在基于运动同步的临床人群中对自闭症进行分类:使用现实生活诊断访谈的概念验证研究
Jana Christina Koehler1, Mark Sen Dong2,3, Da-Yea Song4,5
1Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany. jana.koehler@med.uni-muenchen.de.
Scientific reports
|March 7, 2024
概括
对自闭症诊断面试的计算机视觉分析显示,预测儿童和年轻人的自闭症谱系障碍 (ASD) 是有前途的. 这种使用人际同步数据的可扩展方法实现了63.4%的平衡精度.
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
- 计算机科学 计算机科学
背景情况:
- 自闭症谱系障碍 (ASD) 诊断面临临临床瓶.
- 预测建模具有潜力,但往往缺乏临床可扩展性.
- 需要对ASD进行客观,可扩展的诊断标记.
研究的目的:
- 探索计算机视觉视频分析,用于预测ASD诊断.
- 评估机器学习模型在现实世界临床环境中的可扩展性.
- 在诊断面试期间调查人际同步作为潜在的生物标志物.
主要方法:
- 在人际同步数据上训练了一个支持矢量机器 (SVM) 模型.
- 利用自闭症诊断观察时间表 (ADOS-2) 采访的视频分析.
- 将模型应用于儿童和年轻人的临床样本.
主要成果:
- 在对自闭症患者与其他精神病诊断患者进行分类时,SVM模型实现了63.4%的平衡准确度.
- 该模型在异质的临床样本中展示了机会以上的表现.
- 在分类指标和临床评级之间没有发现显著的关联.
结论:
- 在诊断面试期间对人际同步的计算机视觉分析是ASD研究的可行,可扩展的方法.
- 这种方法显示了未来自闭症诊断标志物发展的潜力.
- 进一步的调整可能会提高这种预测建模策略的临床实用性.
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