Feasibility Study of Assessing the Efficacy of Coronary Atherosclerosis Based on CCTA-Derived Artificial Intelligence Measurement of Pericoronary Fat Attenuation Index(Pericoronary FAI)
Feasibility Study of Assessing the Efficacy of Coronary Atherosclerosis Based on CCTA-Derived Artificial Intelligence Measurement of Pericoronary Fat Attenuation Index(Pericoronary FAI)
Objective The aim of this study was to assess the efficacy of coronary atherosclerosis based on the CCTA-derived artificial intelligence measure of pericoronary fat attenuation index(pericoronary FAI),and to investigate whether it can provide diagnostic and therapeutic evidence of prognostic conditions of patients with coronary artery disease(CAD).Methods CAD patients treated in Baotou Central Hospital 2022 were enrolled as study subjects.They were divided into six groups according to treatment methods and efficacy.Coronary computed tomography angiography(CCTA)was performed to meet the criteria for natriuresis.Patients'baseline data and clinical indicators were collected.Two physicians with the title of attending physician or above performed image post-processing using CT work software,and then measured and recorded pericoronary FAI using the FAI intelligent analysis system of Skview software.Pearson's correlation analysis and rank correlation analysis were used to investigate the correlation between peri-coronary FAI andthe indexes.Results The post-processing results of CCTA images in this study showed that the totality pericoronary FAI was-83.10±10.46 HU,and the pericoronary FAI of the six groups were-89.71±5.56 HU,-71.35±4.39 HU,-91.94±3.97 HU,-71.62±4.79 HU,-89.38±3.07 HU,-71.69±3.79 HU.PCI efficacy results were stronger thandrug and CABG efficacy.Treatment methods and efficacy profiles showed differentcorrelations with clinical indicators.Conclusion CCTA-derived artificial intelligence-based measurement of pericoronary FAI enables assessment of coronary atherosclerosis efficacy and identification of categorized residual inflammatory risk(RIR),thus helping to provide clinical insight into the prognosis of patients with CAD.
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