Calcium detection, its quantification, and grayscale morphology-based risk stratification using machine learning in multimodality big data coronary and carotid scans: A review

Atherosclerosis is the leading cause of cardiovascular disease (CVD) and stroke. Typically, atherosclerotic calcium is found during the mature stage of the atherosclerosis disease. It is therefore often a challenge to identify and quantify the calcium. This is due to the presence of multiple compone...

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Bibliographic Details
Published in:Computers in Biology and Medicine Vol. 101; pp. 184 - 198
Main Authors: Banchhor, Sumit K., Londhe, Narendra D., Araki, Tadashi, Saba, Luca, Radeva, Petia, Khanna, Narendra N., Suri, Jasjit S.
Format: Journal Article
Language:English
Published: United States Elsevier Ltd 01.10.2018
Elsevier BV
Elsevier Limited
Subjects:
Arteries
Arteriosclerosis
Atherosclerosis
Atherosclerosis; Calcium; Carotid; Coronary; Heart disease; Intravascular; Morphology; Risk stratification; Stroke; Computer Science Applications1707 Computer Vision and Pattern Recognition; Health Informatics
Big Data
Blood clots
Calcification
Calcium
Cardiology
Cardiovascular disease
Cardiovascular diseases
Carotid
Carotid Arteries
Carotid artery
Carotid Artery Diseases
Cholesterol
Coronary
Coronary artery disease
Coronary vessels
Data management
Heart attacks
Heart disease
Heart diseases
Humans
Image Interpretation, Computer-Assisted
Image resolution
Intravascular
Learning algorithms
Lipids
Machine Learning
Mathematical models
Medical imaging
Morphology
Multimodal Imaging
Optical Coherence Tomography
Plaque, Atherosclerotic
Risk
Risk Assessment
Risk stratification
State-of-the-art reviews
Statistical analysis
Statistical models
Stroke
Systems analysis
Ultrasonography, Interventional
Ultrasound
Stroke
Intravascular
Calcium
Heart disease
Carotid
Morphology
Atherosclerosis
Risk stratification
Coronary
ISSN:0010-4825, 1879-0534, 1879-0534
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Summary:Atherosclerosis is the leading cause of cardiovascular disease (CVD) and stroke. Typically, atherosclerotic calcium is found during the mature stage of the atherosclerosis disease. It is therefore often a challenge to identify and quantify the calcium. This is due to the presence of multiple components of plaque buildup in the arterial walls. The American College of Cardiology/American Heart Association guidelines point to the importance of calcium in the coronary and carotid arteries and further recommend its quantification for the prevention of heart disease. It is therefore essential to stratify the CVD risk of the patient into low- and high-risk bins. Calcium formation in the artery walls is multifocal in nature with sizes at the micrometer level. Thus, its detection requires high-resolution imaging. Clinical experience has shown that even though optical coherence tomography offers better resolution, intravascular ultrasound still remains an important imaging modality for coronary wall imaging. For a computer-based analysis system to be complete, it must be scientifically and clinically validated. This study presents a state-of-the-art review (condensation of 152 publications after examining 200 articles) covering the methods for calcium detection and its quantification for coronary and carotid arteries, the pros and cons of these methods, and the risk stratification strategies. The review also presents different kinds of statistical models and gold standard solutions for the evaluation of software systems useful for calcium detection and quantification. Finally, the review concludes with a possible vision for designing the next-generation system for better clinical outcomes. •Different modalities were presented for calcium detection and its quantification.•Speed issues were presented in the form of multiresolution paradigms.•Learning-based risk stratification studies were discussed.•Different methods were presented for performance evaluation and validation.
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ISSN:0010-4825
1879-0534
1879-0534
DOI:10.1016/j.compbiomed.2018.08.017