Multi-view Deep Learning Improves Detection of Major Cardiac Conditions from Echocardiography
Year: 2026
Type: dataset
Abstract: This dataset provides a minimal, fully de-identified example of the multiview echocardiography inputs used in “Multi-view Deep Learning Improves Detection of Major Cardiac Conditions from Echocardiography.” It contains 30 echocardiography studies, each with three standard non-Doppler views (apical four-chamber, apical two-chamber, and parasternal long-axis) along with a CSV file specifying file paths and study-level labels for ventricular abnormality. Videos were preprocessed exactly as described in the manuscript, including cropping to the echo cone, removing burned-in text, resizing to 224×224 pixels, and selecting the first 64 frames. This small dataset is intended only to illustrate the multiview data format and support reproducibility of model-loading and training code. It is not suitable for clinical modeling. All clips are fully de-identified and contain no protected health information.
Source: ICPSR Data Holdings
DOI: 10.3886/e241296v1
Authors: Joshua Barrios, Geoff Tison
Institution: University of California, San Francisco
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7125423845