Gait characteristics and clinical relevance of hereditary spinocerebellar ataxia on deep learning.

Artificial Intelligence in Medicine
Luya JinHuaying Cai

Abstract

Deep learning has always been at the forefront of scientific research. It has also been applied to medical research. Hereditary spinocerebellar ataxia (SCA) is characterized by gait abnormalities and is usually evaluated semi-quantitatively by scales. However, more detailed gait characteristics of SCA and related objective methods have not yet been established. Therefore, the purpose of this study was to evaluate the gait characteristics of SCA patients, as well as to analyze the correlation between gait parameters, clinical scales, and imaging on deep learning. Twenty SCA patients diagnosed by genetic detection were included in the study. Ten patients who were tested via functional magnetic resonance imaging (fMRI) were included in the SCA imaging subgroup. All SCA patients were evaluated with the International Cooperative Ataxia Rating Scale (ICARS) and Scale for the Assessment and Rating of Ataxia (SARA) clinical scales. The gait control group included 16 healthy subjects, and the imaging control group included seven healthy subjects. Gait data consisting of 10 m of free walking of each individual in the SCA group and the gait control group were detected by wearable gait-detection equipment. Stride length, stride time, veloc...Continue Reading

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