scholarly journals European experts develop a new framework to screen early ASD

2018 ◽  
Keyword(s):  
Autism Spectrum Disorder ◽  
Early Detection ◽  
Content Validity ◽  
Universal Screening ◽  
Screening Method ◽  
Autism Spectrum ◽  
Screening Methods ◽  
Novel Approach ◽  
Outcomes For Children ◽  
New Framework

Early detection of Autism Spectrum Disorder (ASD) can improve outcomes for children, yet the effectiveness and validity of universal screening methods has been questioned. Now, researchers have created a new framework to generate a valid early ASD screening method using a novel approach based on “face and content validity”.

scholarly journals An accessible and efficient autism screening method for behavioural data and predictive analyses

2018 ◽  
Vol 25 (4) ◽  
pp. 1739-1755 ◽  
Author(s):  
Fadi Thabtah
Keyword(s):  
Autism Spectrum Disorder ◽  
Waiting Times ◽  
Screening Method ◽  
Autistic Traits ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder ◽  
Screening Methods ◽  
Autism Screening ◽  
Predictive Analyses

Autism spectrum disorder is associated with significant healthcare costs, and early diagnosis can substantially reduce these. Unfortunately, waiting times for an autism spectrum disorder diagnosis are lengthy due to the fact that current diagnostic procedures are time-consuming and not cost-effective. Overall, the economic impact of autism and the increase in the number of autism spectrum disorder cases across the world reveal an urgent need for the development of easily implemented and effective screening methods. This article proposes a new mobile application to overcome the problem by offering users and the health community a friendly, time-efficient and accessible mobile-based autism spectrum disorder screening tool called ASDTests. The proposed ASDTests app can be used by health professionals to assist their practice or to inform individuals whether they should pursue formal clinical diagnosis. Unlike existing autism screening apps being tested, the proposed app covers a larger audience since it contains four different tests, one each for toddlers, children, adolescents and adults as well as being available in 11 different languages. More importantly, the proposed app is a vital tool for data collection related to autism spectrum disorder for toddlers, children, adolescent and adults since initially over 1400 instances of cases and controls have been collected. Feature and predictive analyses demonstrate small groups of autistic traits improving the efficiency and accuracy of screening processes. In addition, classifiers derived using machine learning algorithms report promising results with respect to sensitivity, specificity and accuracy rates.

scholarly journals Utilizing two-tiered screening for early detection of autism spectrum disorder

Autism ◽  
2017 ◽  
Vol 22 (7) ◽  
pp. 881-890 ◽  
Author(s):  
Meena Khowaja ◽  
Diana L Robins ◽  
Lauren B Adamson
Keyword(s):  
Early Detection ◽  
Young Children ◽  
False Positive ◽  
Screening Tool ◽  
False Positive Rate ◽  
Autism Spectrum ◽  
Screening Methods ◽  
Positive Rate ◽  
Level 1 ◽  
Level 2

Despite advances in autism screening practices, challenges persist, including barriers to implementing universal screening in primary care and difficulty accessing services. The high false positive rate of Level 1 screening methods presents especially daunting difficulties because it increases the need for comprehensive autism evaluations. This study explored whether two-tiered screening—combining Level 1 (Modified Checklist for Autism in Toddlers, Revised with Follow-Up) and Level 2 (Screening Tool for Autism in Toddlers and Young Children) measures—improves the early detection of autism. This study examined a sample of 109 toddlers who screened positive on Level 1 screening and completed a Level 2 screening measure prior to a diagnostic evaluation. Results indicated that two-tiered screening reduced the false positive rate using published Screening Tool for Autism in Toddlers and Young Children cutoffs compared to Level 1 screening alone, although at a cost to sensitivity. However, alternative Screening Tool for Autism in Toddlers and Young Children scoring in the two-tiered screening improved both positive predictive value and sensitivity. Exploratory analyses were conducted, including comparison of autism symptoms and clinical profiles across screening subsamples. Recommendations regarding clinical implications of two-tiered screening and future areas of research are presented.

scholarly journals Multisite Autism Spectrum Disorder Classification Using Convolutional Neural Network Classifier and Individual Morphological Brain Networks

2021 ◽  
Vol 14 ◽  
Author(s):  
Jingjing Gao ◽  
Mingren Chen ◽  
Yuanyuan Li ◽  
Yachun Gao ◽  
Yanling Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Autism Spectrum Disorder ◽  
Convolutional Neural Network ◽  
Structural Mri ◽  
Autism Spectrum ◽  
Brain Network ◽  
Spectrum Disorder ◽  
Structural Covariance ◽  
Almost All ◽  
New Framework

Autism spectrum disorder (ASD) is a range of neurodevelopmental disorders with behavioral and cognitive impairment and brings huge burdens to the patients’ families and the society. To accurately identify patients with ASD from typical controls is important for early detection and early intervention. However, almost all the current existing classification methods for ASD based on structural MRI (sMRI) mainly utilize the independent local morphological features and do not consider the covariance patterns of these features between regions. In this study, by combining the convolutional neural network (CNN) and individual structural covariance network, we proposed a new framework to classify ASD patients with sMRI data from the ABIDE consortium. Moreover, gradient-weighted class activation mapping (Grad-CAM) was applied to characterize the weight of features contributing to the classification. The experimental results showed that our proposed method outperforms the currently used methods for classifying ASD patients with the ABIDE data and achieves a high classification accuracy of 71.8% across different sites. Furthermore, the discriminative features were found to be mainly located in the prefrontal cortex and cerebellum, which may be the early biomarkers for the diagnosis of ASD. Our study demonstrated that CNN is an effective tool to build the framework for the diagnosis of ASD with individual structural covariance brain network.

scholarly journals Alterations of the endocannabinoid system and its therapeutic potential in autism spectrum disorder

Open Biology ◽  
2021 ◽  
Vol 11 (2) ◽  
Author(s):  
Mingyang Zou ◽  
Yu Liu ◽  
Shu Xie ◽  
Luxi Wang ◽  
Dexin Li ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Animal Model ◽  
Social Preference ◽  
Therapeutic Potential ◽  
Hydrolytic Enzyme ◽  
Endocannabinoid System ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Autistic Children ◽  
Novel Approach

Autism spectrum disorder (ASD) is a group of developmental disabilities, the aetiology of which remains elusive. The endocannabinoid (eCB) system modulates neurotransmission and neuronal plasticity. Evidence points to the involvement of this neuromodulatory system in the pathophysiology of ASD. We investigated whether there is a disruption to the eCB system in ASD and whether pharmacological modulation of the eCB system might offer therapeutic potential. We examined three major components of the eCB system—endogenous cannabinoids, their receptors and associated enzymes—in ASD children as well as in the valproic acid (VPA) induced animal model in autism. Furthermore, we specifically increased 2-arachidonoylglycerol (2-AG) levels by administering JZL184, a selective inhibitor of monoacylglycerol lipase which is the hydrolytic enzyme for 2-AG, to examine ASD-like behaviours in VPA-induced rats. Results showed that autistic children and VPA-induced rats exhibited reduced eCB content, increased degradation of enzymes and upregulation of CBRs. We found that repetitive and stereotypical behaviours, hyperactivity, sociability, social preference and cognitive functioning improved after acute and chronic JZL184 treatment. The major efficacy of JZL184 was observed after administration of a dosage regimen of 3 mg kg −1 , which affected both the eCB system and ASD-like behaviours. In conclusion, a reduced eCB signalling was observed in autistic children and in the ASD animal model, and boosting 2-AG could ameliorate ASD-like phenotypes in animals. Collectively, the results suggested a novel approach to ASD treatment.

Automatic Detection and Assessment of Autism Spectrum Disorder

2021 ◽  
pp. 396-425
Author(s):  
Thanga Aarthy M. ◽  
Menaka R. ◽  
Karthik R.
Keyword(s):  
Autism Spectrum Disorder ◽  
Early Detection ◽  
Early Diagnosis ◽  
Neurodevelopmental Disorders ◽  
Pathological Condition ◽  
Neurodevelopmental Disorder ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Attention Deficit Hyperactive Disorder ◽  
Clinical Methods

Children with neurodevelopmental disorders are increasing gradually every year. One in 100 children are diagnosed with brain function disorder. There are wide categories of disorder such as attention deficit hyperactive disorder, learning, autism spectrum disorder (ASD), etc. In this work, the focus is on ASD, its clinical methods, and analysis in various research works. ASD is a neurodevelopmental disorder which affects the intellectual functioning, social interaction (adaptive behavior), and has a specific obsessive interest. At present, there is no known cure for ASD, but the level of the pathological condition can be reduced when it is detected early. Early detection is tough and challenging till date. Many researches were carried out to ease the early detection for clinicians. Each method has its own merits and demerits. This chapter reviews and condenses various research works and their efficacy in analysis for the early diagnosis and improvement in children with autism.

scholarly journals Screening for Autism Spectrum Disorder in Premature Subjects Hospitalized in a Neonatal Intensive Care Unit

2020 ◽  
Vol 17 (20) ◽  
pp. 7675
Author(s):  
Norrara Scarlytt de Oliveira Holanda ◽  
Lidiane Delgado Oliveira da Costa ◽  
Sabrinne Suelen Santos Sampaio ◽  
Gentil Gomes da Fonseca Filho ◽  
Ruth Batista Bezerra ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Autism Spectrum Disorder ◽  
Intensive Care ◽  
Neonatal Intensive Care Unit ◽  
Neonatal Intensive Care ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Screening Methods ◽  
Early Signs ◽  
Type Of Delivery

Considering that the average age for diagnosis of autism spectrum disorder (ASD) is 4–5 years, testing screening methods for ASD risk in early infancy is a public health priority. This study aims to identify the risks for development of ASD in children born prematurely and hospitalized in a neonatal intensive care unit (NICU) and explore the association with pre-, peri- and postnatal factors. Methods: The children’s families were contacted by telephone when their child was between 18 and 24 months of age, to apply the Modified Checklist for Autism in Toddlers (M-CHAT). The sample consisted of 40 children (57.5% boys). M-CHAT screening revealed that 50% of the sample showed early signs of ASD. Although the frequency of delayed development was higher in boys, this difference was not statistically significant between the sexes (p = 0.11). Assessment of the association between perinatal conditions and early signs of autism in children hospitalized in an NICU exhibited no correlation between the factors analyzed (birth weight and type of delivery). The findings indicate a high risk of ASD in premature children, demonstrating no associations with gestational and neonatal variables or the hospitalization conditions of the NICUs investigated.

scholarly journals Motor anticipation failure in infants with autism: a retrospective analysis of feeding situations

Autism ◽  
2012 ◽  
Vol 16 (4) ◽  
pp. 420-429 ◽  
Author(s):  
Julie Brisson ◽  
Petra Warreyn ◽  
Josette Serres ◽  
Stephane Foussier ◽  
Jean Adrien-Louis
Keyword(s):  
Autism Spectrum Disorder ◽  
Early Detection ◽  
Retrospective Analysis ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Typical Development ◽  
Adults With Autism ◽  
Home Movies ◽  
Family Home ◽  
Everyday Situation

Previous studies on autism have shown a lack of motor anticipation in children and adults with autism. As part of a programme of research into early detection of autism, we focussed on an everyday situation: spoon-feeding. We hypothesize that an anticipation deficit may be found very early on by observing whether the baby opens his or her mouth in anticipation of the spoon’s approach. The study is based on a retrospective analysis from family home movies. Observation of infants later diagnosed with autism or an autism spectrum disorder (ASD) (n = 13) and infants with typical development (n = 14) between 4 and 6 months old show that the autism/ASD group has an early anticipation deficit.

scholarly journals Newly Proposed Technique for Autism Spectrum Disorder based Machine Learning

2021 ◽  
Vol 13 (2) ◽  
pp. 1-15
Author(s):  
Sherif Kamel ◽  
Rehab Al-harbi
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Screening Methods ◽  
Disease Prediction ◽  
Healthcare Organizations ◽  
Machine Learning Applications ◽  
Research Stage

The rapid growth in the number of autism disorder among toddlers needs for the development of easily implemented and effective screening methods. In this current era, the causes of Autism Spectrum Disorder (ASD) do not know yet, however, the diagnosis and detection of ASD is based on behaviours and symptoms. This paper aims to improve ASD disease prediction accuracy among toddlers by using the Logistic Regression model of Machine Learning, through the collected health care dataset and by using an algorithm for rapid classification of the behaviours to check whether the children are having autism diseases or not according to information in the dataset. Therefore, Machine Learning decreasing the time needed to detect the disorder, then providing the necessary health services early for infected toddlers to enhance their lifestyle. In healthcare, most machine learning applications are in the research stage, and to take the advantage of emerging software tools that incorporate artificial intelligence, healthcare organizations first need to overcome a variety of challenges.

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