Social Skill Focuses of Virtual Reality Systems for Individuals Diagnosed with Autism Spectrum Disorder; A Systematic Review

2018 ◽  
Vol 62 (1) ◽  
pp. 1469-1473 ◽  
Author(s):  
Elaine Thai ◽  
Dan Nathan-Roberts
Keyword(s):  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Virtual Reality ◽  
Social Interactions ◽  
Social Skill ◽  
A Priori ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Future Work ◽  
Direct Measures

A systematic review of virtual reality (VR) systems for individuals diagnosed with autism spectrum disorder (ASD) is detailed. The aim of this proceeding is to determine which social skills focuses are most important and how their potential improvements should be measured. Following the PRISMA guidelines, 48 articles were identified, and a total of 12 articles that met the a priori criteria were given full review. All 12 studies aimed to train some social skill, but there was no agreement among which single theme was most essential. The ones that received the most attention were facial expression and emotion recognition, appropriate behaviors and responses, and initiating social interactions. Only 9 studies used direct measures to assess changes made as a result of the VR systems. The ones most commonly used were subjective measures and participants’ body movements and gestures. The collective impacts and limitations of the studies are presented, as well as implications for future work.

THE USE OF AUGMENTED AND VIRTUAL REALITY IN CHILDREN WITH AUTISM SPECTRUM DISORDER: A BIBLIOGRAPHIC RESEARCH

2021 ◽  
Author(s):  
THAYS GRUBER ◽  
Fabio Evangelista Santana ◽  
Marcio Fontana Catapan
Keyword(s):  
Autism Spectrum Disorder ◽  
Virtual Reality ◽  
Literature Review ◽  
Social Interactions ◽  
Repetitive Behavior ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Narrative Literature ◽  
Narrative Literature Review

Autism Spectrum Disorder (ASD) affects children s neurodevelopment, impairing the ability for social interaction, communication and generating repetitive behavior. The treatment is performed according to specific methods, to practice social interactions, emotions and basic activities of daily living. Augmented Reality (AR) and Virtual Reality (VR) are collaborating as a complement to existing methods practiced by psychologists/therapists. Thus, this research seeks, through literature review, to find out what are the gaps in this theme and to know the applications of AR and VR concerning the treatment of children with ASD at national and international levels. The method chosen was the systematic literature review (SLR) and the narrative literature review. The database used was Scopus, where 39 articles were obtained, revealing a gap due to the low number of results, and finally, after using the filters, 19 articles were analyzed. The result shows great potential for using the technologies mentioned in support of the allowed therapeutic methods, allowing the treatment to evolve more efficiently.

scholarly journals Effectiveness of Virtual Reality for Children and Adolescents with Autism Spectrum Disorder: An Evidence-Based Systematic Review

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2486 ◽  
Author(s):  
Patricia Mesa-Gresa ◽  
Hermenegildo Gil-Gómez ◽  
José-Antonio Lozano-Quilis ◽  
José-Antonio Gil-Gómez
Keyword(s):  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Virtual Reality ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Evidence Based ◽  
Neurodevelopmental Disease ◽  
Clinical Databases ◽  
Science Index ◽  
High Prevalence

Autism Spectrum Disorder (ASD) is a neurodevelopmental disease that is specially characterized by impairments in social communication and social skills. ASD has a high prevalence in children, affecting 1 in 160 subjects. Virtual reality (VR) has emerged as an effective tool for intervention in the health field. Different recent papers have reviewed the VR-based treatments in ASD, but they have an important limitation because they only use clinical databases and do not include important technical indexes such as the Web of Science index or the Scimago Journal & Country Rank. To our knowledge, this is the first contribution that has carried out an evidence-based systematic review including both clinical and technical databases about the effectiveness of VR-based intervention in ASD. The initial search identified a total of 450 records. After the exclusion of the papers that are not studies, duplicated articles, and the screening of the abstract and full text, 31 articles met the PICO (Population, Intervention, Comparison and Outcomes) criteria and were selected for analysis. The studies examined suggest moderate evidence about the effectiveness of VR-based treatments in ASD. VR can add many advantages to the treatment of ASD symptomatology, but it is necessary to develop consistent validations in future studies to state that VR can effectively complement the traditional treatments.

Parent Education in Studies With Nonverbal and Minimally Verbal Participants With Autism Spectrum Disorder: A Systematic Review

2020 ◽  
Vol 29 (2) ◽  
pp. 890-902
Author(s):  
Lynn Kern Koegel ◽  
Katherine M. Bryan ◽  
Pumpki Lei Su ◽  
Mohini Vaidya ◽  
Stephen Camarata
Keyword(s):  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Parent Education ◽  
Autism Spectrum ◽  
Verbal Communication ◽  
Fidelity Of Implementation ◽  
Spectrum Disorder ◽  
Education Programs ◽  
Minimally Verbal ◽  
Parent Education Programs

Purpose The purpose of this systematic review was to identify parent education procedures implemented in intervention studies focused on expressive verbal communication for nonverbal (NV) or minimally verbal (MV) children with autism spectrum disorder (ASD). Parent education has been shown to be an essential component in the habilitation of individuals with ASD. Parents of individuals with ASD who are NV or MV may particularly benefit from parent education in order to provide opportunities for communication and to support their children across the life span. Method ProQuest databases were searched between the years of 1960 and 2018 to identify articles that targeted verbal communication in MV and NV individuals with ASD. A total of 1,231 were evaluated to assess whether parent education was implemented. We found 36 studies that included a parent education component. These were reviewed with regard to (a) the number of participants and participants' ages, (b) the parent education program provided, (c) the format of the parent education, (d) the duration of the parent education, (e) the measurement of parent education, and (f) the parent fidelity of implementation scores. Results The results of this analysis showed that very few studies have included a parent education component, descriptions of the parent education programs are unclear in most studies, and few studies have scored the parents' implementation of the intervention. Conclusions Currently, there is great variability in parent education programs in regard to participant age, hours provided, fidelity of implementation, format of parent education, and type of treatment used. Suggestions are made to provide both a more comprehensive description and consistent measurement of parent education programs.

Herbal Medicine as an Alternative Treatment in Autism Spectrum Disorder: A Systematic Review

2018 ◽  
Vol 19 (5) ◽  
pp. 454-459 ◽  
Author(s):  
Francielly Mourao Gasparotto ◽  
Francislaine Aparecida dos Reis Lívero ◽  
Sara Emilia Lima Tolouei Menegati ◽  
Arquimedes Gasparotto Junior
Keyword(s):  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Herbal Medicine ◽  
Alternative Treatment ◽  
Autism Spectrum ◽  
Spectrum Disorder

Diagnostic test accuracy for use of machine learning in diagnosis of autism spectrum disorder: A Systematic Review and Meta-Analysis (Preprint)

2019 ◽  
Author(s):  
Sun Jae Moon ◽  
Jin Seub Hwang ◽  
Rajesh Kana ◽  
John Torous ◽  
Jung Won Kim
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Meta Analysis ◽  
Learning Algorithms ◽  
Structural Mri ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder ◽  
Test Accuracy

BACKGROUND Over the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, its application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder. However, given its complexity and potential clinical implications, there is ongoing need for further research on its accuracy. OBJECTIVE The current study aims to summarize the evidence for the accuracy of use of machine learning algorithms in diagnosing autism spectrum disorder (ASD) through systematic review and meta-analysis. METHODS MEDLINE, Embase, CINAHL Complete (with OpenDissertations), PsyINFO and IEEE Xplore Digital Library databases were searched on November 28th, 2018. Studies, which used a machine learning algorithm partially or fully in classifying ASD from controls and provided accuracy measures, were included in our analysis. Bivariate random effects model was applied to the pooled data in meta-analysis. Subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false negative and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw SROC curves, and obtain area under the curve (AUC) and partial AUC. RESULTS A total of 43 studies were included for the final analysis, of which meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural MRI subgroup meta-analysis (12 samples with 1,776 participants) showed the sensitivity at 0.83 (95% CI-0.76 to 0.89), specificity at 0.84 (95% CI -0.74 to 0.91), and AUC/pAUC at 0.90/0.83. An fMRI/deep neural network (DNN) subgroup meta-analysis (five samples with 1,345 participants) showed the sensitivity at 0.69 (95% CI- 0.62 to 0.75), the specificity at 0.66 (95% CI -0.61 to 0.70), and AUC/pAUC at 0.71/0.67. CONCLUSIONS Machine learning algorithms that used structural MRI features in diagnosis of ASD were shown to have accuracy that is similar to currently used diagnostic tools.

scholarly journals Scalp acupuncture for Autism spectrum disorder: a systematic review

2020 ◽  
Vol 440 ◽  
pp. 042094
Author(s):  
Huaimin Yi ◽  
Yajun Han ◽  
Mengxin Li ◽  
Jiong Wang ◽  
Liping Yang
Keyword(s):  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Autism Spectrum ◽  
Scalp Acupuncture ◽  
Spectrum Disorder

scholarly journals A novel automated autism spectrum disorder detection system

2021 ◽  
Author(s):  
Shu Lih Oh ◽  
V. Jahmunah ◽  
N. Arunkumar ◽  
Enas W. Abdulhay ◽  
Raj Gururajan ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Developmental Disorder ◽  
Detection System ◽  
Brain Activity ◽  
Limited Range ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Nonlinear Features ◽  
Genetic And Environmental Factors ◽  
Future Work

AbstractAutism spectrum disorder (ASD) is a neurological and developmental disorder that begins early in childhood and lasts throughout a person’s life. Autism is influenced by both genetic and environmental factors. Lack of social interaction, communication problems, and a limited range of behaviors and interests are possible characteristics of autism in children, alongside other symptoms. Electroencephalograms provide useful information about changes in brain activity and hence are efficaciously used for diagnosis of neurological disease. Eighteen nonlinear features were extracted from EEG signals of 40 children with a diagnosis of autism spectrum disorder and 37 children with no diagnosis of neuro developmental disorder children. Feature selection was performed using Student’s t test, and Marginal Fisher Analysis was employed for data reduction. The features were ranked according to Student’s t test. The three most significant features were used to develop the autism index, while the ranked feature set was input to SVM polynomials 1, 2, and 3 for classification. The SVM polynomial 2 yielded the highest classification accuracy of 98.70% with 20 features. The developed classification system is likely to aid healthcare professionals as a diagnostic tool to detect autism. With more data, in our future work, we intend to employ deep learning models and to explore a cloud-based detection system for the detection of autism. Our study is novel, as we have analyzed all nonlinear features, and we are one of the first groups to have uniquely developed an autism (ASD) index using the extracted features.

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