A Survey on Coronary Atherosclerotic Plaque Tissue Characterization in Intravascular Optical Coherence Tomography

Abstract

Atherosclerotic plaque components such as thin cap fibroatheroma (TCFA), fibrous cap, macrophage infiltration, large necrotic core, and thrombus are the microstructural plaque components that can be detected with only high-resolution imaging modalities such as intravascular ultrasound (IVUS) and optical coherence tomography (OCT). Light-based OCT provides better visualization of plaque tissue layers of coronary vessel walls as compared to IVUS. Three dominant paradigms have been identified to characterize atherosclerotic plaque components based on optical attenuation coefficients, machine learning algorithms, and deep learning techniques. This review (condensation of 126 papers after downloading 150 articles) presents a detailed comparison among various methodologies utilized for plaque tissue characterization, classification, and arterial measurements in OCT. Furthermore, this review presents the different ways to predict and stratify the risk associated with the CVD based on plaque characterization and measurements in OCT. Moreover, this review discovers three different paradigms for plaque characterization and their pros and cons. Among all of the techniques, a combination of machine learning and deep learning techniques is a best possible solution that provides improved OCT-based risk stratification.

COVID-19 pathways for brain and heart injury in comorbidity patients: A role of medical imaging and artificial intelligence-based COVID severity classification: A review

“…The highest fuzzy classifier accuracy achieved for the Portugal cohort was 93.1% , while the NBC and SVM-RBF kernel performed equally ( 85.3% ). Different methods for CVD risk stratification using the AI paradigm for plaque characterization have been published [ 27 , 28 , 158 ].…”

Atherosclerotic Plaques Recognition in Intracoronary Optical Images Using Neural Networks

“…When it comes to plaques detection in IOCT, the survey of Boi et al [8] describes that methods using texture information for plaque detection and classification performs with accuracy ranging between 80.41% and 88%. The recent paper of Gessert et al [6] report an accuracy of 92% for plaque detection using texture information.…”

“…Our hypothesis is that plaques are detectable using simply lumen contour information, manually or automatically delineated. To the best of our knowledge, this is the first paper to use only contour for plaque detection (Boi et al [8]).…”

A low-cost machine learning-based cardiovascular/stroke risk assessment system: integration of conventional factors with image phenotypes

“…However, CRF alone does not explain the elevated risk of CV/stroke events (4). This is because of the morphological variations in the atherosclerotic plaque that cannot be captured using CRF alone but which can easily be assessed using imaging modalities (5,6). Thus, there is a need to look beyond the scope of CRF and search for preventive healthcare solutions that can provide an accurate routine risk assessment at an affordable cost.…”