Key result
A motion-based computer-aided diagnosis tool using kinematic features of the arterial wall achieved 88% classification accuracy in discriminating symptomatic from asymptomatic carotid atherosclerosis, compared to 80% for texture-based classification.
Why the study?
Does a CAD tool using kinematic features improve diagnostic classification accuracy for symptomatic vs asymptomatic carotid atherosclerosis compared to texture-based CAD?
Cross-Sectional (n=56)
Yes
Does a CAD tool using kinematic features improve diagnostic classification accuracy for symptomatic vs asymptomatic carotid atherosclerosis compared to texture-based CAD?
Absolute Event Rate: 88% vs 80%
The incorporation of kinematic features of the arterial wall into a computer-aided diagnosis tool significantly improves the classification accuracy of symptomatic versus asymptomatic carotid atherosclerosis compared to traditional texture-based analysis.
May support kinematic CAD evaluation in carotid atherosclerosis; leaves open prospective validation before clinical adoption.
Valid characterization of carotid atherosclerosis (CA) is a crucial public health issue, which would limit the major risks held by CA for both patient safety and state economies. This paper investigated the unexplored potential of kinematic features in assisting the diagnostic decision for CA in the framework of a computer-aided diagnosis (CAD) tool. To this end, 15 CAD schemes were designed and were fed with a wide variety of kinematic features of the atherosclerotic plaque and the arterial wall adjacent to the plaque for 56 patients from two different hospitals. The CAD schemes were benchmarked in terms of their ability to discriminate between symptomatic and asymptomatic patients and the combination of the Fisher discriminant ratio, as a feature-selection strategy, and support vector machines, in the classification module, was revealed as the optimal motion-based CAD tool. The particular CAD tool was evaluated with several cross-validation strategies and yielded higher than 88% classification accuracy; the texture-based CAD performance in the same dataset was 80%. The incorporation of kinematic features of the arterial wall in CAD seems to have a particularly favorable impact on the performance of image-data-driven diagnosis for CA, which remains to be further elucidated in future prospective studies on large datasets.
No takes yet. Share an insight, caveat, or question.
Gastounioti et al. (2014) conducted a cross-sectional in Carotid atherosclerosis (n=56). Motion-based computer-aided diagnosis (CAD) using kinematic features vs. Texture-based CAD was evaluated on Classification accuracy to discriminate between symptomatic and asymptomatic patients. A motion-based computer-aided diagnosis tool using kinematic features of the arterial wall achieved 88% classification accuracy in discriminating symptomatic from asymptomatic carotid atherosclerosis, compared to 80% for texture-based classification.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: