Prediction of the Exact Degree of Internal Carotid Artery Stenosis Using an Artificial Neural Network Based on Duplex Velocity Measurements

Duplex ultrasound criteria use a combination of velocity measurements to evaluate internal carotid artery (ICA) stenosis. These evaluations divide ICA stenosis into broad categories. The aim of this study was to design an artificial neural network (ANN) capable of predicting the exact degree of ICA stenosis based on duplex velocity measurements. Consecutive patients with significant carotid atherosclerosis underwent carotid duplex ultrasound and angiography. Peak systolic and end-diastolic velocities in the ICA and common carotid artery were measured. Multilayered perceptron ANNs were constructed and trained to predict the degree of ICA stenosis and band the degree of ICA stenosis into 10% intervals based on these measurements. The accuracy of the ANN models in predicting the degree of ICA stenosis and classifying the ICA stenosis was compared with the angiographic degree of ICA stenosis and duplex velocity criteria. A total of 208 carotid bifurcations were studied. ANNs were able to accurately predict the degree of angiographic ICA stenosis (R² = 0.9374, p < 0.0001) and band the ICA stenosis into the predefined 10% intervals [sensitivity 97.3% (95% CI 90.7-99.3), specificity 97.7 % (95% CI 93.6-99.2), accuracy 97.5%]. The ANN model was more accurate [discriminant power (DP) = 4.11] in banding the degree of ICA stenosis than duplex velocity criteria (DP = 1.67) (p < 0.05). The accuracy of the ANN in correctly identifying >70% ICA stenosis was 98.4% [sensitivity 96.4% (95% CI 93.8-99.3), specificity 98.7% (95% CI 93.4-99.8), DP = 4.21]. ANNs can accurately predict the degree of ICA stenosis. With further refinement, ANNs could replace velocity criteria in the assessment of ICA stenosis using duplex ultrasound.