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Do plaque-related factors affect the diagnostic performance of an artificial intelligence coronary-assisted diagnosis system? Comparison with invasive coronary angiography



The aim of this study was to investigate the effects of plaque-related factors on the diagnostic performance of an artificial intelligence coronary-assisted diagnosis system (AI-CADS).


Patients who underwent coronary computed tomography angiography (CCTA) and invasive coronary angiography (ICA) were retrospectively included in this study. The degree of stenosis in each vessel was collected from CCTA and ICA, and the information on plaque-related factors (plaque length, plaque type, and coronary artery calcium score (CAC)) of the vessels with plaques was collected from CCTA.


In total, 1224 vessels in 306 patients (166 men; 65.7 ± 10.1 years) were analyzed. Of these, 391 vessels in 249 patients showed significant stenosis using ICA as the gold standard. Using per-vessel as the unit, the area under the curves of coronary stenosis ≥ 50% for AI-CADS, doctor, and AI-CADS + doctor was 0.764, 0.837, and 0.853, respectively. The accuracies in interpreting the degree of coronary stenosis were 56.0%, 68.1%, and 71.2%, respectively. Seven hundred fifty vessels showed plaques on CCTA; plaque type did not affect the interpretation results by AI-CADS (chi-square test: p = 0.0093; multiple logistic regression: p = 0.4937). However, the interpretation results for plaque length (chi-square test: p < 0.0001; multiple logistic regression: p = 0.0061) and CACs (chi-square test: p < 0.0001; multiple logistic regression: p = 0.0001) were significantly different.


AI-CADS has an ability to distinguish ≥ 50% coronary stenosis, but additional manual interpretation based on AI-CADS is necessary. The plaque length and CACs will affect the diagnostic performance of AI-CADS.

Key Points

• AI-CADS can help radiologists quickly assess CCTA and improve diagnostic confidence.

• Additional manual interpretation on the basis of AI-CADS is necessary.

• The plaque length and CACs will affect the diagnostic performance of AI-CADS.

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Artificial intelligence coronary-assisted diagnosis system


Area under the curve


Coronary artery bypass grafting


Coronary artery calcium score


Coronary artery disease


Coronary computed tomography angiography


Confidence interval


Invasive coronary angiography


Negative predictive value


Percutaneous coronary intervention


Positive predictive value


Receiver operating characteristic


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The authors are grateful to American Journal Experts (AJE) for their assistance with language editing. This study was supported by the Kuanren Talents Program of the Second Affiliated Hospital of Chongqing Medical University (2020-7, 2021-24).


This study has received funding from the Kuanren Talents Program of the second affiliated hospital of Chongqing Medical University (2020–7, 2021–24).

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Correspondence to Dajing Guo or Zheng Fang.

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The scientific guarantor of this publication is Dajing Guo.

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Xu, J., Chen, L., Wu, X. et al. Do plaque-related factors affect the diagnostic performance of an artificial intelligence coronary-assisted diagnosis system? Comparison with invasive coronary angiography. Eur Radiol 32, 1866–1878 (2022).

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  • Computed tomography angiography
  • Artificial intelligence
  • Coronary stenosis