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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

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Abstract

Objective

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).

Methods

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.

Results

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.

Conclusion

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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Abbreviations

AI-CADS:

Artificial intelligence coronary-assisted diagnosis system

AUC:

Area under the curve

CABG:

Coronary artery bypass grafting

CACs:

Coronary artery calcium score

CAD:

Coronary artery disease

CCTA:

Coronary computed tomography angiography

CI:

Confidence interval

ICA:

Invasive coronary angiography

NPV:

Negative predictive value

PCI:

Percutaneous coronary intervention

PPV:

Positive predictive value

ROC:

Receiver operating characteristic

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Acknowledgements

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).

Funding

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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Author notes

  1. Jie Xu and Linli Chen contributed equally to this work and should be considered co-authors.

Authors and Affiliations

  1. Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China

    Jie Xu, Linli Chen, Xiaojia Wu, Chuanming Li, Guangyong Ai, Yuexi Liu, Bitong Tian, Dajing Guo & Zheng Fang

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  1. Jie Xu
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Correspondence to Dajing Guo or Zheng Fang.

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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). https://doi.org/10.1007/s00330-021-08299-6

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