使用扩散张力成像和机器学习来检测自闭症谱系障碍
Noel A Cardenas-Hernandez1, Marlen Perez-Diaz2, Karla Batista García-Ramó3,4
1Department of Physics, Universidad Central "Marta Abreu" de Las Villas, Santa Clara, Cuba.
PLOS digital health
|December 23, 2025
概括
本研究介绍了一种使用扩散张力成像 (DTI) 的机器学习系统,以客观检测自闭症谱系障碍 (ASD). 人工智能模型实现了高精度,为早期ASD诊断提供了潜在的工具.
科学领域:
- 神经科学是一个神经科学.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 自闭症谱系障碍 (ASD) 诊断通常是主观的,并依赖于行为评估.
- 扩散张力成像 (DTI) 显示了在ASD中识别微结构生物标志物的潜力.
- 有效的ASD干预需要客观和早期诊断工具.
研究的目的:
- 开发和评估一个机器学习 (ML) 驱动的计算机辅助诊断 (CAD) 系统,用于使用DTI数据检测ASD.
- 评估ML分类器在基于DTI衍生微观结构指标的ASD识别中的有效性.
- 调查基于DTI的神经成像用于客观ASD诊断的潜力.
主要方法:
- 使用ABIDE II数据库 (n=150) 进行系统开发和验证.
- 在25个与ASD相关的白质区域中,处理了DTI数据以提取分数异构 (FA),平均扩散率 (MD),辐射扩散率 (RD) 和轴向扩散率 (AD).
- 训练和评估ML二进制分类器,包括支持矢量机 (SVM) 和随机森林 (RF),优化计算效率.
主要成果:
- 优化的随机森林 (RF) 模型在内部数据集上实现了100%的灵敏度,95.65%的准确性,91.67%的精度和91.67%的特异性.
- 外部测试表明,该模型的概括能力具有94.73%的灵敏度,97.37%的准确性,以及100%的精度和特异性.
- 该研究强调了将DTI成像信息与ASD受影响白质区域的临床知识相结合的实用性.
结论:
- 开发的基于机器学习的CAD系统显示出对ASD的客观和早期检测有很大的希望.
- 当使用先进的ML技术进行分析时,DTI衍生的微结构指标可以作为ASD的可靠生物标志物.
- 这种方法提供了一个快速的,客观的,和潜在的更容易获得的替代目前的主观诊断方法的ASD.
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