Validation of a fully automated deep learning-enabled solution for CCTA atherosclerotic plaque and stenosis quantification in a diverse real-world cohort

J Cardiovasc Comput Tomogr. 2024 Sep-Oct;18(5):507-509. doi: 10.1016/j.jcct.2024.03.012. Epub 2024 Mar 28.

Authors

Affiliations

¹ Division of Cardiology, Montefiore Medical Center/Albert Einstein College of Medicine, Bronx, NY, USA.
² Department of Cardiology, CEDIMAT, Santo Domingo, Dominican Republic.
³ Cedars-Sinai Heart Institute and Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
⁴ Minneapolis Heart Institute, Abbott Northwestern Hospital, Minneapolis, MN, USA.
⁵ Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
⁶ Division of Cardiology, Montefiore Medical Center/Albert Einstein College of Medicine, Bronx, NY, USA. Electronic address: https://twitter.com/CardioMDPhD.

PMID: 38553402
DOI: 10.1016/j.jcct.2024.03.012

No abstract available

Publication types

Validation Study
Letter

MeSH terms

Aged
Automation*
Computed Tomography Angiography*
Coronary Angiography*
Coronary Artery Disease* / diagnostic imaging
Coronary Artery Disease* / therapy
Coronary Stenosis* / diagnostic imaging
Coronary Vessels / diagnostic imaging
Deep Learning*
Female
Humans
Male
Middle Aged
Plaque, Atherosclerotic*
Predictive Value of Tests*
Radiographic Image Interpretation, Computer-Assisted
Reproducibility of Results
Retrospective Studies
Severity of Illness Index