Evaluation of a deep learning model on coronary CT angiography for automatic stenosis detection.

Journal: Diagnostic and interventional imaging

Published Date: Jan 26, 2022

Abstract

PURPOSE: The purpose of this study was to evaluate a deep-learning model (DLM) for classifying coronary arteries on coronary computed tomography -angiography (CCTA) using the Coronary Artery Disease-Reporting and Data System (CAD-RADS).

Authors

Jean-François Paul

Institut Mutualiste Montsouris : 42 Boulevard Jourdan, 75014 Paris. Electronic address: dr.jfpaul@gmail.com.
Adela Rohnean

Institut Mutualiste Montsouris, Department of Radiology, Cardiac Imaging, 75014 Paris, France.
Henri Giroussens

Spimed-AI, 92340 Bourg-La-Reine, France.
Thibaut Pressat-Laffouilhere

CHU Rouen, Department of Biostatistics, 76000 Rouen, France.
Tatiana Wong

Institut Mutualiste Montsouris, Department of Radiology, Cardiac Imaging, 75014 Paris, France.

Keywords

Computed Tomography Angiography Constriction, Pathologic Coronary Angiography Coronary Artery Disease Coronary Stenosis Deep Learning Humans Predictive Value of Tests

External Resources

View on PubMed Access via DOI PubMed (35090845)

Evaluation of a deep learning model on coronary CT angiography for automatic stenosis detection.

Abstract

Authors

Keywords

External Resources

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