Key result
A computer-aided diagnostic technique using an SVM classifier achieved 90.66% accuracy, 83.33% sensitivity, and 95.39% specificity in classifying symptomatic and asymptomatic carotid plaques.
Why the study?
Does a computer-aided diagnostic technique using SVM and ultrasound features accurately classify symptomatic and asymptomatic carotid plaques?
Does a computer-aided diagnostic technique using SVM and ultrasound features accurately classify symptomatic and asymptomatic carotid plaques?
A machine learning approach using ultrasound texture features and degree of stenosis can accurately classify symptomatic and asymptomatic carotid plaques, potentially aiding in surgical decision-making.
Answers are grounded in this paper and cite their sources.
May aid SVM-based carotid plaque classification; leaves open prospective validation before clinical adoption.
The selection of carotid atherosclerosis patients for surgery or stenting is a crucial task in atherosclerosis disease management. In order to select only those symptomatic cases who need surgery, we have, in this work, presented a computer-aided diagnostic technique to effectively classify symptomatic and asymptomatic plaques from B-mode ultrasound carotid images. We extracted several grayscale features that quantify the textural differences inherent in the manually delineated plaque regions and selected the most significant among these extracted features. These features, along with the degree of stenosis (DoS), were used to train and test a support vector machine (SVM) classifier using threefold stratified cross-validation using a data set consisting of 160 (50 symptomatic and 110 asymptomatic) images. Using 32 features in an SVM classifier with a polynomial kernel of order 1, we obtained the best accuracy of 90.66%, sensitivity of 83.33%, and specificity of 95.39%. The DoS was found to be a valuable feature in addition to other texture-based features. We have also proposed the plaque risk index (PRI) made up of a combination of significant features such that thePRIhas unique ranges for both plaque classes. PRI can be used in monitoring the variations in features over a period of time which will provide evidence on how and which features change as asymptomatic plaques become symptomatic.
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Acharya et al. (2012) studied Carotid atherosclerosis (n=160). Computer-aided diagnostic technique using SVM classifier was evaluated on Classification of symptomatic and asymptomatic plaques. A computer-aided diagnostic technique using an SVM classifier achieved 90.66% accuracy, 83.33% sensitivity, and 95.39% specificity in classifying symptomatic and asymptomatic carotid plaques.
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