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相关概念视频

Autism Spectrum Disorder01:19

Autism Spectrum Disorder

104
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.
104

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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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使用深度学习方法对ASD进行特定年龄的诊断分类.

Vaibhav Jain1, Sandeep Singh Sengar2, Jac Fredo Agastinose Ronickom1

  • 1Indian Institute of Technology (Banaras Hindu University), Varanasi, India.

Studies in health technology and informatics
|October 23, 2023
PubMed
概括

这项研究使用深度学习来分析自闭症谱系障碍 (ASD) 中的大脑功能连接. 特定年龄的模型显示了ASD的更好的诊断准确性,突出了年龄对大脑连接模式的重要性.

关键词:
自闭症谱系障碍 自闭症谱系障碍深度学习 (Deep Learning) 是一种深度学习.功能连接的功能连接性功能磁力共振成像 (fMRI) 是一种

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科学领域:

  • 神经科学是一个神经科学.
  • 人工智能的人工智能
  • 生物医学工程 生物医学工程

背景情况:

  • 自闭症谱系障碍 (ASD) 是高度异质的,由于病因,遗传学和大脑功能连接 (FC) 的变化,缺乏通用生物标志物.
  • 现有的诊断方法难以应对自闭症的复杂性,需要新的方法来准确识别.

研究的目的:

  • 通过深度学习研究年龄和多变量模式在大脑FC中的作用,以诊断ASD.
  • 开发和评估特定年龄的深度学习模型,以区分ASD和典型的发展个体.

主要方法:

  • 利用来自ABIDE-I和ABIDE-II数据库的功能磁共振成像 (fMRI) 数据,跨越三个年龄组 (6-11,11-18,6-18岁).
  • 提取血氧水平依赖 (BOLD) 时间序列,以使用皮尔森相关性计算236x236 FC矩阵.
  • 采用卷积神经网络 (MobileNetV2,DenseNet201),使用FC热图作为年龄特定诊断模型的输入.

主要成果:

  • 与MobileNetV2.2相比,DenseNet201表现出优越的特征提取和准确性.
  • 年龄特定的模型获得了最高的准确性:72.19%的6-11岁,71.88%的11-18岁,69.74%的6-18岁.
  • 6-11岁年龄组的数据集产生了最佳的诊断性能.

结论:

  • 特定年龄的深度学习模型可以有效地解决ASD的异质性.
  • 分析大脑功能连接中的与年龄相关的模式,可以改善对自闭症谱系障碍的诊断歧视.