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. 2024 Mar 10;10(6):e27937.
doi: 10.1016/j.heliyon.2024.e27937. eCollection 2024 Mar 30.

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

Jingcheng Hu  1 , Guangyu Hao  2 , Jialiang Xu  3 , Ximing Wang  2 , Meng Chen  2
Affiliations

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

Jingcheng Hu et al. Heliyon. .

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.

Keywords: Coronary artery calcium score; Coronary artery disease; Deep learning; Prediction; Type 2 diabetes mellitus.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Flow chart.
Fig. 2
Fig. 2
Automated identification of calcified regions in coronary arteries and results presentation of DL-CACS. DL-CACS, deep learning-based coronary artery calcium score.
Fig. 3
Fig. 3
ROC curves of DL-CACS for obstructive CAD (AUC: 0.753, 3A) and hemodynamically significant CAD (AUC: 0.769, 3B). ROC, receiver operating characteristic; DL-CACS, deep learning-based coronary artery calcium score; CAD, coronary artery disease; AUC, area under ROC curve.
Fig. 4
Fig. 4
Representative case of a 64 years old male showed obstructive stenosis (4A: LAD; 4B: LCX; 4C: RCA) and hemodynamically significant stenosis (4D) in LAD and LCX. DL-CACS of the patient was 333.18. CAD, coronary artery disease; LAD, left anterior descending calcium; LCX, left circumflex calcium; RCA, right coronary artery calcium; DL-CACS, deep learning-based coronary artery calcium score.
See this image and copyright information in PMC

References

    1. GBD 2017 Disease and Injury Incidence and Prevalence Collaborators Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392:1789–1858. doi: 10.1016/S0140-6736(18)32279-7. - DOI - PMC - PubMed
    1. Shi R., Gao Y., Shen L.L., Shi K., Wang J., Jiang L., Li Y., Yang Z.G. The effect of LDL-C status on the association between increased coronary artery calcium score and compositional plaque volume progression in statins-treated diabetic patients: evaluated using serial coronary CTAs. Cardiovasc. Diabetol. 2022;21:121. doi: 10.1186/s12933-022-01556-y. - DOI - PMC - PubMed
    1. Godoy L.C., Ko D.T., Rao V., Farkouh M.E. The role of coronary artery bypass surgery versus percutaneous intervention in patients with diabetes and coronary artery disease. Prog. Cardiovasc. Dis. 2019;62:358–363. doi: 10.1016/j.pcad.2019.07.004. - DOI - PubMed
    1. Kosmopoulos M., Drekolias D., Zavras P.D., Piperi C., Papavassiliou A.G. Impact of advanced glycation end products (AGEs) signaling in coronary artery disease. Biochim. Biophys. Acta, Mol. Basis Dis. 2019;1865:611–619. doi: 10.1016/j.bbadis.2019.01.006. - DOI - PubMed
    1. Yahagi K., Kolodgie F.D., Lutter C., Mori H., Romero M.E., Finn A.V., Virmani R. Pathology of human coronary and carotid artery atherosclerosis and vascular calcification in diabetes mellitus. Arterioscler. Thromb. Vasc. Biol. 2017;37:191–204. doi: 10.1161/ATVBAHA.116.306256. - DOI - PMC - PubMed

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