追踪社会行为和支持自闭症谱系障碍诊断的人工智能:系统性审查和元分析
Carter Sun1, Alistair McEwan2, Kelsie A Boulton3
1Clinic for Autism and Neurodevelopment (CAN) Research, Brain and Mind Centre, Children's Hospital Westmead Clinical School, Faculty of Medicine and Health, University of Sydney, Australia; Child Neurodevelopment and Mental Health Team, Brain and Mind Centre, University of Sydney, Australia; School of Biomedical Engineering, Faculty of Engineering, University of Sydney, Australia.
EBioMedicine
|September 27, 2025
概括
人工智能 (AI) 可以通过分析面部暗示和社会行为来改善自闭症谱系障碍 (ASD) 评估. 这项技术提高了ASD患者的诊断准确性和治疗结果.
科学领域:
- 医疗信息学 医疗信息学
- 计算精神病学是一种计算精神病学.
- 发育神经科学的发展神经科学.
背景情况:
- 人工智能 (AI) 显示出开发追踪社会行为和帮助自闭症谱系障碍 (ASD) 临床评估的工具的潜力.
- 审查了现有的AI算法,用于在社交互动评估期间提取面部信息,以及它们对ASD评估和治疗响应诊断准确性的贡献.
研究的目的:
- 系统地审查和评估AI算法,以确定它们在ASD分类中的诊断准确性.
- 通过面部信息评估人工智能在追踪社会发展中的实用性,用于社会互动中的临床应用.
主要方法:
- 对患有自闭症个体的研究进行系统审查,搜索多个数据库 (Medline,Embase,Scopus,科学网,IEEE Xplore,ACM数字图书馆).
- 使用双变量和多级模型来分析诊断准确性,考虑异质性和调节者 (模式,算法,任务).
- 使用QUADAS-2工具评估了偏差风险和适用性;研究注册在PROSPERO (CRD42021249905) 上.
主要成果:
- 38项研究满足了40570项确定标准中的标准;七项提供了元分析的数据.
- 聚合的诊断几率比为15.917 (95% CI [4.775-53.059]),ROC曲线下的面积为0.862.
- 人工智能准确性提高了面部特征,非结构化的游戏,支持向量机器和决策树算法,分析眼睛的目光,情绪表达和联合注意力.
结论:
- 人工智能可以准确客观地增强自闭症评估,跟踪社会行为,改善治疗结果.
- 为了确保AI在ASD中的临床适用性和伦理使用,在不同人群中进一步验证是必不可少的.
- 支持人工智能的机器人已经在指导ASD治疗时发挥了实用性.
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