在学龄前和学龄儿童中自闭症数字表型化
Vikram Aikat1, Kimberly L H Carpenter2,3, Pradeep Raj Krishnappa Babu4
1Department of Computer Science, Duke University, Durham, North Carolina, USA.
概括
SenseToKnow是一个数字表型化应用程序,通过计算机视觉客观地测量儿童的自闭症相关行为. 它作为一种可扩展的工具,在不同年龄组中进行自闭症查和监测.
科学领域:
- 神经科学是一个神经科学.
- 发展心理学 发展心理学
- 数字健康数字健康
背景情况:
- 传统的自闭症评估方法缺乏可扩展性和客观性.
- 数字表型化为行为测量提供了一种新的方法.
- 之前的研究证实SenseToKnow适用于幼儿.
研究的目的:
- 评估SenseToKnow应用程序在区分3-8岁的自闭症和神经类型儿童中的有效性.
- 评估数字现象型在时间上的稳定性.
- 为了确定与已确定的临床测量方法的融合有效性.
主要方法:
- 对149名3-8岁儿童 (45名神经类型儿童,104名自闭症儿童) 进行了SenseToKnow应用程序的管理.
- 利用计算机视觉和触摸数据来捕捉行为特征.
- 与Vineland适应性行为尺度和社会响应度尺度相关联的应用程序衍生的表型.
主要成果:
- 在自闭症和神经类型儿童之间发现了行为特征的显著差异.
- 数字表型在重复使用后显示稳定性.
- 应用程序特征与临床测量结果呈现一致的有效性,与适应性和社会响应能力相关.
结论:
- SenseToKnow有效地测量了学龄前和学龄儿童的自闭症相关行为.
- 该应用程序为自闭症评估提供了一个客观,可扩展和可访问的工具.
- 这些发现支持SenseToKnow在自闭症查和结果监测中的实用性.
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