从家庭视频中基于人工智能的自动识别自闭症谱系障碍
Dong Yeong Kim1,2, Ryemi Do3, Youmin Shin1,2
1Interdisciplinary Program in Bioengineering, Seoul National University College of Engineering, Seoul, Republic of Korea.
NPJ digital medicine
|October 10, 2025
概括
使用简短家庭视频的人工智能系统可以帮助在幼儿中选自闭症谱系障碍 (ASD). 这种方法改善了早期检测,有助于及时干预发育条件.
科学领域:
- 神经发育障碍 神经发育障碍
- 人工智能在医学中的应用
- 儿科健康 儿科健康
背景情况:
- 自闭症谱系障碍 (ASD) 是一种常见的神经发育状况,在儿童早期发作.
- 及时诊断ASD往往受到传统评估所需的时间,成本和专业知识的阻碍.
- 早期发现对于有效的干预和改善ASD儿童的治疗结果至关重要.
研究的目的:
- 开发和验证基于人工智能的查系统,以使用家庭录制的视频来早期检测ASD.
- 评估使用基于任务的短视频协议来捕捉孩子的自然行为是否可行.
- 改善早期ASD查的可访问性,特别是在资源有限的环境中.
主要方法:
- 收集了510名儿童 (18-48个月) 的家庭视频,他们执行了三个简短的基于任务的协议 (名称响应,模仿,球游戏).
- 利用深度学习模型从视频中提取特定任务的行为特征.
- 将人口统计数据的提取特征集成到用于ASD查的机器学习分类器中.
主要成果:
- 基于AI的选系统实现了接收器操作特征曲线 (AUC) 下的面积为0.83.
- 该系统在区分患有ASD和没有ASD的儿童方面表现出0.75的整体准确性.
- 自动化方法有效地分析了通过简短的视频任务引起的自然行为.
结论:
- 家庭视频的AI驱动分析为早期ASD查提供了一个有希望的,可访问的工具.
- 这种自动化系统可以补充临床评估,并帮助优先考虑及时干预的转诊.
- 利用技术进行自闭症查可以减少障碍,并促进对受影响儿童的早期支持.
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