Estimation of common carotid intima-media thickening using neural networks in adults with and without ischemic stroke

Introduction: carotid intima-media thickening indicates potential atherosclerosis and risk of is-chemic stroke. The objective was to predict the presence of carotid intima-media thickening using neural networks in adults with and without stroke. Materials and methods: analytical and cross-sectional study of a secondary database of 600 patients with and without a history of ischemic stroke. The dependent variable was the intima-media thickness of the right and left common carotid artery (RCC and LCC). Biochemical mar-kers frequently used in primary care, systolic and diastolic blood pressure, were used. Multilayer perceptron-type neural networks with area under the curve (AUC) were used. Results: without a history of stroke, the perceptron predictive model for RCC was good (AUC=0.852). For LCC, it was acceptable (AUC=0.799). In patients with a history of ischemic stroke, the predictive model for RCC was good (AUC=0.826). The model for LCC was acceptable (AUC=0.789). In the absence of stroke, the neural network test had a percentage of correct predictions for right and left common carotid intima-media thickening of 81.30% and 79.20%, respectively. With a history of ischemic stroke, it was 82.80% and 91.50%, respectively. Conclusions: the multilayer perceptron-type neural network model, based on tests performed in primary care, had a high capacity to correctly predict intima-media thickening of the common carotid artery in patients without a history of ischemic stroke.