Use of Machine Learning to Improve Echocardiographic Image Interpretation Workflow: A Disruptive Paradigm Change?
J Am Soc Echocardiogr
.
2021 Apr;34(4):443-445.
doi: 10.1016/j.echo.2020.11.017.
Epub 2020 Dec 1.
Authors
Roberto M Lang
1
,
Karima Addetia
1
,
Tatsuya Miyoshi
2
,
Kalie Kebed
1
,
Alexandra Blitz
3
,
Marcus Schreckenberg
3
,
Niklas Hitschrich
3
,
Victor Mor-Avi
1
,
Federico M Asch
2
Affiliations
1
University of Chicago Medical Center, Chicago, Illinois.
2
MedStar Heart and Vascular Institute/Health Research Institute, Washington, D.C.
3
TOMTEC Imaging Systems, Unterschleissheim, Germany.
PMID:
33276079
PMCID:
PMC8026622
DOI:
10.1016/j.echo.2020.11.017
No abstract available
Publication types
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
MeSH terms
Echocardiography
Humans
Image Interpretation, Computer-Assisted*
Machine Learning*
Workflow
Grants and funding
T32 HL007381/HL/NHLBI NIH HHS/United States