Derivation and validation of an artificial intelligence-based plaque burden safety cut-off for long-term acute coronary syndrome from coronary computed tomography angiography
This study suggests that PAV up to 2.6% quantified by AI is associated with low-ACS risk in two independent patient cohorts. This cut-off may be helpful for clinical application of AI-guided CCTA analysis, which detects any plaque in up to 96-97% of patients.
Authors
- Teemu Maaniitty (ORCID: https://orcid.org/0000-0001-5819-4635)
- Antti Saraste (ORCID: https://orcid.org/0000-0003-2488-0893)
- 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)
- Tanja Kero (ORCID: https://orcid.org/0009-0008-2002-8699)
- Jeroen Joost Bax (ORCID: https://orcid.org/0000-0001-7368-0500)
- Takeru Nabeta (ORCID: https://orcid.org/0000-0003-4671-7667)
- Riku Klén (ORCID: https://orcid.org/0000-0002-0982-8360)
- Juhani Knuuti (ORCID: https://orcid.org/0000-0003-3156-9593)
- Sarah Bär (ORCID: https://orcid.org/0000-0003-0045-6596)
Institutions
- Uppsala University (SE)
- University of Turku (FI)
- Leiden University Medical Center (NL)
- University Hospital of Bern (CH)
- Turku University Hospital (FI)
- Turku PET Centre (FI)
- Amsterdam University Medical Centers (NL)
- Vrije Universiteit Amsterdam (NL)
Publication Details
- Journal
- European Heart Journal - Cardiovascular Imaging
- Published
- 2025-04-17
- DOI
- https://doi.org/10.1093/ehjci/jeaf121
- Citations
- 8
- Primary Topic
- Cardiac Imaging and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 10.88
Funders
- National Science Foundation
- Foundation for Cardiovascular Research
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
- Turun Yliopisto
- Sydäntutkimussäätiö