TY  - JOUR
AU  - Sumit K. Banchhor
AU  - Narendra D. Londhe
AU  - Tadashi Araki
AU  - Luca Saba
AU  - Petia Radeva
AU  - Narendra N. Khanna
AU  - Jasjit S. Suri
PY  - 2018//
TI  - Calcium detection, its quantification, and grayscale morphology-based risk stratification using machine learning in multimodality big data coronary and carotid scans: A review.
T2  - CBM
JO  - Computers in Biology and Medicine
SP  - 184
EP  - 198
VL  - 101
KW  - Heart disease
KW  - Stroke
KW  - Atherosclerosis
KW  - Intravascular
KW  - Coronary
KW  - Carotid
KW  - Calcium
KW  - Morphology
KW  - Risk stratification
N2  - Purpose of reviewAtherosclerosis 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.Recent findingCalcium 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.
UR  - https://doi.org/10.1016/j.compbiomed.2018.08.017
N1  - MILAB; no proj
ID  - Sumit K. Banchhor2018
ER  -