使用ASDSpeech可靠量化自闭症儿童社会症状的严重程度
Marina Eni1,2, Yaniv Zigel3,4, Michal Ilan4,5
1Department of Biomedical Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel. marinamu@post.bgu.ac.il.
Translational psychiatry
|January 18, 2025
概括
一个新的AI算法,ASDSpeech,可以使用语音分析客观地测量患有自闭症谱系障碍 (ASD) 的儿童的社交沟通困难. 该工具有助于ASD研究和临床管理.
科学领域:
- 神经科学是一个神经科学.
- 语音语言病理学 语音语言病理学
- 人工智能的人工智能
背景情况:
- 社会沟通缺陷是自闭症谱系障碍 (ASD) 的核心症状.
- 患有自闭症的人的言语特征与症状严重程度相关.
- 通过语音分析对ASD症状的客观量化是研究和临床实践的一个有希望的途径.
研究的目的:
- 介绍和评估ASDSpeech,这是一个开源AI算法,用于分析ASD儿童的语音.
- 证明算法能够客观地量化社会沟通困难的能力.
- 在多个开发时间点中评估算法的实用性.
主要方法:
- 在最大的可用的ASD语音数据集上训练和测试ASDSpeech (来自197名儿童的99,193个发音).
- 在自闭症诊断观察计划,第二版 (ADOS-2) 评估期间,利用了语音录音中的声学和对话特征.
- 在61名儿童的独立记录上验证了算法,每个孩子都有两个ADOS-2评估,相隔1-2年.
主要成果:
- ASDSpeech在测试组中的估计和实际ADOS-2总分数之间显示出显著的相关性 (r=0.544和r=0.605).
- 该算法在估计社会沟通症状 (ADOS-2社会影响得分) 中显示出特别的准确性.
- 结果表明可靠量化跨发展时间点的社会沟通困难.
结论:
- ASDSspeech显示了提高ASD基础和临床研究的巨大潜力.
- 该算法可以帮助临床管理自闭症谱系障碍.
- 算法和数据集的开放共享促进了社区驱动的开发和应用.
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