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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.

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相关实验视频

Updated: Jan 14, 2026

Eye Tracking Young Children with Autism

Eye Tracking Young Children with Autism

Published on: March 27, 2012

基于使用深度学习模型的眼睛跟踪技术诊断自闭症谱系障碍.

Mosleh Hmoud Al-Adhaileh^1,2, Saleh N M Alsubari³, Abdullah H Al-Nefaie^1,4

¹King Salman Center for Disability Research, Riyadh, Saudi Arabia.

Frontiers in medicine

|October 27, 2025

概括

这项研究使用深度学习和眼睛跟踪数据来准确诊断儿童的自闭症谱系障碍 (ASD). 先进的AI模型实现了99.78%的准确性,为临床诊断提供了一个有前途的工具.

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.诊断诊断诊断是为了诊断.用眼睛追踪来进行追踪.

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Using the Visual World Paradigm to Study Sentence Comprehension in Mandarin-Speaking Children with Autism

Using the Visual World Paradigm to Study Sentence Comprehension in Mandarin-Speaking Children with Autism

Published on: October 3, 2018

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Last Updated: Jan 14, 2026

Eye Tracking Young Children with Autism

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Published on: March 27, 2012

Comparing Eye-tracking Data of Children with High-functioning ASD, Comorbid ADHD, and of a Control Watching Social Videos

Comparing Eye-tracking Data of Children with High-functioning ASD, Comorbid ADHD, and of a Control Watching Social Videos

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Using the Visual World Paradigm to Study Sentence Comprehension in Mandarin-Speaking Children with Autism

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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:

患有自闭症谱系障碍 (ASD) 的儿童在社交沟通方面遇到挑战,特别是保持眼神接触.
眼球追踪 (ET) 技术为视觉社会注意力模式提供了精确的实时洞察力.
确定可靠的ASD生物标志物对于早期干预和支持至关重要.

研究的目的:

实施深度学习 (DL) 算法,用于分析ASD儿童的眼睛跟踪数据.
开发一个人工智能驱动的系统,使用社会注意力指标准确诊断ASD.

主要方法:

利用了来自有或没有自闭症患者的标准眼睛跟踪数据集.
应用卷积神经网络 (CNN) 和长短期记忆 (LSTM) 模型用于数据分析.
采用数据预处理,特征选择 (相互信息) 和CNN-LSTM模型来评估ASD诊断.

主要成果:

在CNN-LSTM模型实现诊断准确率为99.78%.
建议的深度学习方法与以前的研究相比,表现优越.
该系统成功地根据眼睛跟踪数据识别了ASD个体.

结论:

开发的系统有效地使用眼睛跟踪数据和深度学习来诊断ASD.
这种基于人工智能的方法显示出在ASD诊断中临床应用的巨大潜力.
该技术可以帮助医疗保健专业人员实现更准确,更有效的ASD诊断.