Detection of arterial remodeling using epicardial adipose tissue assessment from CT calcium scoring scan
We developed an innovative machine learning model to predict coronary arterial remodeling from epicardial fat and calcification features from low-cost/no-cost screening CTCS scans. Our results suggest that vast number of CTCS scans can support more informed clinical decision-making and potentially reduce the need for invasive and costly testing for low-risk patients.
Authors
- Michelle C. Williams (ORCID: https://orcid.org/0000-0003-3556-2428)
- Ammar Hoori (ORCID: https://orcid.org/0000-0002-8458-1214)
- David L. Wilson (ORCID: https://orcid.org/0000-0001-9763-1463)
- David E. Newby (ORCID: https://orcid.org/0000-0001-7971-4628)
- Justin N. Kim (ORCID: https://orcid.org/0000-0003-4713-8552)
- Robert Gilkeson (ORCID: https://orcid.org/0000-0002-5931-8123)
- Tao Hu (ORCID: https://orcid.org/0000-0001-5267-7804)
- Hao Wu (ORCID: https://orcid.org/0000-0001-6993-8863)
- Ju Hwan Lee (ORCID: https://orcid.org/0000-0001-6762-4588)
- Sanjay Rajagopalan
Institutions
- University Hospitals of Cleveland (US)
- Case Western Reserve University (US)
- University of Edinburgh (GB)
Publication Details
- Journal
- Frontiers in Cardiovascular Medicine
- Published
- 2025-03-14
- DOI
- https://doi.org/10.3389/fcvm.2025.1543816
- Citations
- 2
- Primary Topic
- Cardiovascular Disease and Adiposity
- Type
- article
- Field-Weighted Citation Impact
- 2.78