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 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.

scholarly journals Data Analysis and Classification of Autism Spectrum Disorder Using Principal Component Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Ammar I. Shihab ◽  
Faten A. Dawood ◽  
Ali H. Kashmar
Keyword(s):  
Autism Spectrum Disorder ◽  
Data Analysis ◽  
Attention Deficit Disorder ◽  
Component Analysis ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Data Set ◽  
Children With Asd ◽  
Adults And Children

Autism spectrum disorder (ASD) is an early developmental disorder characterized by mutation of enculturation associated with attention deficit disorder in the visual perception of emotional expressions. An estimated one in more than 100 people has autism. Autism affects almost four times as many boys than girls. Data analysis and classification of ASD is still challenging due to unsolved issues arising from many severity levels and range of signs and symptoms. To understanding the functions which involved in autism, neuroscience technology analyzed responses to stimuli of autistic audio and video. The study focuses on analyzing the data set of adults and children with ASD using practical component analysis method. To satisfy this aim, the proposed method consists of three main stages including: (1) data set preparation, (2) Data analysis, and (3) Unsupervised Classification. The experimental results were performed to classify adults and children with ASD. The classification results in adults give a sensitivity of 78.6% and specificity of 82.47%, while the classification results in children give a sensitivity of 87.5% and specificity of 95.7%.

scholarly journals Exploring Eating and Nutritional Challenges for Children with Autism Spectrum Disorder: Parents’ and Special Educators’ Perceptions

Nutrients ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2530
Author(s):  
Noor Akmal Shareela Ismail ◽  
Nurul Syafinaz Ramli ◽  
Nur Hana Hamzaid ◽  
Nurul Izzaty Hassan
Keyword(s):  
Autism Spectrum Disorder ◽  
Nutrition Education ◽  
Special Educators ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Full Data ◽  
Data Set ◽  
Children With Asd ◽  
Healthy Nutrition ◽  
Primary Caretakers

Autism spectrum disorder (ASD) is a complex neurodevelopmental disability that is frequently associated with food refusal, limited food repertoire and high-frequency single food intake mainly among children with ASD. Provision of nutrition can be very challenging due to the fact of these behavioural problems, either for the parents or special educators. Healthy nutrition is associated with providing and consuming nutritious food with results being in a good state of health. Semi-structured focus group discussions (FGDs) were conducted among 20 participants at a National Autism Centre to explore their understanding towards healthy nutrition. They were parents and special educators who were actively involved with children with ASD. A series of discussions were transcribed verbatim, and four researchers examined each transcript. Inductive analysis linking codes into main thematic categories was conducted using the constant comparison approach across the full data set. The outcome suggested that participants had limited knowledge relating to the proper dietary and nutritional needs of the children. The key messages from the discussion provide a foundation on the development of a nutrition education module which involves primary caretakers of children with ASD.

The Influence of Language Context on Repetitive Speech Use in Children With Autism Spectrum Disorder

2020 ◽  
Vol 29 (1) ◽  
pp. 327-334 ◽  
Author(s):  
Allison Gladfelter ◽  
Cassidy VanZuiden
Keyword(s):  
Autism Spectrum Disorder ◽  
Autism Spectrum ◽  
Individual Characteristics ◽  
Receptive Vocabulary ◽  
Spectrum Disorder ◽  
School Age Children ◽  
Social Responsiveness ◽  
Contributing Factors ◽  
Children With Asd ◽  
Language Context

Purpose Although repetitive speech is a hallmark characteristic of autism spectrum disorder (ASD), the contributing factors that influence repetitive speech use remain unknown. The purpose of this exploratory study was to determine if the language context impacts the amount and type of repetitive speech produced by children with ASD. Method As part of a broader word-learning study, 11 school-age children with ASD participated in two different language contexts: storytelling and play. Previously collected language samples were transcribed and coded for four types of repetitive speech: immediate echolalia, delayed echolalia, verbal stereotypy, and vocal stereotypy. The rates and proportions of repetitive speech were compared across the two language contexts using Wilcoxon signed-ranks tests. Individual characteristics were further explored using Spearman correlations. Results The children produced lower rates of repetitive speech during the storytelling context than the play-based context. Only immediate echolalia differed between the two contexts based on rate and approached significance based on proportion, with more immediate echolalia produced in the play-based context than in the storytelling context. There were no significant correlations between repetitive speech and measures of social responsiveness, expressive or receptive vocabulary, or nonverbal intelligence. Conclusions The children with ASD produced less immediate echolalia in the storytelling context than in the play-based context. Immediate echolalia use was not related to social skills, vocabulary, or nonverbal IQ scores. These findings offer valuable insights into better understanding repetitive speech use in children with ASD.

scholarly journals Parents’ Perceptions of Early Interventions and Related Services for Children with Autism Spectrum Disorder in Saudi Arabia

2016 ◽  
Vol 9 (10) ◽  
pp. 128 ◽  
Author(s):  
Faihan Alotaibi ◽  
Nabil Almalki
Keyword(s):  
Autism Spectrum Disorder ◽  
Saudi Arabia ◽  
Early Intervention ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Early Interventions ◽  
Related Services ◽  
Children With Asd ◽  
The Government

<p class="apa">The present study sought to examine parents’ perceptions of early interventions and related services for children with autism spectrum disorder (ASD) in Saudi Arabia. In this study a survey was distributed to a sample of 80 parents with children who have ASD. Parents also were asked open-ended questions to enable them to provide suggestions. The findings indicate that parents have varying perceptions of early interventions and related services. However, they seem to agree that these services are important in assisting their children. Accordingly, parents have suggested that the government needs to increase these services by providing more centers for children with ASD in Saudi Arabia, providing more specialists to deal with children with ASD, promoting inclusion in regular schools and providing more information on early intervention.</p>

scholarly journals Implication of the Sensory Environment in Children with Autism Spectrum Disorder: Perspectives from School

2021 ◽  
Vol 18 (14) ◽  
pp. 7670
Author(s):  
Ana Gentil-Gutiérrez ◽  
José Luis Cuesta-Gómez ◽  
Paula Rodríguez-Fernández ◽  
Jerónimo Javier González-Bernal
Keyword(s):  
Autism Spectrum Disorder ◽  
Sensory Processing ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Sensory Profile ◽  
School Factors ◽  
Cross Sectional ◽  
Sensory Stimuli ◽  
Children With Asd

(1) Background: Children with Autism Spectrum Disorder (ASD) frequently have difficulties in processing sensory information, which is a limitation when participating in different contexts, such as school. The objective of the present study was to compare the sensory processing characteristics of children with ASD in the natural context of school through the perception of professionals in the field of education, in comparison with neurodevelopmental children (2) Methods: A cross-sectional descriptive study as conducted with study population consisting of children between three and ten years old, 36 of whom were diagnosed with ASD and attended the Autismo Burgos association; the remaining 24 had neurotypical development. The degree of response of the children to sensory stimuli at school was evaluated using the Sensory Profile-2 (SP-2) questionnaire in its school version, answered by the teachers. (3) Results: Statistically significant differences were found in sensory processing patterns (p = 0.001), in sensory systems (p = 0.001) and in school factors (p = 0.001). Children with ASD who obtained worse results. (4) Conclusions: Children with ASD are prone to present sensory alterations in different contexts, giving nonadapted behavioral and learning responses.

scholarly journals Review of Cognitive Characteristics of Autism Spectrum Disorder Using Performance on Six Subtests on Four Versions of the Wechsler Intelligence Scale for Children

2021 ◽  
Author(s):  
Mizuho Takayanagi ◽  
Yoko Kawasaki ◽  
Mieko Shinomiya ◽  
Hoshino Hiroshi ◽  
Satoshi Okada ◽  
...  
Keyword(s):  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Block Design ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Intelligence Scale ◽  
Cognitive Characteristics ◽  
Children With Asd ◽  
The Right ◽  
The Impact

AbstractThis study was a systematic review of research using the Wechsler Intelligence Scale for Children (WISC) with Autism Spectrum Disorder (ASD) to examine cognitive characteristics of children with ASD beyond the impact of revisions based on WISC and diagnostic criteria changes. The classic “islets of ability” was found in individuals with full-scale IQs < 100. The “right-descending profiles” were observed among high IQ score individuals. High levels on the Block Design and low Coding levels were consistently found regardless of the variation in intellectual functioning or diagnosis. This review identified patterns of cognitive characteristics in ASD individuals using empirical data that researchers may have previously been aware of, based on their experiences, owing to the increased prevalence of ASD.

scholarly journals A Systematic Review and Meta-Analysis of Immunoglobulin G Abnormalities and the Therapeutic Use of Intravenous Immunoglobulins (IVIG) in Autism Spectrum Disorder

2021 ◽  
Vol 11 (6) ◽  
pp. 488
Author(s):  
Daniel A Rossignol ◽  
Richard E Frye
Keyword(s):  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Immunoglobulin G ◽  
Meta Analysis ◽  
Intravenous Immunoglobulins ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Effect Sizes ◽  
Aberrant Behavior ◽  
Children With Asd

Autism spectrum disorder (ASD) is a neurodevelopmental disorder affecting approximately 2% of children in the United States. Growing evidence suggests that immune dysregulation is associated with ASD. One immunomodulatory treatment that has been studied in ASD is intravenous immunoglobulins (IVIG). This systematic review and meta-analysis examined the studies which assessed immunoglobulin G (IgG) concentrations and the therapeutic use of IVIG for individuals with ASD. Twelve studies that examined IgG levels suggested abnormalities in total IgG and IgG 4 subclass concentrations, with concentrations in these IgGs related to aberrant behavior and social impairments, respectively. Meta-analysis supported possible subsets of children with ASD with low total IgG and elevated IgG 4 subclass but also found significant variability among studies. A total of 27 publications reported treating individuals with ASD using IVIG, including four prospective, controlled studies (one was a double-blind, placebo-controlled study); six prospective, uncontrolled studies; 2 retrospective, controlled studies; and 15 retrospective, uncontrolled studies. In some studies, clinical improvements were observed in communication, irritability, hyperactivity, cognition, attention, social interaction, eye contact, echolalia, speech, response to commands, drowsiness, decreased activity and in some cases, the complete resolution of ASD symptoms. Several studies reported some loss of these improvements when IVIG was stopped. Meta-analysis combining the aberrant behavior checklist outcome from two studies demonstrated that IVIG treatment was significantly associated with improvements in total aberrant behavior and irritability (with large effect sizes), and hyperactivity and social withdrawal (with medium effect sizes). Several studies reported improvements in pro-inflammatory cytokines (including TNF-alpha). Six studies reported improvements in seizures with IVIG (including patients with refractory seizures), with one study reporting a worsening of seizures when IVIG was stopped. Other studies demonstrated improvements in recurrent infections, appetite, weight gain, neuropathy, dysautonomia, and gastrointestinal symptoms. Adverse events were generally limited but included headaches, vomiting, worsening behaviors, anxiety, fever, nausea, fatigue, and rash. Many studies were limited by the lack of standardized objective outcome measures. IVIG is a promising and potentially effective treatment for symptoms in individuals with ASD; further research is needed to provide solid evidence of efficacy and determine the subset of children with ASD who may best respond to this treatment as well as to investigate biomarkers which might help identify responsive candidates.

Exploring Growth in Expressive Communication of Infants and Toddlers With Autism Spectrum Disorder

2021 ◽  
pp. 105381512199557
Author(s):  
Jay Buzhardt ◽  
Anna Wallisch ◽  
Dwight Irvin ◽  
Brian Boyd ◽  
Brenda Salley ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Progress Monitoring ◽  
Autism Spectrum ◽  
Growth Patterns ◽  
Spectrum Disorder ◽  
Future Research ◽  
Infants And Toddlers ◽  
Early Communication ◽  
Children With Asd ◽  
Language Growth

One of the earliest indicators of autism spectrum disorder (ASD) is delay in language and social communication. Despite consensus on the benefits of earlier diagnosis and intervention, our understanding of the language growth of children with ASD during the first years of life remains limited. Therefore, this study compared communication growth patterns of infants and toddlers with ASD to growth benchmarks of a standardized language assessment. We conducted a retrospective analysis of growth on the Early Communication Indicator (ECI) of 23 infants and toddlers who received an ASD diagnosis in the future. At 42 months of age, children with ASD had significantly lower rates of gestures, single words, and multiple words, but significantly higher rates of nonword vocalizations. Children with ASD had significantly slower growth of single and multiple words, but their rate of vocalization growth was significantly greater than benchmark. Although more research is needed with larger samples, because the ECI was designed for practitioners to monitor children’s response to intervention over time, these findings show promise for the ECI’s use as a progress monitoring measure for young children with ASD. Limitations and the need for future research are discussed.

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