself. These vectors enable algorithmic improvements and multimodal applications within automated echocardiography, benefitting the research community and those with ventricular systolic dysfunction (https://github.com/Team-Echo-MIT/r3d-v0-embeddings).
MIT Department
Sloan School of Management
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Harvard--MIT Program in Health Sciences and Technology. Laboratory for Computational Physiology
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
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DOI of Published Version
https://doi.org/10.1016/j.jacadv.2024.101196
