Performance of fully automated deep-learning-based coronary artery calcium scoring in ECG-gated calcium CT and non-gated low-dose chest CT.

Journal: European radiology
Published Date: May 10, 2025

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.

Authors

  • Sihwan Kim
    Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea.
  • Eun-Ah Park
    Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
  • Chulkyun Ahn
    Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea.
  • Baren Jeong
    Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
  • Yoon Seong Lee
    Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, South Korea.
  • Whal Lee
    Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Korea.
  • Jong Hyo Kim
    Interdisciplinary Program of Radiation Applied Life Science, Seoul National University College of Medicine.

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