Peer-Reviewed Publication
Eur Radiol2025;35(11):7084-7095.November 1, 2025Journal Article
Performance of fully automated deep-learning-based coronary artery calcium scoring in ECG-gated calcium CT and non-gated low-dose chest CT.
Sihwan Kim1,2, Eun-Ah Park3,4,5, Chulkyun Ahn2, Baren Jeong6, Yoon Seong Lee6, Whal Lee6,7,8, Jong Hyo Kim1,2,6,7,8
1Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea.
2ClariPi Research, Seoul, Republic of Korea.
3Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea. iameuna1@gmail.com.
4Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea. iameuna1@gmail.com.
5Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea. iameuna1@gmail.com.
6Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
7Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea.
8Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea.
Abstract
OBJECTIVES: This study aimed to validate the agreement and diagnostic performance of a deep-learning-based coronary artery calcium scoring (DL-CACS) system for ECG-gated and non-gated low-dose chest CT (LDCT) across multivendor datasets.
MATERIALS AND METHODS: In this retrospective study, datasets from Seoul National University Hospital (SNUH, 652 paired ECG-gated and non-gated CT scans) and the…
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