Diagnostic accuracy in coronary CT angiography analysis: artificial intelligence versus human assessment
AI-QCT demonstrated superior agreement with invasive QCA compared to clinical CCTA assessments, particularly compared to level 2 readers in those with extensive CAD. Integrating AI-QCT into routine clinical practice holds promise for improving the accuracy of stenosis quantification through CCTA.
Authors
- Michiel J. Bom (ORCID: https://orcid.org/0000-0002-6326-7437)
- James K. Min (ORCID: https://orcid.org/0000-0001-8812-7388)
- Paul Knaapen (ORCID: https://orcid.org/0000-0001-8038-7898)
- Ruurt Jukema (ORCID: https://orcid.org/0000-0002-3212-2665)
- Nick S. Nurmohamed (ORCID: https://orcid.org/0000-0001-9045-6009)
- Ibrahim Danad (ORCID: https://orcid.org/0000-0002-3140-1320)
- James P. Earls (ORCID: https://orcid.org/0000-0001-6343-3661)
- Rachel Bernardo (ORCID: https://orcid.org/0000-0001-7139-6899)
- Ralf W. Sprengers (ORCID: https://orcid.org/0000-0003-1463-1842)
- Ruben W. de Winter (ORCID: https://orcid.org/0000-0003-2199-5943)
- Andrew Choi (ORCID: https://orcid.org/0000-0003-0232-1994)
- Erik S.G. Stroes (ORCID: https://orcid.org/0000-0001-9555-6260)
Institutions
- George Washington University (US)
- Amsterdam University Medical Centers (NL)
- Vrije Universiteit Amsterdam (NL)
- University of Amsterdam (NL)
Publication Details
- Journal
- Open Heart
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1136/openhrt-2024-003115
- Citations
- 15
- Primary Topic
- Cardiac Imaging and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 20.40
Funders
- Heart Foundation
- University of Louisville
- European Atherosclerosis Society