使用深度学习来对患有自闭症谱系障碍和没有自闭症的儿童在达到和放置运动中的发育差异进行分类
Wan-Chun Su1,2, John Mutersbaugh1, Wei-Lun Huang1,3
1Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health, Building 49, Room 5A82, 49 Convent Drive, Bethesda, MD, 20892-4480, USA.
Scientific reports
|December 5, 2024
概括
这项研究使用上肢运动分析和深度学习来识别儿童的自闭症谱系障碍 (ASD). 这些发现显示了ASD儿童的明显运动模式,有助于客观分类.
科学领域:
- 神经科学是一个神经科学.
- 生物医学工程 生物医学工程
- 计算机科学 计算机科学
背景情况:
- 自闭症谱系障碍 (ASD) 诊断依赖于行为评估,缺乏客观生物标志物.
- 需要早期和准确的识别方法来支持患有自闭症的儿童.
研究的目的:
- 调查上肢动力学作为ASD潜在的客观生物标志物.
- 应用深度学习技术来分类有或没有自闭症的儿童.
- 探索自闭症谱系障碍的新型诊断支持工具.
主要方法:
- 招募了41名学龄儿童 (有或没有自闭症).
- 使用单个惯性测量单元 (IMU) 捕捉手腕动力学在达到和放置任务.
- 用多层感知器 (MLP) 深度学习模型进行分类.
主要成果:
- 与典型发育 (TD) 儿童相比,患有ASD的儿童表现出受损的前/反手臂运动控制.
- 自闭症组表现出增加的运动单位,超射,以及长时间达到峰值速度/加速度.
- 该MLP模型在分类患有或没有自闭症儿童方面达到78.1%的准确性.
结论:
- 上肢运动动力学与深度学习相结合,显示出ASD分类的前景.
- 这些发现表明,有潜在的客观生物标志物可以帮助诊断ASD.
- 需要对年轻人群进行进一步的验证,以加强早期ASD识别.
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