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Cited by 10Cardiac Imaging and DiagnosticsCoronary Interventions and DiagnosticsArtificial Intelligence in Healthcare

Deep learning-based coronary artery calcium score to predict coronary artery disease in type 2 diabetes mellitus

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Hu, Jingcheng, Hao, Guangyu, Xu, Jialiang, Wang, Ximing, Chen, Meng·2024·Heliyon
DOI 10.1016/j.heliyon.2024.e27937PMC PMC10944251
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Abstract

Background Coronary artery disease (CAD) in type 2 diabetes mellitus (T2DM) patients often presents diffuse lesions, with extensive calcification, and it is time-consuming to measure coronary artery calcium score (CACS). Objectives To explore the predictive ability of deep learning (DL)-based CACS for obstructive CAD and hemodynamically significant CAD in T2DM. Methods 469 T2DM patients suspected of CAD who accepted CACS scan and coronary CT angiography between January 2013 and December 2020 were enrolled. Obstructive CAD was defined as diameter stenosis ≥50%. Hemodynamically significant CAD was defined as CT-derived fractional flow reserve ≤0.8. CACS was calculated with a fully automated method based on DL algorithm. Logistic regression was applied to determine the independent predictors. The predictive performance was evaluated with area under receiver operating characteristic curve (AUC). Results DL-CACS (adjusted odds ratio (OR): 1.005; 95% CI: 1.003–1.006; P ＜ 0.001) was significantly associated with obstructive CAD. DL-CACS (adjusted OR:1.003; 95% CI: 1.002–1.004; P ＜ 0.001) was also an independent predictor for hemodynamically significant CAD. The AUCs, sensitivities, specificities, positive predictive values and negative predictive values of DL-CACS for obstructive CAD and hemodynamically significant CAD were 0.753 (95% CI: 0.712–0.792), 75.9%, 66.5%, 74.8%, 67.8% and 0.769 (95% CI: 0.728–0.806), 80.7%, 62.1%, 59.6% and 82.3% respectively. It took 1.17 min to perform automated measurement of DL-CACS in total, which was significantly less than manual measurement of 1.73 min ( P ＜ 0.001). Conclusions DL-CACS, with less time-consuming, can accurately and effectively predict obstructive CAD and hemodynamically significant CAD in T2DM.

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Authors
Hu, J., Hao, G., Xu, J., Wang, X., & Chen, M.
Affiliations
First Affiliated Hospital of Soochow University (CN)
Corresponding author
Chen, Meng
ORCID
Venue
Heliyon
Publisher
Elsevier BV
Published
2024-03
Volume
10
Issue
6
Pages
e27937
ISSN
2405-8440 (ISSN-L)
DOI
10.1016/j.heliyon.2024.e27937
PMC
PMC10944251
OpenAlex
W4392648815
Cited by
10
References
31
Citations by year
2026: 3 · 2025: 6 · 2024: 1
FWCI
2.55
Type
journal article
Language
en
Open access
Licence
Locations
  • Heliyon (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Suzhou Municipal Health Commission
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
  • Health Sciences › Medicine › Surgery › Coronary Interventions and Diagnostics
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
coronary artery calcium score, type 2 diabetes, coronary artery disease, deep learning, obstructive coronary artery disease, coronary CT angiography, CT-derived fractional flow reserve, coronary artery calcification

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