A comparative study of the oxidative stress indices of children with autism and healthy children

Uğur, Çağatay; Tunca, Hüseyin; Sekmen, Ebru; Üneri, Özden Şükran; Alışık, Murat; Erel, Özcan; Solmaz, Esra

doi:10.5455/apd.276094

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“…Machine learning is a promising tool for investigating the replicability of patterns across larger, more heterogeneous datasets [11][12][13]. For automated diagnosis of ASD, personal characteristic (PC) data, such as intelligence quotient (IQ) and Social Responsiveness Scale (SRS) score have been adopted in several studies [14][15][16]. In the study of ASD, IQ is a type of standard score that is derived from several standardized tests designed to assess human intelligence, and the SRS score includes a 65-item standardized questionnaire regarding behaviors that are associated with ASD [17].…”

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confidence: 99%

Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data

et al. 2020

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Autism spectrum disorder (ASD) is a developmental disorder that impacts more than 1.6% of children aged 8 across the United States. It is characterized by impairments in social interaction and communication, as well as by a restricted repertoire of activity and interests. The current standardized clinical diagnosis of ASD remains to be a subjective diagnosis, mainly relying on behavior-based tests. However, the diagnostic process for ASD is not only time consuming, but also costly, causing a tremendous financial burden for patients’ families. Therefore, automated diagnosis approaches have been an attractive solution for earlier identification of ASD. In this work, we set to develop a deep learning model for automated diagnosis of ASD. Specifically, a multichannel deep attention neural network (DANN) was proposed by integrating multiple layers of neural networks, attention mechanism, and feature fusion to capture the interrelationships in multimodality data. We evaluated the proposed multichannel DANN model on the Autism Brain Imaging Data Exchange (ABIDE) repository with 809 subjects (408 ASD patients and 401 typical development controls). Our model achieved a state-of-the-art accuracy of 0.732 on ASD classification by integrating three scales of brain functional connectomes and personal characteristic data, outperforming multiple peer machine learning models in a k-fold cross validation experiment. Additional k-fold and leave-one-site-out cross validation were conducted to test the generalizability and robustness of the proposed multichannel DANN model. The results show promise for deep learning models to aid the future automated clinical diagnosis of ASD.

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Section: Introductionmentioning

confidence: 99%

Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data

et al. 2020

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A comparative study of the oxidative stress indices of children with autism and healthy children

Abstract: Objective: This study was aimed to analyze the serum total antioxidant/total oxidant (TAS/TOS) levels and thiol/ disulphide balance of children with autism spectrum disorders (ASD) and draws a comparison

Cited by 1 publication

References 32 publications

Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data

Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data

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