Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2026
Evaluation of different artificial intelligence-based coronary artery calcium scoring algorithms in nongated chest computed tomography.
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Li Ting, Xu Xiaodie, Hu Keqing, Li Kun, Sun Xiao, Su Guohai, Song Peiji
Аннотация
BACKGROUND: To compare the accuracy of coronary artery calcium scores (CACSs) automatically measured by two artificial intelligence (AI) systems on nonelectrocardiogram (ECG)-gated chest computed tomography (CT) and to investigate the relationship between CACS and the degree of coronary artery stenosis.
MATERIALS AND METHODS: A retrospective study was conducted on 182 patients who underwent both ECG-gated Coronary computed tomography angiography (CTA) and non-ECG-gated chest CT. Images from all patients were processed using postprocessing software and two AI systems (Software A/B). The manually measured CACS by experienced physicians using ECG-gated Coronary CTA was used as the gold standard. The agreement between two AI systems and the gold standard was assessed using the intraclass correlation coefficient (ICC) and Bland-Altman analysis. In addition, Spearman's correlation analysis was performed to evaluate the relationship between CACS and the degree of coronary artery stenosis.
RESULTS: Both AI systems demonstrated high ICCs for CACS measurement (0.90, 95% confidence interval [CI]: 0.871-0.926 for Software A; 0.96, 95% CI: 0.943-0.968 for Software B). The mean differences (95% limits of agreement) were 23.59 ± 442.35 for Software A and -80.35 ± 292.99 for Software B. Both systems showed good agreement in CACS measurement, with Software B exhibiting higher accuracy than Software A. Spearman's correlation analysis revealed weak-to-moderate positive correlations between CACS measured by different methods and the degree of coronary artery stenosis (r = 0.151-0.560, P < 0.05) across all coronary branches.
CONCLUSION: The two AI-based CACS systems, when applied to nongated chest CT, demonstrated consistency with manually measured CACS from ECG-gated coronary CTA. Software B showed superior accuracy compared to Software A. The correlation between CACS and the degree of vascular stenosis is influenced by vascular remodeling patterns, with positive remodeling reducing its consistency. Therefore, a comprehensive assessment of vascular stenosis should integrate vascular remodeling patterns and other diagnostic indicators.
MATERIALS AND METHODS: A retrospective study was conducted on 182 patients who underwent both ECG-gated Coronary computed tomography angiography (CTA) and non-ECG-gated chest CT. Images from all patients were processed using postprocessing software and two AI systems (Software A/B). The manually measured CACS by experienced physicians using ECG-gated Coronary CTA was used as the gold standard. The agreement between two AI systems and the gold standard was assessed using the intraclass correlation coefficient (ICC) and Bland-Altman analysis. In addition, Spearman's correlation analysis was performed to evaluate the relationship between CACS and the degree of coronary artery stenosis.
RESULTS: Both AI systems demonstrated high ICCs for CACS measurement (0.90, 95% confidence interval [CI]: 0.871-0.926 for Software A; 0.96, 95% CI: 0.943-0.968 for Software B). The mean differences (95% limits of agreement) were 23.59 ± 442.35 for Software A and -80.35 ± 292.99 for Software B. Both systems showed good agreement in CACS measurement, with Software B exhibiting higher accuracy than Software A. Spearman's correlation analysis revealed weak-to-moderate positive correlations between CACS measured by different methods and the degree of coronary artery stenosis (r = 0.151-0.560, P < 0.05) across all coronary branches.
CONCLUSION: The two AI-based CACS systems, when applied to nongated chest CT, demonstrated consistency with manually measured CACS from ECG-gated coronary CTA. Software B showed superior accuracy compared to Software A. The correlation between CACS and the degree of vascular stenosis is influenced by vascular remodeling patterns, with positive remodeling reducing its consistency. Therefore, a comprehensive assessment of vascular stenosis should integrate vascular remodeling patterns and other diagnostic indicators.