Stroke Risk Stratification and its Validation using Ultrasonic Echolucent Carotid Wall Plaque Morphology: A Machine Learning Paradigm

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• 作者：Tadashi Araki^aAuthor Vitae ; Pankaj K. Jain^bAuthor Vitae ; Harman S. Suri^cAuthor Vitae ; Narendra D. Londhe^dAuthor Vitae ; Nobutaka Ikeda^eAuthor Vitae ; Ayman El-Baz^f ; Vimal K. Shrivastava^dAuthor Vitae ; Luca Saba^gAuthor Vitae ; Andrew Nicolaides^h ; ⁱAuthor Vitae ; Shoaib Shafique^jAuthor Vitae ; John R. Laird^kAuthor Vitae ; Ajay Gupta^lAuthor Vitae ; Jasjit S. Suri^b ; ^c ; ^m ; ^{Jasjit.Suri@atheropoint.com}Author Vitae
• 关键词：Stroke ; Carotid wall ; Ultrasound ; Near ; Far ; Segmentation ; Machine learning ; Precision of merit ; ROC
• 刊名：Computers in Biology and Medicine
• 出版年：2017
• 出版时间：1 January 2017
• 年：2017
• 卷：80
• 期：Complete
• 页码：77-96
• 全文大小：2576 K
• 卷排序：80

文摘

Stroke risk stratification based on grayscale morphology of the ultrasound carotid wall has recently been shown to have a promise in classification of high risk versus low risk plaque or symptomatic versus asymptomatic plaques. In previous studies, this stratification has been mainly based on analysis of the far wall of the carotid artery. Due to the multifocal nature of atherosclerotic disease, the plaque growth is not restricted to the far wall alone. This paper presents a new approach for stroke risk assessment by integrating assessment of both the near and far walls of the carotid artery using grayscale morphology of the plaque. Further, this paper presents a scientific validation system for stroke risk assessment. Both these innovations have never been presented before.The methodology consists of an automated segmentation system of the near wall and far wall regions in grayscale carotid B-mode ultrasound scans. Sixteen grayscale texture features are computed, and fed into the machine learning system. The training system utilizes the lumen diameter to create ground truth labels for the stratification of stroke risk. The cross-validation procedure is adapted in order to obtain the machine learning testing classification accuracy through the use of three sets of partition protocols: (5, 10, and Jack Knife).The mean classification accuracy over all the sets of partition protocols for the automated system in the far and near walls is 95.08% and 93.47%, respectively. The corresponding accuracies for the manual system are 94.06% and 92.02%, respectively. The precision of merit of the automated machine learning system when compared against manual risk assessment system are 98.05% and 97.53% for the far and near walls, respectively. The ROC of the risk assessment system for the far and near walls is close to 1.0 demonstrating high accuracy.