Structured Viewing Classification Annotations From the MIMIC-IV-ECHO Dataset (ECHOVIEW)

The development of robust echocardiography analyses has been limited by the
scarcity of well-annotated, labeled echocardiogram datasets. We introduce the
Structured Viewing Classification Annotations From the MIMIC-IV-ECHO Dataset
(ECHOVIEW), a derived dataset providing transthoracic (TTE) echocardiogram
viewing classifications for a derived subset of the MIMIC-IV-ECHO dataset.
This derived dataset includes viewing classifications for 29,196
echocardiograms across 717 studies, from 463 patients. Each study contains
multiple echocardiogram views and sometimes uses multiple ultrasound
techniques, with changes in the position and angle of the ultrasound probe
corresponding to different views. Likewise, each patient can have multiple
studies. The ECHOVIEW dataset features 23 separate viewing classifications
predicted for each echocardiogram, which are produced by a machine learning
(ML) classifier. Our model provides classification probabilities for
echocardiograms with at least 10 frames of data. We validated our predictions
by having a licensed cardiologist and an intensivist independently review a
sample of 50 echocardiograms. Their evaluations were then compared against the
ML view classifications. With the release of this dataset, we provide machine-
generated view classifications for the MIMIC-IV-ECHO dataset and hope to
encourage the development of further echocardiography analyses and models.