Artificial intelligence-enhanced detection of subclinical coronary artery disease in athletes: diagnostic performance and limitations
AI-enhanced CCTA is a valuable non-invasive tool for detecting CAD in asymptomatic, low-risk populations. The AI model exhibited high sensitivity and NPV, particularly for identifying significant stenosis, reinforcing its potential role in screening. However, limitations such as a lower PPV and overestimation of disease indicate that further refinement of AI algorithms is needed to improve specificity. Despite these challenges, AI-based CCTA offers significant promise when integrated with clinical expertise, enhancing diagnostic accuracy and guiding patient management in low-risk groups.
Authors
- Konstantin Nikolaou (ORCID: https://orcid.org/0000-0003-2668-7325)
- Sebastian Gassenmaier (ORCID: https://orcid.org/0000-0003-3701-2227)
- 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)
- Christof Burgstahler (ORCID: https://orcid.org/0000-0002-6977-4202)
- Patrick Krumm (ORCID: https://orcid.org/0000-0003-1705-8439)
- Moritz T. Winkelmann (ORCID: https://orcid.org/0000-0002-3945-3686)
- Jan M. Brendel (ORCID: https://orcid.org/0000-0002-2729-610X)
- Ilias Tsiflikas (ORCID: https://orcid.org/0000-0001-7166-4689)
- Jonathan Walterspiel
Institutions
- Institute Mutualiste Montsouris (FR)
- Alcimed (FR)
- University of Tübingen (DE)
Publication Details
- Journal
- The International Journal of Cardiovascular Imaging
- Published
- 2024-10-07
- DOI
- https://doi.org/10.1007/s10554-024-03256-y
- Citations
- 10
- Primary Topic
- Cardiac Imaging and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 4.04
Funders
- Universitätsklinikum Tübingen