Factors affecting the performance of a novel artificial intelligence-based coronary computed tomography-derived ischaemia algorithm

影响新型基于人工智能的冠状动脉计算机断层扫描衍生缺血算法性能的因素

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Abstract

AIMS: AI-QCT(ischaemia) is an FDA-cleared novel artificial intelligence-guided method that utilizes features from coronary computed tomography angiography (CCTA) to predict myocardial ischaemia. OBJECTIVE: To identify factors associated with discrepancy between AI-QCT(ischaemia) and positron emission tomography (PET) perfusion. METHODS AND RESULTS: Six hundred and sixty-two patients with suspected obstructive coronary artery disease (CAD) on CCTA and undergoing [(15)O]H(2)O PET were analysed using AI-QCT(ischaemia). Multivariable logistic regression identified factors associated with discrepancy. Perfusion homogeneity was measured by relative flow reserve. A total of 209 (32%) patients showed discrepancies: 62 (9%) exhibited normal AI-QCT(ischaemia) but abnormal perfusion (false negative AI-QCT(ischaemia)), whereas 147 (22%) had abnormal AI-QCT(ischaemia) despite normal perfusion (false positive AI-QCT(ischaemia)). False positive AI-QCT(ischaemia) patients (vs. true positive) were more often females, older, with less typical angina, and less advanced CAD. In multivariable analysis, typical angina [OR 95% CI: 1.796 (1.015-3.179), P = 0.044], diameter stenosis per 1% increase [1.058 (1.036-1.080), P < 0.001], and percent atheroma volume per 1% increase [1.103 (1.051-1.158), P < 0.001] significantly predicted true positive, while age was inversely associated [0.955 (0.923-0.989), P = 0.010]. False-negative AI-QCT(ischaemia) patients (vs. true negative) were more often males, smokers, with less good CCTA image quality, and more advanced CAD. However, none was significant in multivariable analysis. Furthermore, false-negative AI-QCT(ischaemia) showed more homogenously reduced perfusion by relative flow reserve compared to true positive (median ± IQR: 0.68 ± 0.15 vs. 0.56 ± 0.23, P < 0.001) and 21 (34%) of false negative showed globally reduced perfusion. CONCLUSION: For abnormal AI-QCT(ischaemia), younger age, typical angina, more severe stenosis, and more extensive atherosclerosis predicted abnormal PET perfusion. With false negative AI-QCT(ischaemia), perfusion abnormalities were partly explained by microvascular disease.

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