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Gait characteristics and clinical relevance of hereditary spinocerebellar ataxia on deep learning.
Artificial Intelligence in Medicine ( IF 7.5 ) Pub Date : 2020-01-07 , DOI: 10.1016/j.artmed.2020.101794
Luya Jin 1 , Wen Lv 1 , Guocan Han 2 , Linhui Ni 1 , Di Sun 1 , Xingyue Hu 1 , Huaying Cai 1
Affiliation  

Background

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.

Methods

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, velocity, supporting-phase percentage, and swinging-phase percentage were extracted as gait parameters. Cerebellar volume and the midsagittal cerebellar proportion in the posterior fossa (MRVD) were calculated according to MR.

Results

There were significant differences in stride length, velocity, supporting-phase percentage, and swinging-phase percentage between the SCA group and the gait control group. The stride length and stride velocity of SCA groups were lower while supporting phase was longer than those of the gait control group. SCA group's velocity was negatively correlated with both the ICARS and SARA scores. The cerebellar volume and MRVD of the SCA imaging subgroup were significantly smaller than those of the imaging control group. MRVD was significantly correlated with ICARS and SARA scores, as well as stride velocity variability.

Conclusion

SCA gait parameters were characterized by a reduced stride length, slower walking velocity, and longer supporting phase. Additionally, a smaller cerebellar volume correlated with an increased irregularity in gait. Gait characteristics exhibited considerable clinical relevance to hereditary SCA. We conclude that a combination of gait parameters, ataxia scales, and MRVD may represent more objective markers for clinical evaluations of SCA.



中文翻译:

遗传性小脑共济失调对深度学习的步态特征和临床意义。

背景

深度学习一直是科学研究的最前沿。它也已应用于医学研究。遗传性脊髓小脑共济失调(SCA)的特征是步态异常,通常通过量表进行半定量评估。但是,尚未建立更详细的SCA步态特征和相关的客观方法。因此,本研究的目的是评估SCA患者的步态特征,并分析步态参数,临床量表和深度学习影像之间的相关性。

方法

通过基因检测诊断出的20名SCA患者被纳入研究。通过功能磁共振成像(fMRI)测试的十名患者被包括在SCA成像亚组中。使用国际合作性共济失调评定量表(ICARS)和评估和评定共济失调量表(SARA)的临床量表对所有SCA患者进行评估。步态对照组包括16名健康受试者,成像对照组包括7名健康受试者。用可穿戴的步态检测设备检测由SCA组和步态对照组中每个人的10 m自由行走组成的步态数据。将步幅,步幅,速度,支撑相位百分比和摆动相位百分比作为步态参数。

结果

SCA组和步态对照组之间的步幅,速度,支撑阶段百分比和摆动阶段百分比存在显着差异。与步态对照组相比,SCA组的步长和步速更低,而支撑期更长。SCA组的速度与ICARS和SARA分数均呈负相关。SCA成像亚组的小脑体积和MRVD显着小于成像对照组。MRVD与ICARS和SARA评分以及步幅速度变异性显着相关。

结论

SCA步态参数的特征是步幅减少,步行速度降低和支撑阶段延长。另外,较小的小脑体积与步态不规则性增加有关。步态特征表现出与遗传性SCA相当大的临床相关性。我们得出结论,步态参数,共济失调量表和MRVD的组合可能代表SCA临床评估的更多客观指标。

更新日期:2020-01-07
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