Diagnosis of Autism Spectrum Disorders in Young Children Based on Resting-State Functional Magnetic Resonance Imaging Data Using Convolutional Neural Networks
Diagnosis of Autism Spectrum Disorders in Young Children Based on Resting-State Functional Magnetic Resonance Imaging Data Using Convolutional Neural Networks
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DOI：
10.1007/s10278-019-00196-1
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发表时间：
2019-12-01
影响因子：
4.4
通讯作者：
Pedram, Mir Mohsen
中科院分区：
文献类型：
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作者：
Aghdam, Maryam Akhavan;Sharifi, Arash;Pedram, Mir Mohsen
统计数据显示，自闭症谱系障碍（ASD）的风险在世界范围内正在增加。早期诊断是治疗ASD的关键。到目前为止，ASD的儿童诊断是基于临床访谈和行为观察。有一个显着的需要，以减少使用传统的诊断技术，并诊断这种疾病在正确的时间和行为症状的表现之前。本研究的目的是提出智能模型来诊断ASD的幼儿静息态功能磁共振成像（rs-fMRI）数据的基础上，使用卷积神经网络（CNN）。CNN是迄今为止最强大的深度学习算法之一，主要使用具有大量样本的数据集进行训练。然而，在医学成像领域获得全面的数据集（如ImageNet）并获得可接受的结果已成为挑战。为了克服这两个挑战，在该分析中使用了两种“组合分类器”的方法，即动态（专家混合）和静态（简单贝叶斯）方法以及“迁移学习”。此外，由于ASD的诊断在早期更有效，因此本研究使用了来自全球自闭症脑成像数据交换I和II（ABIDE I和ABIDE II）数据集的5至10岁样本。该模型的准确性、灵敏度和特异性均优于先前在ABIDE I数据集上进行的研究结果（Adamax优化技术获得的最佳结果：准确性= 0.7273，灵敏度= 0.712，特异性= 0.7348）。此外，从ABIDE II数据集获得了可接受的分类结果（Adamax优化技术获得的最佳结果：准确度= 0.7，灵敏度= 0.582，特异性= 0.804）和ABIDE I和ABIDE II数据集的组合（Adam优化技术获得的最佳结果：准确性= 0.7045，灵敏度= 0.679，特异性= 0.7421）。我们可以得出结论，所提出的架构可以被认为是一个有效的工具，诊断ASD的幼儿。从另一个角度来看，该方法可以应用于分析与脑功能障碍相关的rs-fMRI数据。
Statistics show that the risk of autism spectrum disorder (ASD) is increasing in the world. Early diagnosis is most important factor in treatment of ASD. Thus far, the childhood diagnosis of ASD has been done based on clinical interviews and behavioral observations. There is a significant need to reduce the use of traditional diagnostic techniques and to diagnose this disorder in the right time and before the manifestation of behavioral symptoms. The purpose of this study is to present the intelligent model to diagnose ASD in young children based on resting-state functional magnetic resonance imaging (rs-fMRI) data using convolutional neural networks (CNNs). CNNs, which are by far one of the most powerful deep learning algorithms, are mainly trained using datasets with large numbers of samples. However, obtaining comprehensive datasets such as ImageNet and achieving acceptable results in medical imaging domain have become challenges. In order to overcome these two challenges, the two methods of "combining classifiers," both dynamic (mixture of experts) and static (simple Bayes) approaches, and "transfer learning" were used in this analysis. In addition, since diagnosis of ASD will be much more effective at an early age, samples ranging in age from 5 to 10 years from global Autism Brain Imaging Data Exchange I and II (ABIDE I and ABIDE II) datasets were used in this research. The accuracy, sensitivity, and specificity of presented model outperform the results of previous studies conducted on ABIDE I dataset (the best results obtained from Adamax optimization technique: accuracy = 0.7273, sensitivity = 0.712, specificity = 0.7348). Furthermore, acceptable classification results were obtained from ABIDE II dataset (the best results obtained from Adamax optimization technique: accuracy = 0.7, sensitivity = 0.582, specificity = 0.804) and the combination of ABIDE I and ABIDE II datasets (the best results obtained from Adam optimization technique: accuracy = 0.7045, sensitivity = 0.679, specificity = 0.7421). We can conclude that the proposed architecture can be considered as an efficient tool for diagnosis of ASD in young children. From another perspective, this proposed method can be applied to analyzing rs-fMRI data related to brain dysfunctions.