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Fully automatic coronary calcium scoring in non-ECG-gated low-dose chest CT: comparison with ECG-gated cardiac CT

  • Cardiac
  • Published:
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Abstract

Objectives

To validate an artificial intelligence (AI)–based fully automatic coronary artery calcium (CAC) scoring system on non-electrocardiogram (ECG)–gated low-dose chest computed tomography (LDCT) using multi-institutional datasets with manual CAC scoring as the reference standard.

Methods

This retrospective study included 452 subjects from three academic institutions, who underwent both ECG-gated calcium scoring computed tomography (CSCT) and LDCT scans. For all CSCT and LDCT scans, automatic CAC scoring (CAC_auto) was performed using AI-based software, and manual CAC scoring (CAC_man) was set as the reference standard. The reliability and agreement of CAC_auto was evaluated and compared with that of CAC_man using intraclass correlation coefficients (ICCs) and Bland-Altman plots. The reliability between CAC_auto and CAC_man for CAC severity categories was analyzed using weighted kappa (κ) statistics.

Results

CAC_auto on CSCT and LDCT yielded a high ICC (0.998, 95% confidence interval (CI) 0.998–0.999 and 0.989, 95% CI 0.987–0.991, respectively) and a mean difference with 95% limits of agreement of 1.3 ± 37.1 and 0.8 ± 75.7, respectively. CAC_auto achieved excellent reliability for CAC severity (κ = 0.918–0.972) on CSCT and good to excellent but heterogenous reliability among datasets (κ = 0.748–0.924) on LDCT.

Conclusions

The application of an AI-based automatic CAC scoring software to LDCT shows good to excellent reliability in CAC score and CAC severity categorization in multi-institutional datasets; however, the reliability varies among institutions.

Key Points

• AI-based automatic CAC scoring on LDCT shows excellent reliability with manual CAC scoring in multi-institutional datasets.

• The reliability for CAC score–based severity categorization varies among datasets.

• Automatic scoring for LDCT shows a higher false-positive rate than automatic scoring for CSCT, and most common causes of a false-positive are image noise and artifacts for both CSCT and LDCT.

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Abbreviations

AI:

Artificial intelligence

CAC:

Coronary artery calcium

CAC_auto:

Automatic coronary artery calcium scoring

CAC_man:

Manual coronary artery calcium scoring

CSCT:

Calcium scoring computed tomography

DL:

Deep learning

LAD:

Left anterior descending artery

LCX:

Left circumflex artery

LDCT:

Low-dose computed tomography

LM:

Left main artery

RCA:

Right coronary artery

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Funding

This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI18C0022); and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2018R1C1B6007251).

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Authors and Affiliations

  1. Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea

    Young Joo Suh

  2. Department of Radiology, Korea University Ansan Hospital, Ansan, South Korea

    Cherry Kim

  3. Biomedical Engineering Research Center, Asan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea

    June-Goo Lee

  4. Department of Radiology and Research Institute of Radiology, Cardiac Imaging Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea

    Hongmin Oh & Dong Hyun Yang

  5. Divison of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea

    Heejun Kang & Young-Hak Kim

Authors
  1. Young Joo Suh
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Corresponding author

Correspondence to Dong Hyun Yang.

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Guarantor

The scientific guarantor of this publication is Dong Hyun Yang.

Conflict of interest

June-Goo Lee owns stocks of Coreline Soft, Co. Ltd., a medical software company in South Korea. The other authors have no relationships to disclose relevant to the content of this paper.

Statistics and biometry

One of the authors (Heejun Kang) has significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• multicenter study

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Suh, Y.J., Kim, C., Lee, JG. et al. Fully automatic coronary calcium scoring in non-ECG-gated low-dose chest CT: comparison with ECG-gated cardiac CT. Eur Radiol 33, 1254–1265 (2023). https://doi.org/10.1007/s00330-022-09117-3

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  • DOI: https://doi.org/10.1007/s00330-022-09117-3

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