Abstract
Background
The adoption of point-of-care ultrasound (POCUS) has greatly improved the ability
to rapidly evaluate unstable emergency department (ED) patients at the bedside. One
major use of POCUS is to obtain echocardiograms to assess cardiac function.
Objectives
We developed EchoNet-POCUS, a novel deep learning system, to aid emergency physicians
(EPs) in interpreting POCUS echocardiograms and to reduce operator-to-operator variability.
Methods
We collected a new dataset of POCUS echocardiogram videos obtained in the ED by EPs
and annotated the cardiac function and quality of each video. Using this dataset,
we train EchoNet-POCUS to evaluate both cardiac function and video quality in POCUS
echocardiograms.
Results
EchoNet-POCUS achieves an area under the receiver operating characteristic curve (AUROC)
of 0.92 (0.89–0.94) for predicting whether cardiac function is abnormal and an AUROC
of 0.81 (0.78–0.85) for predicting video quality.
Conclusions
EchoNet-POCUS can be applied to bedside echocardiogram videos in real time using commodity
hardware, as we demonstrate in a prospective pilot study.
Keywords
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Article info
Publication history
Published online: February 24, 2023
Accepted:
February 17,
2023
Received in revised form:
January 11,
2023
Received:
July 20,
2022
Publication stage
In Press Uncorrected ProofIdentification
Copyright
© 2023 Elsevier Inc. All rights reserved.
