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Autism Spectrum Disorder01:19

Autism Spectrum Disorder

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Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.
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Understanding and evaluating diffusion and perfusion is critical in assessing a patient's respiratory and circulatory health. These processes play key roles in maintaining the body's internal environment, ensuring that tissues receive adequate oxygen while waste products are efficiently removed.
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Updated: Jan 8, 2026

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging
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Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

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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
PubMed
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.

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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.