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为ASD制造:用于诊断自闭症谱系障碍的多图谱深层整体网络

Xuehan Liu¹, Md Rakibul Hasan², Tom Gedeon³

¹Australian National University, Canberra, ACT, 2601, Australia.

Computers in biology and medicine

|September 4, 2024

概括

一个新的深度学习模型,MADE-for-ASD,使用脑成像改进了自闭症谱系障碍 (ASD) 诊断. 这种自动化系统提高了早期检测的准确性和效率,以获得更好的患者结果.

科学领域:

神经科学是一个神经科学.
人工智能的人工智能
医疗成像医学成像

背景情况:

自闭症谱系障碍 (ASD) 的诊断是复杂且耗时的.
现有的方法缺乏早期检测的效率和可扩展性.
需要自动化诊断工具来弥合这一差距.

研究的目的:

开发和评估一种新的深度学习模型,以有效和准确地诊断ASD.
将多图谱功能磁共振成像 (fMRI) 数据与人口信息集成.
用自动化方法提高现有的ASD诊断准确度.

主要方法:

为ASD诊断提出了一个多地图集深度组合网络 (MADE-for-ASD).
来自ABIDE I数据集的综合静止状态fMRI数据与人口信息.
利用加权的深层组合网络结合多个大脑地图.

主要成果:

在整个ABIDE I数据集上达到75.20%的准确性,在特定子集上达到96.40%.
在fMRI研究中以4.4个百分点优于先前的ASD诊断准确度.
在完整的数据集上显示出高灵敏度 (82.90%) 和特异性 (69.70%).

结论:

该MADE-for-ASD系统为ASD诊断提供了一种具有成本效益,效率和可扩展的方法.
该模型的fMRI和人口统计数据的整合提供了一个整体的患者概况.
这种自动化系统有可能显著改善ASD早期检测和干预.

关键词:

自闭症自闭症自闭症计算机视觉计算机视觉计算机视觉深度学习是一种深度学习.医疗计算健康计算神经成像是一种神经成像.

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Autism Spectrum Disorder

Autism Spectrum Disorder

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.