Performance of fully automated deep-learning-based coronary artery calcium scoring in ECG-gated calcium CT and non-gated low-dose chest CT
Question How accurate and reliable is deep-learning-based coronary artery calcium scoring (DL-CACS) in ECG-gated CT and non-gated low-dose chest CT (LDCT) across multivendor datasets? Findings DL-CACS showed near-perfect performance for ECG-gated CT. For non-gated LDCT, performance was excellent using manual scores as the reference and lower but reliable when using ECG-gated CT scores. Clinical relevance DL-CACS provides a reliable and automated solution for CACS, potentially reducing workload and improving diagnostic workflow. It supports cardiovascular risk stratification and broader clinical adoption, especially in settings where ECG-gated CT is unavailable.
Authors
- Baren Jeong (ORCID: https://orcid.org/0000-0003-2812-7823)
- Eun‐Ah Park (ORCID: https://orcid.org/0000-0001-6203-1070)
- Sihwan Kim (ORCID: https://orcid.org/0000-0001-9444-0303)
- Jong Hyo Kim (ORCID: https://orcid.org/0000-0002-5695-4976)
- Chulkyun Ahn (ORCID: https://orcid.org/0000-0003-2919-4892)
- Whal Lee (ORCID: https://orcid.org/0000-0003-1285-5033)
- Yoon Seong Lee (ORCID: https://orcid.org/0000-0001-6833-6596)
Institutions
- Seoul National University (KR)
- New Generation University College (ET)
- Seoul National University Hospital (KR)
Publication Details
- Journal
- European Radiology
- Published
- 2025-05-10
- DOI
- https://doi.org/10.1007/s00330-025-11559-4
- Citations
- 3
- Primary Topic
- Cardiac Imaging and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 4.08
Funders
- Seoul National University
- Seoul National University Hospital