A novel computerized tool to stratify risk in carotid atherosclerosis using kinematic features of the arterial wall

Abstract

Valid characterization of carotid atherosclerosis (CA) is a crucial public health issue, which would limit the major risks held by CA for both patient safety and state economies. This paper investigated the unexplored potential of kinematic features in assisting the diagnostic decision for CA in the framework of a computer-aided diagnosis (CAD) tool. To this end, 15 CAD schemes were designed and were fed with a wide variety of kinematic features of the atherosclerotic plaque and the arterial wall adjacent to the plaque for 56 patients from two different hospitals. The CAD schemes were benchmarked in terms of their ability to discriminate between symptomatic and asymptomatic patients and the combination of the Fisher discriminant ratio, as a feature-selection strategy, and support vector machines, in the classification module, was revealed as the optimal motion-based CAD tool. The particular CAD tool was evaluated with several cross-validation strategies and yielded higher than 88% classification accuracy; the texture-based CAD performance in the same dataset was 80%. The incorporation of kinematic features of the arterial wall in CAD seems to have a particularly favorable impact on the performance of image-data-driven diagnosis for CA, which remains to be further elucidated in future prospective studies on large datasets.

A survey on artificial intelligence in pulmonary imaging

“…The first application of AI in healthcare can be traced to the early 1970s, when a rule‐based system, referred to as MYCIN, was developed to assist physicians to select appropriate therapies for patients with bacterial infections (Shortliffe, 1974). Throughout the 1980s and 1990s, the proliferation of AI in healthcare and medical imaging research continued, and computer‐aided diagnosis and/or detection (CAD) emerged as a prominent research area in cancer detection (Giger & Suzuki, 2008; Jiang et al, 1999; van Ginneken et al, 2001), cardiovascular disease and congenital heart defects (Gastounioti et al, 2013, 2014; Golemati et al, 2013; Golemati & Nikita, 2019), pathological brain detection (Chaplot et al, 2006; Maitra & Chatterjee, 2006), Alzheimer's disease (Zhang et al, 2014; Zhang, Dong, et al, 2015), and diabetic retinopathy (Ahmad et al, 2014; Tufail et al, 2017). Simultaneously, data mining approaches (Jothi et al, 2015; Tomar & Agarwal, 2013; Yoo et al, 2012) drew research interest in various application areas including risk assessment (Patel et al, 2009), modeling disease pathways and progression (Hauskrecht & Fraser, 2000), genome analysis (Ferreira, 2006), and treatment planning (Holmes et al, 2004; Patel et al, 2009).…”