scholarly journals Identification of autism spectrum disorder using deep learning and the ABIDE dataset

2018 ◽  
Vol 17 ◽  
pp. 16-23 ◽  
Author(s):  
Anibal Sólon Heinsfeld ◽  
Alexandre Rosa Franco ◽  
R. Cameron Craddock ◽  
Augusto Buchweitz ◽  
Felipe Meneguzzi
Keyword(s):  
Autism Spectrum Disorder ◽  
Deep Learning ◽  
Autism Spectrum ◽  
Spectrum Disorder

scholarly journals Genotype-Based Deep Learning in Autism Spectrum Disorder: Diagnostic Classification and Prognostic Prediction Using Common Genetic Variants (Preprint)

2020 ◽  
Author(s):  
Haishuai Wang ◽  
Paul Avillach
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Deep Learning ◽  
The United States ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
Screening Tools ◽  
Common Variants ◽  
Autism Screening

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.

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

Sensors ◽  
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.

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

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.

scholarly journals Computer-Aided Screening of Autism Spectrum Disorder Using Eye-Tracking, Data Visualization and Deep Learning (Preprint)

10.2196/27706 ◽  
2021 ◽  
Author(s):  
Federica Cilia ◽  
Romuald Carette ◽  
Mahmoud Elbattah ◽  
Gilles Dequen ◽  
Jean-Luc Guérin ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Deep Learning ◽  
Eye Tracking ◽  
Data Visualization ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Tracking Data ◽  
Computer Aided

scholarly journals Autism Spectrum Disorder Classification Using Deep Learning

2021 ◽  
Vol 17 (08) ◽  
pp. 103
Author(s):  
Abdulrazak Yahya Saleh ◽  
Lim Huey Chern
Keyword(s):  
Artificial Intelligence ◽  
Autism Spectrum Disorder ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Image Data ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Support Vector ◽  
Wide Range

<p class="0abstract">The goal of this paper is to evaluate the deep learning algorithm for people placed in the Autism Spectrum Disorder (ASD) classification. ASD is a developmental disability that causes the affected people to have significant communication, social, and behavioural challenges. People with autism are saddled with communication problems, difficulties in social interaction and displaying repetitive behaviours. Several methods have been used to classify the ASD from non-ASD people. However, there is a need to explore more algorithms that can yield better classification performance. Recently, deep learning methods have significantly sharpened the cutting edge of learning algorithms in a wide range of artificial intelligence tasks. These artificial intelligence tasks refer to object detection, speech recognition, and machine translation. In this research, the convolutional neural network (CNN) is employed. This algorithm is used to find processes that can classify ASD with a higher level of accuracy. The image data is pre-processed; the CNN algorithm is then applied to classify the ASD and non-ASD, and the steps of implementing the CNN algorithm are clearly stated. Finally, the effectiveness of the algorithm is evaluated based on the accuracy performance. The support vector machine (SVM) is utilised for the purpose of comparison. The CNN algorithm produces better results with an accuracy of 97.07%, compared with the SVM algorithm. In the future, different types of deep learning algorithms need to be applied, and different datasets can be tested with different hyper-parameters to produce more accurate ASD classifications.</p>

scholarly journals Autism spectrum disorder classification on electroencephalogram signal using deep learning algorithm

2020 ◽  
Vol 9 (1) ◽  
pp. 91
Author(s):  
Nur Alisa Ali
Keyword(s):  
Autism Spectrum Disorder ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Research Work ◽  
Autism Spectrum ◽  
Performance Measure ◽  
Spectrum Disorder ◽  
Font Size ◽  
Deep Learning Algorithm

<span style="color: black; font-family: 'Times New Roman',serif; font-size: 9pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-US">Autism Spectrum Disorder (ASD) is a neurodevelopmental that impact the social interaction and communication skills. Diagnosis of ASD is one of the difficult problems facing researchers. This research work aimed to reveal the different pattern between autistic and normal children via electroencephalogram (EEG) by using the deep learning algorithm. The brain signal database used pattern recognition where the extracted features will undergo the multilayer perceptron network for the classification process. The promising method to perform the classification is through a deep learning algorithm, which is currently a well-known and superior method in the pattern recognition field. The performance measure for the classification would be the accuracy. The higher percentage means the more effectiveness for the ASD diagnosis. </span><span style="color: black; font-family: 'Times New Roman',serif; font-size: 9pt; mso-fareast-font-family: 'Times New Roman+FPEF'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-US">This can be seen as the ground work for applying a new algorithm for further development diagnosis of autism to see how the treatment is working as well in future.</span>

scholarly journals ASD-SAENet: A Sparse Autoencoder, and Deep-Neural Network Model for Detecting Autism Spectrum Disorder (ASD) Using fMRI Data

2021 ◽  
Vol 15 ◽  
Author(s):  
Fahad Almuqhim ◽  
Fahad Saeed
Keyword(s):  
Neural Network ◽  
Autism Spectrum Disorder ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Learning Model ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Fmri Data ◽  
Sparse Autoencoder ◽  
Deep Learning Model

Autism spectrum disorder (ASD) is a heterogenous neurodevelopmental disorder which is characterized by impaired communication, and limited social interactions. The shortcomings of current clinical approaches which are based exclusively on behavioral observation of symptomology, and poor understanding of the neurological mechanisms underlying ASD necessitates the identification of new biomarkers that can aid in study of brain development, and functioning, and can lead to accurate and early detection of ASD. In this paper, we developed a deep-learning model called ASD-SAENet for classifying patients with ASD from typical control subjects using fMRI data. We designed and implemented a sparse autoencoder (SAE) which results in optimized extraction of features that can be used for classification. These features are then fed into a deep neural network (DNN) which results in superior classification of fMRI brain scans more prone to ASD. Our proposed model is trained to optimize the classifier while improving extracted features based on both reconstructed data error and the classifier error. We evaluated our proposed deep-learning model using publicly available Autism Brain Imaging Data Exchange (ABIDE) dataset collected from 17 different research centers, and include more than 1,035 subjects. Our extensive experimentation demonstrate that ASD-SAENet exhibits comparable accuracy (70.8%), and superior specificity (79.1%) for the whole dataset as compared to other methods. Further, our experiments demonstrate superior results as compared to other state-of-the-art methods on 12 out of the 17 imaging centers exhibiting superior generalizability across different data acquisition sites and protocols. The implemented code is available on GitHub portal of our lab at: https://github.com/pcdslab/ASD-SAENet.

scholarly journals Deep Learning Approach for Screening Autism Spectrum Disorder in Children with Facial Images and Analysis of Ethnoracial Factors in Model Development and Application

2021 ◽  
Vol 11 (11) ◽  
pp. 1446
Author(s):  
Angelina Lu ◽  
Marek Perkowski
Keyword(s):  
Autism Spectrum Disorder ◽  
Deep Learning ◽  
Model Development ◽  
Facial Feature ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Intellectual Ability ◽  
Related Factors ◽  
Children With Asd ◽  
Facial Images

Autism spectrum disorder (ASD) is a developmental disability that can cause significant social, communication, and behavioral challenges. Early intervention for children with ASD can help to improve their intellectual ability and reduces autistic symptoms. Multiple clinical researches have suggested that facial phenotypic differences exist between ASD children and typically developing (TD) children. In this research, we propose a practical ASD screening solution using facial images through applying VGG16 transfer learning-based deep learning to a unique ASD dataset of clinically diagnosed children that we collected. Our model produced a 95% classification accuracy and 0.95 F1-score. The only other reported study using facial images to detect ASD was based on the Kaggle ASD Facial Image Dataset, which is an internet search-produced, low-quality, and low-fidelity dataset. Our results support the clinical findings of facial feature differences between children with ASD and TD children. The high F1-score achieved indicates that it is viable to use deep learning models to screen children with ASD. We concluded that the racial and ethnic-related factors in deep-learning based ASD screening with facial images are critical to solution viability and accuracy.

An Enhanced Deep Recurrent Neural Network for Autism Spectrum Disorder Diagnosis

2021 ◽  
Vol 11 (12) ◽  
pp. 3028-3037
Author(s):  
D. Pavithra ◽  
A. N. Jayanthi
Keyword(s):  
Neural Network ◽  
Autism Spectrum Disorder ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Domain Identification ◽  
Investigation Area ◽  
Deep Recurrent Neural Network ◽  
Sensitivity Specificity

Autism Spectrum Disorder is one of the major investigation area in current era. There are many research works introduced earlier for handling the Autism Spectrum Disorders. However those research works doesn’t achieve the expected accuracy level. The accuracy and prediction efficiency can be increased by building a better classification system using Deep Learning. This paper focuses on the deep learning technique for Autism Diagnosis and the domain identification. In the proposed work, an Enhanced Deep Recurrent Neural Network has been developed for the detection of ASD at all ages. It attempts to predict the autism spectrum in the children along with prediction of areas which can predict the autism in the prior level. The main advantage of EDRNN is to provide higher accuracy in classification and domain identification. Here Artificial Algal Algorithm is used for identifying the most relevant features from the existing feature set. This model was evaluated for the data that followed Indian Scale for Assessment of Autism. The results obtained for the proposed EDRNN has better accuracy, sensitivity, specificity, recall and precision.

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