Key result
The Atheromatic CAD system using a Support Vector Machine classifier with seven features achieved an accuracy of 99.1% in classifying symptomatic versus asymptomatic carotid ultrasound images.
Why the study?
Does the Atheromatic computer-aided diagnostic method accurately differentiate symptomatic from asymptomatic carotid B-mode ultrasonographic images?
Observational (n=59)
No
Does the Atheromatic computer-aided diagnostic method accurately differentiate symptomatic from asymptomatic carotid B-mode ultrasonographic images?
An automated computer-aided diagnostic method using IMT, IMT variability, and grayscale features can accurately differentiate symptomatic from asymptomatic carotid ultrasound images.
Should not yet alter carotid plaque risk assessment; leaves open prospective validation of SVM-based CAD systems.
PURPOSE: To test a computer-aided diagnostic method for differentiating symptomatic from asymptomatic carotid B-mode ultrasonographic images. METHODS: Our system (called Atheromatic) automatically computed the intima-media thickness (IMT) of the carotid far wall using AtheroEdge, calculated nonlinear features based on higher order spectra, and used these features and IMT and IMT variability (IMTVpoly ) to associate each image to a feature vector that was then labeled as symptomatic or asymptomatic (Sym/Asym) by a multiclassifiers system. We tested this method on a database of 118 carotid artery images from 37 symptomatic and 22 asymptomatic patients RESULTS: The highest accuracy (99.1%) was obtained by the support vector machine classifier using seven features. These features, relevant to discriminate Sym/Asym, included IMT and IMTVpoly , along with the bispectral entropies of the distal wall image at 77°, 78°, and 79° angles. CONCLUSIONS: Classification in Sym/Asym of the far carotid wall is feasible and accurate and could be useful for the early detection of atherosclerosis and to identify patients with higher cardiovascular risk.
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Acharya et al. (2014) conducted an observational in Carotid atherosclerosis (n=59). Atheromatic CAD system (SVM classifier) was evaluated on Classification accuracy (Symptomatic vs Asymptomatic). The Atheromatic CAD system using a Support Vector Machine classifier with seven features achieved an accuracy of 99.1% in classifying symptomatic versus asymptomatic carotid ultrasound images.
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