A new method for IVUS-based coronary artery disease risk stratification: A link between coronary & carotid ultrasound plaque burdens

•Coronary risk stratification.•Machine learning to link two arteries.•56 grayscale features.•Coronary tissue characterization.•Classification accuracy 94.95% and AUC 0.95. Interventional cardiologists have a deep interest in risk stratification prior to stenting and percutaneous coronary interventio...

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Vydáno v:	Computer Methods and Programs in Biomedicine Ročník 124; s. 161 - 179
Hlavní autoři:	Araki, Tadashi, Ikeda, Nobutaka, Shukla, Devarshi, Londhe, Narendra D., Shrivastava, Vimal K., Banchhor, Sumit K., Saba, Luca, Nicolaides, Andrew, Shafique, Shoaib, Laird, John R., Suri, Jasjit S.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Ireland Elsevier Ireland Ltd 01.02.2016 Elsevier BV
Témata:	Adult Aged Aged, 80 and over Atherosclerosis B-mode ultrasound B-mode ultrasound; Carotid disease; Coronary artery; IVUS; Machine learning; Risk assessment; Computer Science Applications1707 Computer Vision and Pattern Recognition; Software; Health Informatics Carotid Artery Diseases Carotid Artery Diseases - complications Carotid Artery Diseases - diagnostic imaging Carotid disease Carotid Intima-Media Thickness Coronary artery Coronary Artery Disease Coronary Artery Disease - diagnostic imaging Coronary Artery Disease - etiology Female Humans Image Interpretation, Computer-Assisted Image Interpretation, Computer-Assisted - methods Internal Medicine IVUS Links Machine Learning Male Middle Aged Morphology Other Patients Pattern Recognition, Automated Pattern Recognition, Automated - methods Reproducibility of Results Risk Risk Assessment Risk Assessment - methods Sensitivity and Specificity Stratification Support vector machines Ultrasonography, Interventional Ultrasonography, Interventional - methods Ultrasound IVUS B-mode ultrasound Risk assessment Coronary artery Carotid disease Machine learning
ISSN:	0169-2607, 1872-7565
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Popis
Shrnutí:	•Coronary risk stratification.•Machine learning to link two arteries.•56 grayscale features.•Coronary tissue characterization.•Classification accuracy 94.95% and AUC 0.95. Interventional cardiologists have a deep interest in risk stratification prior to stenting and percutaneous coronary intervention (PCI) procedures. Intravascular ultrasound (IVUS) is most commonly adapted for screening, but current tools lack the ability for risk stratification based on grayscale plaque morphology. Our hypothesis is based on the genetic makeup of the atherosclerosis disease, that there is evidence of a link between coronary atherosclerosis disease and carotid plaque built up. This novel idea is explored in this study for coronary risk assessment and its classification of patients between high risk and low risk. This paper presents a strategy for coronary risk assessment by combining the IVUS grayscale plaque morphology and carotid B-mode ultrasound carotid intima-media thickness (cIMT) – a marker of subclinical atherosclerosis. Support vector machine (SVM) learning paradigm is adapted for risk stratification, where both the learning and testing phases use tissue characteristics derived from six feature combinational spaces, which are then used by the SVM classifier with five different kernels sets. These six feature combinational spaces are designed using 56 novel feature sets. K-fold cross validation protocol with 10 trials per fold is used for optimization of best SVM-kernel and best feature combination set. IRB approved coronary IVUS and carotid B-mode ultrasound were jointly collected on 15 patients (2 days apart) via: (a) 40MHz catheter utilizing iMap (Boston Scientific, Marlborough, MA, USA) with 2865 frames per patient (42,975 frames) and (b) linear probe B-mode carotid ultrasound (Toshiba scanner, Japan). Using the above protocol, the system shows the classification accuracy of 94.95% and AUC of 0.95 using optimized feature combination. This is the first system of its kind for risk stratification as a screening tool to prevent excessive cost burden and better patients’ cardiovascular disease management, while validating our two hypotheses.
Bibliografie:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Undefined-1 ObjectType-Feature-3 content type line 23 ObjectType-Article-1 ObjectType-Feature-2
ISSN:	0169-2607 1872-7565
DOI:	10.1016/j.cmpb.2015.10.022