A robust and data-efficient deep learning model for cardiac assessment without segmentation.

Conor M Artman¹, Ricardo Henao²

¹AI Research Group, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, CA, 94550, USA. artman1@llnl.gov.

²Department of Biostatistics & Bioinformatics, Duke University, 2424 Erwin Road Suite 1104, Durham, 27705, NC, USA. ricardo.henao@duke.edu.

PubMed PMC DOI PDF

Abstract

Video-based deep learning (DL) algorithms often rely on segmentation models to detect clinically important features in transthoracic echocardiograms (TTEs). While effective, these algorithms can be too data hungry for practice and may be sensitive to common data quality issues. To overcome these concerns, we present a data-efficient DL algorithm, Scaled Gumbel Softmax (SGS) EchoNet, that is robust…

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