Automated classification of coronary LEsions fRom coronary computed Tomography angiography scans with an updated deep learning model: ALERT study
Question Can deep learning models improve objectivity in coronary stenosis grading and reporting as coronary CT angiography (CTA) workloads rise? Findings The deep learning model (CorEx-2.0) identified all patients with severe stenoses when compared with expert readers and approached expert reader performance at vessel level. Clinical relevance CorEx-2.0 is a reliable tool for identifying patients with severe stenoses (≥ 70%), underscoring the potential of using this deep learning model to prioritize coronary CTA reading by flagging patients at risk of severe obstructive coronary artery disease.
Authors
- José P.S. Henriques (ORCID: https://orcid.org/0000-0002-8969-7929)
- R. Nils Planken (ORCID: https://orcid.org/0000-0003-3940-4670)
- Casper F. Coerkamp (ORCID: https://orcid.org/0000-0002-7237-9473)
- Ivana Išgum (ORCID: https://orcid.org/0000-0003-1869-5034)
- Victor A. Verpalen
Institutions
- Amsterdam Neuroscience (NL)
- Amsterdam University Medical Centers (NL)
- Amsterdam University of Applied Sciences (NL)
- University of Amsterdam (NL)
Publication Details
- Journal
- European Radiology
- Published
- 2025-01-10
- DOI
- https://doi.org/10.1007/s00330-024-11308-z
- Citations
- 6
- Primary Topic
- Cardiac Imaging and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 8.16