Computer Aided System for Autism Spectrum Disorder Using Deep Learning Methods

BACKGROUND In the United States, about 3 million people have autism spectrum disorder (ASD), and around 1 out of 59 children are diagnosed with ASD. People with ASD have characteristic social communication deficits and repetitive behaviors. The causes of this disorder remain unknown; however, in up to 25% of cases, a genetic cause can be identified. Detecting ASD as early as possible is desirable because early detection of ASD enables timely interventions in children with ASD. Identification of ASD based on objective pathogenic mutation screening is the major first step toward early intervention and effective treatment of affected children. OBJECTIVE Recent investigation interrogated genomics data for detecting and treating autism disorders, in addition to the conventional clinical interview as a diagnostic test. Since deep neural networks perform better than shallow machine learning models on complex and high-dimensional data, in this study, we sought to apply deep learning to genetic data obtained across thousands of simplex families at risk for ASD to identify contributory mutations and to create an advanced diagnostic classifier for autism screening. METHODS After preprocessing the genomics data from the Simons Simplex Collection, we extracted top ranking common variants that may be protective or pathogenic for autism based on a chi-square test. A convolutional neural network–based diagnostic classifier was then designed using the identified significant common variants to predict autism. The performance was then compared with shallow machine learning–based classifiers and randomly selected common variants. RESULTS The selected contributory common variants were significantly enriched in chromosome X while chromosome Y was also discriminatory in determining the identification of autistic from nonautistic individuals. The ARSD, MAGEB16, and MXRA5 genes had the largest effect in the contributory variants. Thus, screening algorithms were adapted to include these common variants. The deep learning model yielded an area under the receiver operating characteristic curve of 0.955 and an accuracy of 88% for identifying autistic from nonautistic individuals. Our classifier demonstrated a significant improvement over standard autism screening tools by average 13% in terms of classification accuracy. CONCLUSIONS Common variants are informative for autism identification. Our findings also suggest that the deep learning process is a reliable method for distinguishing the diseased group from the control group based on the common variants of autism.

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Potential identification of vitamin B6 responsiveness in autism spectrum disorder utilizing phenotype variables and machine learning methods

Scientific Reports ◽

10.1038/s41598-018-33110-w ◽

2018 ◽

Vol 8 (1) ◽

Cited By ~ 8

Author(s):

Taku Obara ◽

Mami Ishikuro ◽

Gen Tamiya ◽

Masao Ueki ◽

Chizuru Yamanaka ◽

...

Keyword(s):

Machine Learning ◽

Autism Spectrum Disorder ◽

Vitamin B6 ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Learning Methods ◽

Machine Learning Methods

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The Effects of a Computer-Aided Listening Comprehension Intervention on the Generalized Communication of Students With Autism Spectrum Disorder and Intellectual Disability

Journal of Special Education Technology ◽

10.1177/0162643419832976 ◽

2019 ◽

Vol 34 (4) ◽

pp. 269-283

Author(s):

Belva C. Collins ◽

Diane M. Browder ◽

Kathryn L. Haughney ◽

Caryn Allison ◽

Kathy Fallon

Keyword(s):

Autism Spectrum Disorder ◽

Intellectual Disability ◽

Listening Comprehension ◽

Functional Relation ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Dependent Variables ◽

Computer Aided ◽

Students With Autism ◽

Comprehension Intervention

In this study, a computer-aided listening comprehension intervention package supported both listening comprehension and communication goals for three students with autism spectrum disorder (ASD) and intellectual disability (ID). The package consisted of systematic instruction (i.e., system of least prompts [SLP] procedure) to teach listening comprehension, an iPad-supported electronic communication book, and a peer support arrangement. The students with ASD and ID who participated in the study increased both listening comprehension and communication skills, while showing an increase in generalizing communication turns to interactions with their peers without disabilities. The researchers found a functional relation between the SLP procedure and both dependent variables. All three participants experienced concurrent growth between the dependent variables, implying a connection between text-based listening comprehension and communication outcomes. Further implications for academic instruction for students with ASD who use augmentative and alternative communication as well as for future inquiry concerning cross-modal generalization to social communication discourse are discussed.

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Deep-Learning-Based Detection of Infants with Autism Spectrum Disorder Using Auto-Encoder Feature Representation

Sensors ◽

10.3390/s20236762 ◽

2020 ◽

Vol 20 (23) ◽

pp. 6762

Author(s):

Jung Hyuk Lee ◽

Geon Woo Lee ◽

Guiyoung Bong ◽

Hee Jeong Yoo ◽

Hong Kook Kim

Keyword(s):

Autism Spectrum Disorder ◽

Feature Extraction ◽

Deep Learning ◽

Autism Spectrum ◽

Feature Representation ◽

Diagnostic Methods ◽

Spectrum Disorder ◽

Joint Optimization ◽

Data Set ◽

Children With Asd

Autism spectrum disorder (ASD) is a developmental disorder with a life-span disability. While diagnostic instruments have been developed and qualified based on the accuracy of the discrimination of children with ASD from typical development (TD) children, the stability of such procedures can be disrupted by limitations pertaining to time expenses and the subjectivity of clinicians. Consequently, automated diagnostic methods have been developed for acquiring objective measures of autism, and in various fields of research, vocal characteristics have not only been reported as distinctive characteristics by clinicians, but have also shown promising performance in several studies utilizing deep learning models based on the automated discrimination of children with ASD from children with TD. However, difficulties still exist in terms of the characteristics of the data, the complexity of the analysis, and the lack of arranged data caused by the low accessibility for diagnosis and the need to secure anonymity. In order to address these issues, we introduce a pre-trained feature extraction auto-encoder model and a joint optimization scheme, which can achieve robustness for widely distributed and unrefined data using a deep-learning-based method for the detection of autism that utilizes various models. By adopting this auto-encoder-based feature extraction and joint optimization in the extended version of the Geneva minimalistic acoustic parameter set (eGeMAPS) speech feature data set, we acquire improved performance in the detection of ASD in infants compared to the raw data set.

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60 Precision Medicine in Developmental Pediatrics: Image-based Classification of Children with Autism Spectrum Disorder using Deep Learning

Paediatrics & Child Health ◽

10.1093/pch/pxaa068.059 ◽

2020 ◽

Vol 25 (Supplement_2) ◽

pp. e25-e25

Author(s):

Sarah MacEachern ◽

Deepthi Rajashekar ◽

Pauline Mouches ◽

Nathan Rowe ◽

Emily Mckenna ◽

...

Keyword(s):

Autism Spectrum Disorder ◽

Deep Learning ◽

Gray Matter ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Classification Model ◽

Children With Asd ◽

Adc Values ◽

Deep Gray Matter ◽

The Brain

Abstract Introduction/Background Autism spectrum disorder (ASD) is a neurodevelopmental disorder resulting in challenges with social communication, sensory differences, and repetitive and restricted patterns of behavior. ASD affects approximately 1 in 66 children in North America, with boys being affected four times more frequently than girls. Currently, diagnosis is made primarily based on clinical features and no robust biomarker for ASD diagnosis has been identified. Potential image-based biomarkers to aid ASD diagnosis may include structural properties of deep gray matter regions in the brain. Objectives The primary objective of this work was to investigate if children with ASD show micro- and macrostructural alterations in deep gray matter structures compared to neurotypical children, and if these biomarkers can be used for an automatic ASD classification using deep learning. Design/Methods Quantitative apparent diffusion coefficient (ADC) magnetic resonance imaging data was obtained from 23 boys with ASD ages 0.8 – 19.6 years (mean 7.6 years) and 39 neurotypical boys ages 0.3 – 17.75 years (mean 7.6 years). An atlas-based method was used for volumetric analysis and extraction of median ADC values for each subject within the cerebral cortex, hippocampus, thalamus, caudate, putamen, globus pallidus, amygdala, and nucleus accumbens. The extracted quantitative regional volumetric and median ADC values were then used for the development and evaluation of an automatic classification method using an artificial neural network. Results The classification model was evaluated using 10-fold cross validation resulting in an overall accuracy of 76%, which is considerably better than chance level (62%). Specifically, 33 neurotypical boys were correctly classified, whereas 6 neurotypical boys were incorrectly classified. For the ASD group, 14 boys were correctly classified, while 9 boys were incorrectly classified. This translates to a precision of 70% for the children with ASD and 79% for neurotypical boys. Conclusion To the best of our knowledge, this is the first method to classify children with ASD using micro- and macrostructural properties of deep gray matter structures in the brain. The first results of the proposed deep learning method to identify children with ASD using image-based biomarkers are promising and could serve as the platform to create a more accurate and robust deep learning model for clinical application.

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EEG-Based Computer Aided Diagnosis of Autism Spectrum Disorder Using Wavelet, Entropy, and ANN

BioMed Research International ◽

10.1155/2017/9816591 ◽

2017 ◽

Vol 2017 ◽

pp. 1-9 ◽

Cited By ~ 20

Author(s):

Ridha Djemal ◽

Khalil AlSharabi ◽

Sutrisno Ibrahim ◽

Abdullah Alsuwailem

Keyword(s):

Autism Spectrum Disorder ◽

Neurodevelopmental Disorder ◽

Autism Spectrum ◽

Computer Aided Diagnosis ◽

Spectrum Disorder ◽

Discrete Wavelet ◽

Eeg Signal ◽

Computer Aided ◽

Artificial Neural Network Ann ◽

Aided Diagnosis

Autism spectrum disorder (ASD) is a type of neurodevelopmental disorder with core impairments in the social relationships, communication, imagination, or flexibility of thought and restricted repertoire of activity and interest. In this work, a new computer aided diagnosis (CAD) of autism ‎based on electroencephalography (EEG) signal analysis is investigated. The proposed method is based on discrete wavelet transform (DWT), entropy (En), and artificial neural network (ANN). DWT is used to decompose EEG signals into approximation and details coefficients to obtain EEG subbands. The feature vector is constructed by computing Shannon entropy values from each EEG subband. ANN classifies the corresponding EEG signal into normal or autistic based on the extracted features. The experimental results show the effectiveness of the proposed method for assisting autism diagnosis. A receiver operating characteristic (ROC) curve metric is used to quantify the performance of the proposed method. The proposed method obtained promising results tested using real dataset provided by King Abdulaziz Hospital, Jeddah, Saudi Arabia.

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