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微表达式可以用作自闭症谱系障碍的生物标志物吗?

Mindi Ruan¹, Na Zhang¹, Xiangxu Yu²

¹Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV, United States.

Frontiers in neuroinformatics

|October 18, 2024

概括

面部微表情显示出诊断自闭症谱系障碍 (ASD) 的潜力,但目前的机器学习模型需要更高质量的视频数据来进行可靠的识别. 需要在行为成像方面取得进一步的进展.

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) 的早期诊断对于有效的干预至关重要,但由于疾病的复杂性,仍然具有挑战性.
微表情,快速的非自愿的面部运动,可能提供了对情绪状态的洞察力,但它们对ASD的诊断实用性尚未被探索.

研究的目的:

研究面部微表情作为自闭症谱系障碍 (ASD) 诊断的生物标志物的潜力.
开发和评估一种机器学习 (ML) 框架,用于识别微表达式中的ASD特定模式.

主要方法:

开发了一个新的ML框架,包括微表达式发现 (SOFTNet),特征提取 (Micron-BERT) 和分类 (MLP,SVM,ResNet).
该框架分析了视频数据,以检测和区分ASD个体与典型发育同龄人之间的微表达模式.

主要成果:

复杂的ML框架可靠地识别ASD特定的微表达模式的能力受到输入视频数据的质量限制.
这种局限性引发了关于目前微表达式在没有改进数据采集的情况下用于ASD诊断的有效性的问题.

结论:

该研究提供了对微表情作为ASD诊断工具的初步评估.
需要在行为成像质量和多式人工智能方面取得重大进展,才能充分发挥ML在ASD诊断中的潜力.

关键词:

自闭症诊断观察时间表 (ADOS)自闭症谱系障碍 (ASD)面对视频视频的视频.可以解释的机器学习.这些都是微表达式.

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