scholarly journals A Systematic Literature Review on the Application of Machine-Learning Models in Behavioral Assessment of Autism Spectrum Disorder

2021 ◽  
Vol 11 (4) ◽  
pp. 299
Author(s):  
Nadire Cavus ◽  
Abdulmalik A. Lawan ◽  
Zurki Ibrahim ◽  
Abdullahi Dahiru ◽  
Sadiya Tahir ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Social Communication ◽  
Behavioral Assessment ◽  
Real Life ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Diagnostic Tools ◽  
Diagnostic Systems ◽  
Behavioral Challenges

Autism spectrum disorder (ASD) is associated with significant social, communication, and behavioral challenges. The insufficient number of trained clinicians coupled with limited accessibility to quick and accurate diagnostic tools resulted in overlooking early symptoms of ASD in children around the world. Several studies have utilized behavioral data in developing and evaluating the performance of machine learning (ML) models toward quick and intelligent ASD assessment systems. However, despite the good evaluation metrics achieved by the ML models, there is not enough evidence on the readiness of the models for clinical use. Specifically, none of the existing studies reported the real-life application of the ML-based models. This might be related to numerous challenges associated with the data-centric techniques utilized and their misalignment with the conceptual basis upon which professionals diagnose ASD. The present work systematically reviewed recent articles on the application of ML in the behavioral assessment of ASD, and highlighted common challenges in the studies, and proposed vital considerations for real-life implementation of ML-based ASD screening and diagnostic systems. This review will serve as a guide for researchers, neuropsychiatrists, psychologists, and relevant stakeholders on the advances in ASD screening and diagnosis using ML.

scholarly journals Machine Learning and Virtual Reality on Body Movements’ Behaviors to Classify Children with Autism Spectrum Disorder

2020 ◽  
Vol 9 (5) ◽  
pp. 1260 ◽  
Author(s):  
Mariano Alcañiz Raya ◽  
Javier Marín-Morales ◽  
Maria Eleonora Minissi ◽  
Gonzalo Teruel Garcia ◽  
Luis Abad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Virtual Reality ◽  
Quantitative Methods ◽  
Real Life ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
Body Movements ◽  
Children With Asd

Autism spectrum disorder (ASD) is mostly diagnosed according to behavioral symptoms in sensory, social, and motor domains. Improper motor functioning, during diagnosis, involves the qualitative evaluation of stereotyped and repetitive behaviors, while quantitative methods that classify body movements’ frequencies of children with ASD are less addressed. Recent advances in neuroscience, technology, and data analysis techniques are improving the quantitative and ecological validity methods to measure specific functioning in ASD children. On one side, cutting-edge technologies, such as cameras, sensors, and virtual reality can accurately detect and classify behavioral biomarkers, as body movements in real-life simulations. On the other, machine-learning techniques are showing the potential for identifying and classifying patients’ subgroups. Starting from these premises, three real-simulated imitation tasks have been implemented in a virtual reality system whose aim is to investigate if machine-learning methods on movement features and frequency could be useful in discriminating ASD children from children with typical neurodevelopment. In this experiment, 24 children with ASD and 25 children with typical neurodevelopment participated in a multimodal virtual reality experience, and changes in their body movements were tracked by a depth sensor camera during the presentation of visual, auditive, and olfactive stimuli. The main results showed that ASD children presented larger body movements than TD children, and that head, trunk, and feet represent the maximum classification with an accuracy of 82.98%. Regarding stimuli, visual condition showed the highest accuracy (89.36%), followed by the visual-auditive stimuli (74.47%), and visual-auditive-olfactory stimuli (70.21%). Finally, the head showed the most consistent performance along with the stimuli, from 80.85% in visual to 89.36% in visual-auditive-olfactory condition. The findings showed the feasibility of applying machine learning and virtual reality to identify body movements’ biomarkers that could contribute to improving ASD diagnosis.

Validity and reliability of a scale to measure knowledge and attitudes toward child atisum spectrum disorder among child caregivers: a study in northern Vietnam

2020 ◽  
Vol 04 (04) ◽  
pp. 120-129
Author(s):  
Thu Ha Dinh ◽  
◽  
Thanh Thuy Hua ◽  
Thi Hai Ha Le ◽  
Thai Quynh Nguyen ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Social Communication ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Validity And Reliability ◽  
Cronbach’S Alpha ◽  
Behavioral Challenges ◽  
Cronbach's Alpha ◽  
Knowledge And Attitudes ◽  
Child Caregivers

Autism Spectrum Disorder (ASD) is a range of developmental disabilities, that can cause significant social, communication and behavioral challenges. This study was conducted in 2017 in order to develop and validate a scale to measure knowledge and attitudes toward child autism spectrum disorder among child caregivers. We interviewed 193 child caregivers in 2 northern provinces of Vietnam, namely Hoa Binh and Thai Binh. Exploratory Factor Analysis (EFA) and Cronbach’s alpha coefficient were used to determine validity and reliability of our scale. Findings indicated that our scale on knowledge, including 13 items distributed into 3 factors with relatively good correlation (0.58-0.79), could explain 82.5% of variability of knowledge. The scale on attitudes consisted of 6 questions, explained 52.8% of variability in attitudes and had a good Cronbach’s alpha of 0.76. The results demonstrated that our scale has satisfactory validity and reliability, thus, could be used to measure knowledge and attitudes towards child ASD among child caregivers. Key words: scale, autism spectrum disorder (ASD), validity, reliability, child caregivers, Vietnam

scholarly journals Data-Driven Automated Detection of Autism Spectrum Disorder Using Activity Analysis: A Review

2020 ◽  
Author(s):  
Omar Shahid ◽  
Sejuti Rahman ◽  
Syeda Faiza Ahmed ◽  
Musabbir Ahmed Arrafi ◽  
M.A.R. Ahad
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Cognitive Skills ◽  
Positive Impact ◽  
Autism Spectrum ◽  
Activity Analysis ◽  
Automated Detection ◽  
Spectrum Disorder ◽  
Data Driven ◽  
Diagnostic Systems

Autism Spectrum Disorder (ASD) is a neuro-developmental disorder that limits social interactions, cognitive skills, and abilities. Since ASD can last during an affected person's entire life cycle, the diagnosis at the early onset can yield a significant positive impact. The current medical diagnostic systems (e.g., DSM-5/ICD-10) are somewhat subjective; rely purely on the behavioral observation of symptoms, and hence, some individuals often go misdiagnosed or late-diagnosed. Therefore, researchers have focused on developing data-driven automated diagnosis systems with less screening time, low cost, and improved accuracy while significantly reducing professional intervention. Human Activity Analysis (HAA) is considered one of the most promising niches in computer vision research. This paper aims to analyze its potentialities in the automated detection of autism by tracking the exclusive characteristics of autistic individuals such as repetitive behavior, atypical walking style, and unusual visual saliency. This review provides a detailed inspection of HAA-based autism detection literature published in 2011 on-wards depicting core approaches, challenges, probable solutions, available resources, and scopes of future exploration in this arena. According to our study, deep learning outperforms machine learning in ASD detection with a classification accuracy of 76\% to 95\% on different datasets comprise of video, image, or skeleton data that recorded participants performing a large number of actions. However, machine learning provides satisfactory results on datasets with a small number of action classes and has a range of 60\% to 93\% accuracy among numerous studies. We hope this extensive review will provide a comprehensive guideline for researchers in this field.

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.

Emotion Dysregulation in Autism Spectrum Disorder

2018 ◽  
pp. 282-298
Author(s):  
Emily Neuhaus
Keyword(s):  
Autism Spectrum Disorder ◽  
Social Communication ◽  
Educational Outcomes ◽  
Emotion Dysregulation ◽  
Research Priorities ◽  
Theoretical Models ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Spectrum Disorder

Autism spectrum disorder (ASD) is defined by deficits in social communication and interaction, and restricted and repetitive behaviors and interests. Although current diagnostic conceptualizations of ASD do not include emotional difficulties as core deficits, the disorder is associated with emotion dysregulation across the lifespan, with considerable implications for long-term psychological, social, and educational outcomes. The overarching goal of this chapter is to integrate existing knowledge of emotion dysregulation in ASD and identify areas for further investigation. The chapter reviews the prevalence and expressions of emotion dysregulation in ASD, discusses emerging theoretical models that frame emotion dysregulation as an inherent (rather than associated) feature of ASD, presents neurobiological findings and mechanisms related to emotion dysregulation in ASD, and identifies continuing controversies and resulting research priorities.

Teaching Social Scripts to Improve Social Communication for Students With Autism Spectrum Disorder

2021 ◽  
pp. 004005992110220
Author(s):  
Gretchen Scheibel ◽  
Zijie Ma ◽  
Jason C. Travers
Keyword(s):  
Autism Spectrum Disorder ◽  
Social Communication ◽  
Challenging Behaviors ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Evidence Based ◽  
Academic Difficulties ◽  
Evidence Based Practices ◽  
Social Scripts ◽  
Students With Autism

Students with Autism Spectrum Disorder are likely to demonstrate social impairments that contribute to challenging behaviors and academic difficulties. As a result, the task of improving social communication skills is a critical component to any educational program for this population. Scripting provides an evidence-based and versatile option for improving social communication, yielding valuable results while requiring limited time and resource preparation from educators. In this article, we present step by step guidance to support practitioners in using scripting interventions. Considerations are discussed for adapting this intervention to meet the needs of students across the autism spectrum and links to resources for strengthening implementation and including other evidence-based practices.

scholarly journals Cariprazine alleviates core behavioral deficits in the prenatal valproic acid exposure model of autism spectrum disorder

2021 ◽  
Author(s):  
Viktor Román ◽  
Nika Adham ◽  
Andrew G. Foley ◽  
Lynsey Hanratty ◽  
Bence Farkas ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Social Communication ◽  
Partial Agonist ◽  
Social Play ◽  
Autism Spectrum ◽  
Social Recognition ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
Exposure Model ◽  
Behavioral Deficits

Abstract Rationale Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by deficits in social communication and interaction and restricted, repetitive behaviors. The unmet medical need in ASD is considerable since there is no approved pharmacotherapy for the treatment of these deficits in social communication, interaction, and behavior. Cariprazine, a dopamine D3-preferring D3/D2 receptor partial agonist, is already approved for the treatment of schizophrenia and bipolar I disorder in adults; investigation in patients with ASD is warranted. Objectives The aim of this study was to investigate the effects of cariprazine, compared with risperidone and aripiprazole, in the rat prenatal valporic acid (VPA) exposure model on behavioral endpoints representing the core and associated symptoms of ASD. Methods To induce the ASD model, time-mated Wistar rat dams were treated with VPA during pregnancy. Male offspring were assigned to groups and studied in a behavioral test battery at different ages, employing social play, open field, social approach-avoidance, and social recognition memory tests. Animals were dosed orally, once a day for 8 days, with test compounds (cariprazine, risperidone, aripiprazole) or vehicle before behavioral assessment. Results Cariprazine showed dose-dependent efficacy on all behavioral endpoints. In the social play paradigm, only cariprazine was effective. On the remaining behavioral endpoints, including the reversal of hyperactivity, risperidone and aripiprazole displayed similar efficacy to cariprazine. Conclusions In the present study, cariprazine effectively reversed core behavioral deficits and hyperactivity present in juvenile and young adult autistic-like rats. These findings indicate that cariprazine may be useful in the treatment of ASD symptoms.

scholarly journals FASTER and SCOTT&EVA trainings for adults with high-functioning autism spectrum disorder (ASD): study protocol for a randomized controlled trial

Trials ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ludger Tebartz van Elst ◽  
Thomas Fangmeier ◽  
Ulrich Max Schaller ◽  
Oliver Hennig ◽  
Meinhard Kieser ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Social Communication ◽  
Primary Outcome ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Social Responsiveness ◽  
Evidence Based ◽  
Computer Based Training ◽  
Randomized Controlled ◽  
Computer Based

Abstract Background Autism spectrum disorder (ASD) is a chronic neurodevelopmental condition with a prevalence rate above 1%, characterized by deficits in social communication and interaction; restrictive, repetitive patterns of behavior, interests, or activities; and a preference for sameness and routines. The majority of adult ASD patients suffer from comorbid conditions such as depression and anxiety. Therapy options for adult ASD patients are lacking, with presently no available evidence-based interventions in Germany. Recently, two interventions to improve social responsiveness have been published. FASTER (“Freiburger Asperger-Spezifische Therapie für ERwachsene” = Freiburg Asperger-specific therapy for adults) is a manualized group psychotherapy program including three modules on psychoeducation, stress regulation management, and non-verbal and verbal social communication training with videotaped tasks. SCOTT&EVA (“Social Cognition Training Tool”, and its enhancement “Emotionen Verstehen und Ausdruecken” = understanding and expressing emotions) is a computer-based training program to enhance social cognition including video and audio material of emotional expressions and complex real-life social situations. Initial studies for both programs have shown good feasibility and efficacy. Methods Three hundred sixty adult participants with an autism spectrum disorder (ASD) will take part in a randomized controlled three-armed multi-center trial to prove the efficacy of manualized group psychotherapy and a manualized computer-based training program. Both interventions will be compared with a treatment as usual (TAU) group, aiming to establish evidence-based psychotherapy approaches for adult individuals with ASD. The primary outcome is evaluated by parents, spouses, or others who have sufficient insight into the respective participant’s social communication and interaction, and will be measured with the Social Responsiveness Scale. First, each of both interventions will be compared to TAU. If at least one of the differences is significant, both interventions will be compared against each other. The primary outcome will be measured at baseline (T0) and 4 months after baseline (T1). Discussion The trial is the first to validate psychiatric therapeutic and training interventions for adult ASD patients in Germany. A trial is needed because the prevalence of ASD in adulthood without intellectual disability is high, and no evidence-based intervention can be offered in Germany. Trial registration German Clinical Trial Register DRKS00017817. Registered on 20 April 2020.

RELATIONSHIP AMONG SENSORY ISSUES AND REPETITIVE BEHAVIORS IN CHILDREN WITH AUTISM SPECTRUM DISORDER

2020 ◽  
pp. 59-64
Author(s):  
OJS Admin
Keyword(s):  
Autism Spectrum Disorder ◽  
Social Communication ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
Sensory Stimuli ◽  
Key Features ◽  
Communication Impairments ◽  
Autism Disorder

Sensory issues and Repetitive Behaviors are the key features of Autism Disorder Syndrome (ASD). This is a neurodevelopmental condition marked by social communication impairments and the occurrence ofrestricted and repeated behavioral habits and desires, including irregular responses to sensory stimuli.

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