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通过使用视觉运动整合测试绘制特征来检测儿童的自闭症

Po Sheng Chen¹, Jasin Wong², Eva E Chen^3,4

¹Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan.

Health information science and systems

|January 29, 2025

概括

这项研究开发了一种使用图纸识别儿童自闭症谱系障碍 (ASD) 的新方法. 该方法准确地区分了ASD儿童,突出了他们的绘画中圆圈和空间关系的困难.

科学领域:

神经发育障碍神经发育障碍
发展心理学发展心理学
机器学习应用机器学习应用

背景情况:

自闭症谱系障碍 (ASD) 诊断依赖于行为观察.
早期发现ASD对于有效干预至关重要.
视觉运动整合技能在患有自闭症的个体中经常受到影响.

研究的目的:

引入一种新的分类方法来区分自闭症儿童和典型发育的儿童.
识别特定的绘图图案,表明ASD.
探索用于早期自闭症检测的跨文化工具的潜力.

主要方法:

在台湾招募了50名学龄儿童 (6-12岁).
从视觉-运动整合测试中收集的绘图数据.
使用集体学习训练深度学习分类模型.

主要成果:

获得了0.934.9的分类准确度.
确定了五个关键的绘画模式,区分了患有和没有自闭症的儿童.
患有自闭症的儿童表现出困难,涉及圆圈和空间关系的模式.

结论:

基于图纸的新型分类方法在识别ASD方面显示出高准确度.
特定的视觉运动缺陷,特别是圆圈和空间关系,与 ASD 相关.
这种方法为早期自闭症检测和干预提供了一个潜在的跨文化工具.

关键词:

自闭症检测自闭症的检测深度学习是一种深度学习.绘画绘画绘画绘画组合学习学习组合学习视觉-发动机集成测试试验

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相关概念视频

Autism Spectrum Disorder

Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.