机器学习对自闭症谱系障碍的分类基于自然主义社会互动中的互惠性
Jana Christina Koehler1, Mark Sen Dong2, Afton M Bierlich2
1Department of Psychiatry and Psychotherapy, Medical Faculty, LMU, Munich, Germany. jana.koehler@med.uni-muenchen.de.
Translational psychiatry
|February 3, 2024
概括
数字表型精确地识别成人自闭症谱系障碍 (ASD),使用视频中的非语言线索. 这种方法提供了一种新的,客观的查和诊断方法,绕过了漫长的临床评估.
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
- 心理学 心理学 心理学
背景情况:
- 自闭症谱系障碍 (ASD) 是一种神经发育状况,其特征是社会沟通缺陷.
- 成年人对ASD的诊断通常涉及广泛的临床评估.
- 数字表型为客观的自闭症查和诊断提供了新的途径.
研究的目的:
- 量化成年自闭症患者的非语言社会互动特征.
- 开发机器学习模型来分类自闭症与非自闭症二合体.
- 创建独立于主观临床评级的诊断工具.
主要方法:
- 分析自闭症和非自闭症成年人对象的自然主义社交互动视频.
- 利用开源计算机视觉来客观地注释运动同步和面部表情.
- 员工支持向量机器学习用于预测分类.
主要成果:
- 基于相互面部适应 (79.5%准确率) 和个体运动/表现力 (68.8%准确率) 的模型显示出最高的预测性能.
- 分析头部和身体运动协调的模型的准确度较低 (62.1%和56.7%).
- 组合模型并没有提高整体预测准确度.
结论:
- 自闭症的不同非语言行为是可量化的,对于基于数字表型的分类是有用的.
- 对社会互动的客观,基于视频的分析显示了ASD评估的前景.
- 进一步的研究应该探索常规护理的多种算法和临床概括性.
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