Key result
The Atheromatic CAD system using an SVM classifier with an RBF kernel classified carotid ultrasound images as symptomatic or asymptomatic with 91.7% accuracy, 97% sensitivity, and 80% specificity.
Why the study?
Does the Atheromatic CAD system accurately classify carotid ultrasound plaques as symptomatic or asymptomatic?
Does the Atheromatic CAD system accurately classify carotid ultrasound plaques as symptomatic or asymptomatic?
A novel computer-aided diagnosis system using ultrasound image features and an SVM classifier achieved 91.7% accuracy in distinguishing symptomatic from asymptomatic carotid plaques.
Supports AI development for carotid plaque assessment; leaves open prospective validation before clinical use.
Quantitative characterization of carotid atherosclerosis and classification into either symptomatic or asymptomatic is crucial in terms of diagnosis and treatment planning for a range of cardiovascular diseases. This paper presents a computer-aided diagnosis (CAD) system (Atheromatic™, patented technology from Biomedical Technologies, Inc., CA, USA) which analyzes ultrasound images and classifies them into symptomatic and asymptomatic. The classification result is based on a combination of discrete wavelet transform, higher order spectra and textural features. In this study, we compare support vector machine (SVM) classifiers with different kernels. The classifier with a radial basis function (RBF) kernel achieved an accuracy of 91.7% as well as a sensitivity of 97%, and specificity of 80%. Encouraged by this result, we feel that these features can be used to identify the plaque tissue type. Therefore, we propose an integrated index, a unique number called symptomatic asymptomatic carotid index (SACI) to discriminate symptomatic and asymptomatic carotid ultrasound images. We hope this SACI can be used as an adjunct tool by the vascular surgeons for daily screening.
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Acharya et al. (2011) studied Carotid atherosclerosis. Atheromatic CAD system (SVM classifier with RBF kernel) vs. Other SVM kernels was evaluated on Classification accuracy for symptomatic vs. asymptomatic plaque. The Atheromatic CAD system using an SVM classifier with an RBF kernel classified carotid ultrasound images as symptomatic or asymptomatic with 91.7% accuracy, 97% sensitivity, and 80% specificity.
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