An Ultrasonographic Risk Score For Detecting Symptomatic Carotid Atherosclerotic Plaques

作者：Afonso David^*; Seabra Jose; Pedro Luis M; Fernandes e Fernandes J; Sanches J Miguel

来源：IEEE Journal of Biomedical and Health Informatics, 2015, 19(4): 1505-1513.

DOI：10.1109/JBHI.2014.2359236

• Atherosclerotic carotid disease
• computer-aided detection and diagnosis
• machine learning
• pattern recognition and classification
• probabilistic and statistical methods
• risk score
• receiver-operator curve (ROC) analysis
• ultrasound
• vessels

this paper proposes a risk score computed from ultrasound data that correlates to plaque activity. It has the twofold purpose of detecting symptomatic plaques and estimating the likelihood of the asymptomatic lesion to become symptomatic. The proposed ultrasonographic activity index (UAI) relies on the plaque active profile, which is a combination of the most discriminate ultrasound parameter associated with symptoms. These features are extracted by the automatic algorithm and also by the physician from the ultrasound images and from some transformations on it, such as monogenic decomposition, which is a novelty in this clinical problem. This information is used to compute a risk score from the conditional probabilities of either symptomatic or asymptomatic groups. Symptom detection performance is evaluated on a transversal dataset of 146 plaques, where UAI obtained 83.5% accuracy, 84.1% sensitivity, and 83.7% specificity. Performance is also assessed on a longitudinal study of 112 plaques, where UAI shows a significant improvement over the gold standard degree of stenosis, demonstrating higher power at predicting which asymptomatic plaques developed symptoms in an average follow-up of ten months. Results suggest that this score could have a positive impact on early stroke prevention and treatment planning.

• 出版日期2015-7