用人工智能增强发育性语言障碍识别:使用真实和合成数据开发可解释的查应用程序
1Department of Languages and Literature, University of Nicosia, Nicosia, Cyprus. georgiou.georg@unic.ac.cy.
Journal of autism and developmental disorders
|December 13, 2025
概括
这项研究确定了在儿童中检测发育性语言障碍 (DLD) 的关键语言标志物. 一个由人工智能驱动的应用程序被开发和验证,在识别DLD方面显示出高准确性,有助于早期诊断.
科学领域:
- 语言学的语言学.
- 发展心理学 发展心理学
- 人工智能的人工智能
背景情况:
- 发育性语言障碍 (DLD) 影响儿童的语言学习.
- 准确及时诊断DLD对于干预至关重要.
- 现有的诊断方法可能是耗时和主观的.
研究的目的:
- 确定区分DLD儿童与典型发育 (TD) 同龄人的语言标记.
- 为DLD开发一个可解释的人工智能 (AI) 选应用程序.
- 评估AI选应用程序的可靠性和可行性.
主要方法:
- 一组30名儿童 (15名DLD,15名TD,年龄在7-10岁) 接受了口头评估.
- 语言测量包括词汇,形态语法和句子重复.
- 一个随机的森林分类器被训练在合成数据创建AI选应用程序.
主要成果:
- 在所有措施中,在DLD和TD群体之间发现了显著的语言差异.
- 人工智能查应用程序与专家临床诊断有很高的一致性.
- 该应用程序在识别患有DLD的儿童方面被证明是可靠的.
结论:
- 特定的语言指标对DLD具有诊断价值.
- 由人工智能驱动的选解决方案是可行的,并提供实际优势.
- 该应用程序的可解释性和可扩展性可以改善DLD检测,特别是在资源不足的地区.
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