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

Authors :: Lorenzatti D
Filtz A
Pina P
Scotti A
Schenone AL
Gongora CA
Kwan AC
Cheng VY
Garcia MJ
Berman DS
Slomka PJ
Dey D
Slipczuk L
Source :: Journal of cardiovascular computed tomography [J Cardiovasc Comput Tomogr] 2024 Sep-Oct; Vol. 18 (5), pp. 507-509. Date of Electronic Publication: 2024 Mar 28.
Publication Year :: 2024
Abstract: Competing Interests: Declaration of competing interest DL, AF – Supported by institutional grants from Amgen and Philips. DD, DSB, PJS – Software royalties from Cedars-Sinai Medical Center. LS – Institutional grants from Amgen and Philips. Site PI for Ocean(a) trial.

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

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