Site specific prediction of atherosclerotic plaque progression using computational biomechanics and machine learning

Artificial Intelligence in Cardiology and Atherosclerosis in the Context of Precision Medicine: A Scoping Review

“…Examinations of knee MRI of osteoarthritis patients [ 144 ] and standardized knee MRI [ 145 ] were a focus of a study of atherosclerosis development within the popliteal artery using ML. AI-based studies which investigated plaque distribution and composition predicted the plaque progression [ 146 , 147 ]. Table 4 presents studies that are included in this analysis.…”

“…However, the authors of this work aimed to present a broader point of view and included studies implementing AI in data analysis. For example, -Machine learning (random forest analysis) Li et al [127] -Human signaling networks, ClusterONE Yang et al [128] Cytoscape, MCODE Machine learning Wang et al [129] DAVID, SPSS Machine learning Tan et al [130] Cytoscape, MCODE Machine learning Zhang et al [131] Cytoscape, MCODE Machine learning Nai et al [132] Cytoscape, R package Machine learning Huang et al [134] Cytoscape Machine learning Yagi et al [135] GeneSpring Machine learning Liu et al [136] Cluster 3.0 genes, Python Machine learning Johno et al [137] -Machine learning Wei and Quan [138] DAVID Machine learning Wang et al [139] Clustering, DAVID, Cytoscape, MCODE Machine learning Wang et al [140] DAVID, R package, Cytoscape, MCODE Machine learning Adela et al [141] -Random forest analysis Canton et al [144] -Deep neural networks Chen et al [145] -Deep neural networks Jurtz et al [146] -Deep learning Kigka et al [147] -Machine learning Wang et al [148] -Machine learning Xu et al [149] -Machine learning Forrest et al [150] -Machine learning Yang et al [151] -Machine learning Sharma et al [152] -Machine learning Chen et al [153] -Machine learning Jones et al [154] -Machine learning Jiang et al [155] -Machine learning Applied Bionics and Biomechanics Depuydt et al [15] presented big data analysis with the R 3.5 environment and Seurat 3.0 [15,33]. "R" is a free software for statistical analysis and graphics and the R method is widely used in new-style AI, involving ML.…”

Artificial Intelligence in Cardiovascular Atherosclerosis Imaging

“…They evaluated 25 clinical and 44 CCTA parameters, and ML showed higher AUC than other models (segment stenosis score, segment involvement score, modified Duke Index, Framingham risk score). Han [ 75 ] and Kigka [ 76 ] used ML to predict the rapid development of coronary plaque, which was thought to be associated with cardiovascular events [ 77 , 78 ], revealing the prediction accuracy of 0.81 and 0.84, respectively. Table 1 displays the application of AI in coronary atherosclerotic plaque analysis.…”