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一种深度学习潜变量模型,通过运动异常来识别患有自闭症的儿童

Nicola Milano¹, Roberta Simeoli^1,2, Angelo Rega^1,2

¹Department of Humanistic Studies, University of Naples Federico II, Napoli, Italy.

Frontiers in psychology

|June 5, 2023

概括

本研究介绍了一种自动化方法来检测自闭症谱系障碍 (ASD),使用机器学习来分析运动特征. 这些发现揭示了ASD儿童的明显运动模式,有助于诊断.

科学领域:

神经科学是一个神经科学.
计算机科学计算机科学
发展心理学发展心理学

背景情况:

自闭症谱系障碍 (ASD) 诊断缺乏客观,定量措施,这使得它具有挑战性和耗时.
目前的自闭症诊断过程往往需要广泛的专家合作.
使用先进技术的自动检测方法是ASD的新兴研究领域.

研究的目的:

开发一种自动化模型来检测自闭症谱系障碍 (ASD).
为评估ASD提供一种新的定量方法.
探索运动异常作为ASD的潜在特征.

主要方法:

利用一个变化自编码器,一种人工神经网络,用于ASD检测.
分析了基于平板电脑的心理测量尺度捕获的运动特征的潜在分布描述.
专注于机器学习方法来分析与ASD相关的运动异常.

主要成果:

提出的ASD检测模型表明,自闭症儿童和典型发育儿童之间的运动特征存在一致的差异.
确定了特定的运动模式,使ASD个体区分开来.
运动特征分析在区分发育群体方面被证明是有效的.

结论:

已经确定了自闭症的潜在运动标志.
开发的模型可以支持临床医生在ASD的诊断过程中.
定量运动分析可以作为ASD诊断或怀疑的额外指标.

关键词:

在ASD中,使用的是ASD.自闭症谱系障碍自闭症谱系障碍深度学习是一种深度学习.诊断诊断诊断诊断诊断诊断早期检测早期检测机器学习是机器学习.运动异常运动异常

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