Atherosclerotic Burden and Remodeling Patterns of the Popliteal Artery as Detected in the Magnetic Resonance Imaging Osteoarthritis Initiative Data Set

Abstract

Background An artificial intelligence vessel segmentation tool, Fully Automated and Robust Analysis Technique for Popliteal Artery Evaluation (FRAPPE), was used to analyze a large databank of popliteal arteries imaged through the OAI (Osteoarthritis Initiative) to study the impact of atherosclerosis risk factors on vessel dimensions and characterize remodeling patterns. Methods and Results Magnetic resonance images from 4668 subjects contributing 9189 popliteal arteries were analyzed using FRAPPE. Age ranged from 45 to 79 years (median, 61), and 58% were women. Mean lumen diameter, mean outer wall diameter, and mean wall thickness (MWT) were measured per artery. Their median values were 5.8 mm (interquartile range, 5.2–6.5 mm), 7.3 mm (interquartile range, 6.7–8.1 mm), and 0.78 mm (interquartile range, 0.73–0.84 mm) respectively. MWT was associated with multiple cardiovascular risk factors, with age (4.2% increase in MWT per 10‐year increase in age; 95% CI, 3.9%–4.5%) and sex (8.6% higher MWT in men than women; 95% CI, 7.7%–9.3%) being predominant. On average, lumen and outer wall diameters increased with increasing MWT until the thickness was 0.92 mm for men and 0.84 mm for women. After this point, lumen diameter decreased steadily, more rapidly in men than women (−7.9% versus −6.1% per 25% increase in MWT; P <0.001), with little change in outer wall diameter. Conclusions FRAPPE has enabled the analysis of the large OAI knee magnetic resonance imaging data set, successfully showing that popliteal atherosclerosis is predominantly associated with age and sex. The average vessel remodeling pattern consisted of an early phase of compensatory enlargement, followed by a negative remodeling, which is more pronounced in men.

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

“…The role of the opportunistic imaging concept has also been raised in the literature. 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 ].…”

“…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.…”

Abdominal aortic diameter as a predictor of peripheral atherosclerosis progression in 40-64-year-old patients with cardiovascular risk factors

“…Всем пациентам проводили дуплексное сканирование артерий каротидного бассейна и артерий нижних конечностей, брюшной аорты. Критериями прогрессирования субклинического МФА являлись: 1) появление новой атеросклеротической бляшки (АСБ); 2) увеличение степени стенозирования ранее имеющегося стеноза ≥ 10%; 3) увеличение суммарной площади каротидных АСБ более чем на 0,106 см 2 . Анализ полученных данных проводили с использованием пакетов статистического анализа данных MedCalc (версия 20.215) и IBM SPSS Statistics (версия 18).…”

“…В настоящее время известно, что традиционные факторы риска (ФР), детерминирующие развитие атеросклероза и связанных с ним заболеваний, оказывают воздействие на процессы ремоделирования артерий, в т. ч. брюшной аорты (БА), с увеличением их диаметра [1,2]. Активно исследуется роль атеросклероза в увеличении диаметра БА и формировании аневризмы БА, однако данные о взаимосвязи между диаметром БА и атеросклерозом других сосудистых бассейнов противоречивы [2−4].…”

An Analysis of the Influence of Transfer Learning When Measuring the Tortuosity of Blood Vessels

“…Therefore, many ailments and developmental disorders may be caused by abnormal blood vessels [1]. Thus, characterizing blood vessels is an important matter not only for diagnosis but also to help answer important research questions regarding angiogenesis [2,3], blood vessel related ailments [4] and the bloodbrain barrier [3,5]. Common metrics for characterizing blood vessels are density and tortuosity since they have been shown to influence neuronal activation [6] and blood flow [7].…”