Prognosis of cervical myelopathy based on diffusion tensor imaging with artificial intelligence methods

Abstract

Diffusion tensor imaging (DTI) has been proposed for the prognosis of cervical myelopathy (CM), but the manual analysis of DTI features is complicated and time consuming. This study evaluated the potential of artificial intelligence (AI) methods in the analysis of DTI for the prognosis of CM. Seventy-five patients who underwent surgical treatment for CM were recruited for DTI imaging and were divided into two groups based on their one-year follow-up recovery. The DTI features of fractional anisotropy, axial diffusivity, radial diffusivity, and mean diffusivity were extracted from DTI maps of all cervical levels. Conventional AI models using logistic regression (LR), k-nearest neighbors (KNN), and a radial basis function kernel support vector machine (RBF-SVM) were built using these DTI features. In addition, a deep learning model was applied to the DTI maps. Their performances were compared using 50 repeated 10-fold cross-validations. The accuracy of the classifications reached 74.2% +/- 1.6% for LR, 85.6% +/- 1.4% for KNN, 89.7% +/- 1.6% for RBF-SVM, and 59.2% +/- 3.8% for the deep leaning model. The RBF-SVM algorithm achieved the best accuracy, with sensitivity and specificity of 85.0% +/- 3.4% and 92.4% +/- 1.9% respectively. This finding indicates that AI methods are feasible and effective for DTI analysis for the prognosis of CM.