Coronary artery disease detection using deep learning and ultrahigh-resolution photon-counting coronary CT angiography
Automated deep learning shows remarkable performance for the diagnosis of significant CAD on non-UHR PC-CCTA images. AI pre-reading may be of supportive value to the human reader in daily clinical practice to target and validate coronary artery stenosis using UHR PC-CCTA.
Authors
- Konstantin Nikolaou (ORCID: https://orcid.org/0000-0003-2668-7325)
- Florian Hagen (ORCID: https://orcid.org/0000-0002-1215-9428)
- Thomas Küstner (ORCID: https://orcid.org/0000-0002-0353-4898)
- Simon Greulich (ORCID: https://orcid.org/0000-0002-4844-5442)
- Jens Kübler (ORCID: https://orcid.org/0000-0002-3450-7585)
- Jean‐François Paul (ORCID: https://orcid.org/0000-0001-8251-0511)
- Patrick Krumm (ORCID: https://orcid.org/0000-0003-1705-8439)
- Saif Afat (ORCID: https://orcid.org/0000-0002-6861-6245)
- Moritz T. Winkelmann (ORCID: https://orcid.org/0000-0002-3945-3686)
- Jan M. Brendel (ORCID: https://orcid.org/0000-0002-2729-610X)
- Meinrad Gawaz (ORCID: https://orcid.org/0000-0003-1124-9592)
- Jonathan Walterspiel
- Andreas S Brendlin
Institutions
- Institute Mutualiste Montsouris (FR)
- University of Tübingen (DE)
Publication Details
- Journal
- Diagnostic and Interventional Imaging
- Published
- 2024-10-04
- DOI
- https://doi.org/10.1016/j.diii.2024.09.012
- Citations
- 23
- Primary Topic
- Advanced X-ray and CT Imaging
- Type
- article
- Field-Weighted Citation Impact
- 4.24