Computer-assisted prediction of atherosclerotic intimal thickness based on weight of adrenal gland, interleukin-6 concentration, and neural networks

OBJECTIVE: Atherosclerosis (AS) is the main pathological basis of ischemic cardio-cerebrovascular diseases, and the intimal thickness (IT) of large arteries is regarded as a powerful evaluation indicator for AS. We established an effective neural network model for automatic prediction of the IT and analyzed the high-risk warning indicators of IT. METHODS: The weight of the left adrenal (WLA) was evaluated. The serum interleukin-6 (IL-6) concentration was measured by enzyme-linked immunosorbent assay. The statistical methods included neural network modeling, a cubic spline interpolation algorithm, Spearman’s rho test, and linear fit. RESULTS: Thirty-seven rabbits were classified into a control group (n = 11), high-fat diet group (n = 13), and high-fat diet plus chronic stress group (n = 13). The neural network model was successfully established and verified by comparing the predicted IT with the actual IT. The high-risk warning indicator of IT was identified as follows: 0.445 g < WLA < 0.610 g and 60 ng/L< IL-6 < 80 ng/L. CONCLUSIONS: The neural network model based on WLA and IL-6 could predict the IT of AS. When 0.445 g < WLA < 0.610 g and 60 ng/L < IL-6 < 80 ng/L, the risk to developing AS is very high.