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ADBrainNet:一个深度神经网络用于自闭症谱系障碍 (ASD) 和注意力缺陷和多动性障碍 (ADHD) 的分类,使用基于可解释的人工智能的休息状态fMRI图像

Xinyao Yi¹, Jian Huang^2,3,4, Babatunde Akinwunmi⁵

¹Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong SAR, China.

Medical & biological engineering & computing

|March 4, 2026

概括

一个新的深度神经网络,ADBrainNet,使用脑成像有效地区分自闭症谱系障碍 (ASD) 和注意力缺陷和多动性障碍 (ADHD) 亚型. 这种可解释的人工智能模型显示了改善精神疾病评估的前景.

科学领域:

神经科学是一个神经科学.
人工智能的人工智能
精神病学是一个精神病学.

背景情况:

自闭症谱系障碍 (ASD) 和注意力缺陷和多动性障碍 (ADHD) 是常见的儿童精神疾病.
在区分ASD和ADHD亚型方面,诊断挑战和错误分类很普遍.
休息状态功能磁共振成像 (fMRI) 提供了基于神经成像的客观评估的潜力.

研究的目的:

开发和评估ADBrainNet,一个轻量级的深度神经网络,用于分类神经类型个体,ASD和ADHD亚型 (组合,多动/冲动,不注意).
通过使用fMRI数据,将ADBrainNet的性能与已建立的ImageNet转移学习模型进行比较.
利用可解释的人工智能 (LIME) 来解释ADBrainNet的决策过程并确定关键的神经成像特征.

主要方法:

利用来自ABIDE和ADHD-200数据集的静止状态fMRI数据.
训练并验证了ADBrainNet,一种新的深度神经网络架构.
通过使用五倍交叉验证将ADBrainNet与AlexNet,MobileNet,ResNet18和Xception进行比较.
应用LIME算法来实现模型的可解释性.

主要成果:

与基准模型相比,ADBrainNet在独立的外部测试集上取得了更高的分类性能.
对于ADBrainNet的平均准确度,精度,回忆和F1分数分别为61.87%,65.72%,61.87%和62.50%.
LIME分析发现了重要的神经成像特征,这些特征有助于ADBrainNet的分类决策.

结论:

ADBrainNet提供了一个可解释的计算框架,用于基于神经成像的ASD和ADHD亚型的分类.
该模型表明,它有助于评估,分层和管理精神疾病的潜力.
需要进一步验证和与临床性能进行比较,以探索其临床实用性.

关键词:

更多关于 ADHD ADHD 的文章在ASD中,使用的是ASD.深度神经网络是一个神经网络.可解释的人工智能休息状态的fMRI.

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