多模式人工智能用于自闭症谱系障碍的风险分层:整合语音和查工具
Sookyung Bae1, Junho Hong2, Sungji Ha3
1Department of Integrated Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.
NPJ digital medicine
|August 21, 2025
概括
早期的自闭症谱系障碍 (ASD) 查具有挑战性. 一个使用移动数据的新人工智能框架准确地识别出患有自闭症的高风险儿童, 能够及时进行干预并提高诊断效率.
科学领域:
- 人工智能
- 儿童发育医学
- 机器学习
背景情况:
- 早期识别自闭症谱系障碍 (ASD) 对于有效的干预至关重要,但仍然需要大量资源.
- 目前的查方法通常需要专业人员和大量的时间.
- 需要可扩展和可访问的查工具来提高早期检测率.
研究的目的:
- 评估一种可扩展的自闭症谱系障碍 (ASD) 查的新型两阶段多式人工智能 (AI) 框架.
- 评估框架在区分典型发育儿童和高风险儿童或自闭症儿童方面的准确性.
- 确定框架对风险分层和支持早期诊断途径的能力.
主要方法:
- 一组1242名18至48个月的儿童参与了这项研究.
- 一个移动应用程序收集了亲子互动音频和查工具数据 (MCHAT,SCQ-L,SRS).
- 一个两级人工智能模型整合了多模式数据 (文本,音频,任务成功) 以进行风险评估,并与ADOS-2进行了验证.
主要成果:
- 在区分典型发育与高风险/自闭症儿童方面,第一阶段的AUROC为0.942.
- 在分辨高风险儿童和自闭症儿童方面,第二阶段的AUROC为0.914和85. 2%.
- 人工智能模型的风险预测与黄金标准ADOS-2评估有很强的一致性 (准确率为79.59%) 和显著的相关性 (Pearson r=0.830).
结论:
- 开发的AI框架为准确和可扩展的ASD早期查提供了一个有希望的方法.
- 利用移动技术和深度学习提高了早期风险分层的潜力.
- 这种框架可以支持及时干预,
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