Value of machine learning in preliminary assessment of the degree of coronary artery stenosis caused by different types of plaques

Abstract

Objective To explore the application value of machine learning in preliminary evaluation of the degree of co-ronary artery stenosis caused by different types of plaques. Methods Eighty patients who underwent coronary CT angiography (CCTA) and coronary angiography (CAG) in the following 14 d from January 2020 to October 2022 were selected. During CCTA, 103 coronary artery stenosis sites were randomly selected and divided into calcified plaque group (38 sites), non-calcified plaque group (34 sites), and mixed plaque group (31 sites) according to plaque properties. Subjective evaluation (SA), post-processing workstation measurement (AW), artificial intelligence (AI), and SA combined with AI (Semi-AI) were used to assess the degree of coronary artery stenosis caused by plaques in each group. CAG results were used as the gold standard for the degree of coronary artery stenosis. The coincidence, underestimation, and overestimation rates were calculated based on the gold standard and compared between the four methods. Results Among the four methods, there were no significant differences in the coincidence rate, underestimation rate, and overestimation rate between AI and SA (P>0.008 3). In the evaluation of non-calcified plaque and mixed plaque, the coincidence rate of AI was significantly higher than those of AW and Semi-AI (χ2=7.65-16.20,P<0.008 3). In the evaluation of calcified plaque, the coincidence rate of AI was not significantly different from those of the other three methods (P>0.05). In the evaluation of calcified plaque and mixed plaque, the overestimation rate of Semi-AI was significantly lower than those of the other three methods (χ2=8.77-23.62,P<0.008 3). Conclusion AI can partly replace the subjective evaluation made by radiologists regarding coronary artery stenosis caused by different types of plaques, thus optimizing the evaluation process of coronary artery stenosis. The Semi-AI method can reduce the overestimation of coronary artery stenosis caused by various types of plaques. However, AI cannot be used as a gold standard, and can only be used to preliminarily evaluate the degree of coronary artery stenosis.

Keywords

• artificial intelligence|machine learning|computed tomography angiography|coronary stenosis|plaque, atherosclerotic