Machine Learning for Predicting Individual Severity of Blepharospasm Using Diffusion Tensor Imaging.

ABSTRACT: Accumulating diffusion tensor imaging (DTI) evidence suggests that white matter abnormalities evaluated by local diffusion homogeneity (LDH) or fractional anisotropy (FA) occur in patients with blepharospasm (BSP), both of which are significantly correlated with disease severity. However, whether the individual severity of BSP can be identified using these DTI metrics remains unknown. We aimed to investigate whether a combination of machine learning techniques and LDH or FA can accurately identify the individual severity of BSP. Forty-one patients with BSP were assessed using the Jankovic Rating Scale and DTI. The patients were assigned to non-functionally and functionally limited groups according to their Jankovic Rating Scale scores. A machine learning scheme consisting of beam search an

SUBMITTER: Liu G 

PROVIDER: S-EPMC8155629 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature