Autism Spectrum Disorder Screening Research Articles

Clinical Significance of Serum Multiple Radical Scavenging Activity Assay (MULTIS) in a Diagnostic Screening of Autism Spectrum Disorders

Clinical Significance of Serum Multiple Radical Scavenging Activity Assay (MULTIS) in a Diagnostic Screening of Autism Spectrum Disorders

41 Content Analysis of the Recommendations from Project Echo Ontario Autism

Abstract Background The rising prevalence of autism spectrum disorder (ASD) diagnoses has caused an increased number of community practitioners to care for this population. However, community practitioners report a lack of knowledge and confidence in treating these patients, resulting in unmet healthcare needs. The Extension of Community Healthcare Outcomes (ECHO) Autism model aims to address this through case-based and didactic learning to help guide community practitioners in providing comprehensive, best-practice care for ASD screening, diagnosis, and management of co-occurring conditions. Each ECHO session involves a case presentation followed by a list of recommendations generated by community participants and an interdisciplinary ‘hub’ team. While ECHO Autism has been shown to improve physicians' abilities to care for children with ASD in their practices, recommendations stemming from ECHO cases have yet to be characterized and may help guide future care. Objectives To quantify and characterize the common categories within ECHO Autism Ontario case recommendations. Design/Methods A content analysis of 422 recommendations from 61 ECHO cases was conducted to identify categories of recommendations and their frequencies. Three researchers independently coded recommendations from five ECHO cases, from which an original coding guide was developed. The researchers then independently coded the remaining cases and met regularly with the ECHO lead to modify and consolidate the codes and coding guide. From there, categories and sub-categories from the various codes were identified. Finally, the frequencies of each code and category were calculated. Results Fifty-seven codes were included in the final coding guide and grouped into eight broad categories. Categories included: 1) diagnosis; 2) concurrent mental and physical health conditions; 3) referrals to allied health providers and other specialists; 4) accessing community resources, such as parent and sibling support groups; 5) providing education and guidance to physicians, patients, and families; 6) management strategies such as nutrition, physical activity, and social skills; and 7) patient and family-centered care. A COVID-19 category was added, as many of the later recommendations were adapted to online service delivery. An analysis of the frequency of codes found that 1,384 total in-text codes were distributed amongst the various categories. The three highest frequencies of categories were providing general guidance and education (22%), accessing resources (16%), and referrals (15%). Conclusion This is the first time recommendations from ECHO Autism have been characterized and quantified. Our results, particularly the most common category of providing general guidance and education about ASD, show there is still important work to be done with educating clinicians and families about aspects of ASD. Furthermore, findings from this study should inform Pediatrics residency programs about real-world knowledge gaps in ASD care, and may help create more tailored ASD training programs and educational materials.

Adaptation of autism spectrum screening questionnaire (ASSQ) for use in Georgian school settings

Purpose Even though 95% of children with neurodevelopmental disorders, including autism spectrum disorder (ASD), live in low- and middle-income countries, there is a dearth of studies from these countries, including the Republic of Georgia. Several ASD screening tools are available, but few are validated for use in Georgian or other smaller countries. This study aims to adapt and validate the autism spectrum screening questionnaire (ASSQ) for use in Georgia. Design/methodology/approach The ASSQ was administered for all third-grade students in 402 schools in the five main Georgian cities, n = 27,336. Prior to use, the 27-item ASSQ was translated, back-translated and adapted for use in Georgia. A total of 16,556 students (approximately 61%) were assessed by a parent and/or teacher. Optimal cutoff scores were estimated. Randomly chosen children who screened positive (n = 173) and negative (n = 127) were offered comprehensive assessment using standardized diagnostic procedures. Findings Data from 15,510 parents- and 13,517 teachers-administered ASSQ revealed statistically significant differences in median and cutoff scores between parents and teachers: 7 versus 4 and 9 versus 6, respectively. Cutoff score = 14, on either parent or teacher ASSQ, had sensitivity of 0.94, indicating that it can be used in school settings. Originality/value The Georgian adaptation of the ASSQ creates opportunity for further ASD research, while also providing a valid screening tool for clinicians. Data from Georgia will add to the growing understanding of the broader ASD phenotype.

Screening of autism spectrum disorder based on task-free fMRI using tensor decomposition approach

In recent decades, autism spectrum disorder (ASD) has displayed an incremental prevalence rate. Due to the unavailability of a definite cure, the early diagnosis of the disorder is of high significance. There is evidence suggesting the discriminable differences between the resting state networks of people who suffer from the disorder and healthy individuals. This distinguishability allows for the utilization of fMRI imaging to perform as a good instrument for the identification of autism spectrum disorder. In this paper, a tensor decomposition method for the diagnosis of autism from fMRI images is presented. The selected dataset for testing the performance of the proposed algorithm is ABIDE1. All sites of the ABIDE1 are used for training the algorithm which is a challenging problem in fMRI data analysis. Our proposed method successfully achieves the classification performance of about 60% for all site analyses.

A Clinical Validity-Preserving Machine Learning Approach for Behavioral Assessment of Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neuropsychiatric disorder associated with critical challenges related to social, communication, and behavioral issues. Recent studies have proposed machine learning (ML) techniques for rapid and accurate assessment of ASD. However, the mismatch between the ML techniques and the clinical basis of ASD assessment reduces the effectiveness of ML-based assessment tools. The present study proposed an approach that utilized the potential of ML modeling and preserved the clinical relevance of the assessment instrument used. Experimental results of the empirical scoring algorithm and multiple ML models employed revealed the possibility of achieving a clinically valid ML-based ASD assessment tool. This study provides a roadmap for real-life implementation of ML-based ASD screening and diagnostic tools that comprise few behavioral features and maintain clinical relevance.

The Technology-Oriented Pathway for Auxiliary Diagnosis in the Digital Health Age: A Self-Adaptive Disease Prediction Model.

The advent of the digital age has accelerated the transformation and upgrading of the traditional medical diagnosis pattern. With the rise of the concept of digital health, the emerging information technologies, such as machine learning (ML) and data mining (DM), have been extensively applied in the medical and health field, where the construction of disease prediction models is an especially effective method to realize auxiliary medical diagnosis. However, the existing related studies mostly focus on the prediction analysis for a certain disease, using models with which it might be challenging to predict other diseases effectively. To address the issues existing in the aforementioned studies, this paper constructs four novel strategies to achieve a self-adaptive disease prediction process, i.e., the hunger-state foraging strategy of producers (PHFS), the parallel strategy for exploration and exploitation (EEPS), the perturbation–exploration strategy (PES), and the parameter self-adaptive strategy (PSAS), and eventually proposes a self-adaptive disease prediction model with applied universality, strong generalization ability, and strong robustness, i.e., multi-strategies optimization-based kernel extreme learning machine (MsO-KELM). Meanwhile, this paper selects six different real-world disease datasets as the experimental samples, which include the Breast Cancer dataset (cancer), the Parkinson dataset (Parkinson’s disease), the Autistic Spectrum Disorder Screening Data for Children dataset (Autism Spectrum Disorder), the Heart Disease dataset (heart disease), the Cleveland dataset (heart disease), and the Bupa dataset (liver disease). In terms of the prediction accuracy, the proposed MsO-KELM can obtain ACC values in analyzing these six diseases of 94.124%, 84.167%, 91.079%, 72.222%, 70.184%, and 70.476%, respectively. These ACC values have all been increased by nearly 2–7% compared with those obtained by the other models mentioned in this paper. This study deepens the connection between information technology and medical health by exploring the self-adaptive disease prediction model, which is an intuitive representation of digital health and could provide a scientific and reliable diagnostic basis for medical workers.

Autism detection for toddlers from behavioural indicators using classification techniques

Autism spectrum disorder (ASD) is a condition associated with impairments in communication, social, and repetitive behaviour; the degree of impairment varies between individuals with ASD. Since ASD has a substantial impact on the individual, caregivers, and family members due to the social and economic costs involved, early ASD screening becomes fundamental to enable faster access to healthcare resources. One of the important studied groups in ASD research is toddlers – detecting autistic traits at an early stage can help physicians develop treatment plans. This paper aims to improve the detection rate of ASD screening for toddlers using a data driven approach by identifying the impactful feature set related to ASD, and then processing these features using classification algorithms to accurately screen for ASD. To achieve the aim, a data driven framework consisting of feature selection and classification algorithms is proposed, and then implemented on a real dataset related to the ASD screening of toddlers. Empirical evaluations on the ASD screening dataset using different classification methods reveal that when support vector machine (SVM) or Naïve Bayes are integrated with the proposed framework good predictive models are constructed for toddler ASD screening. These predictive models can be adopted by different medical staff and caregivers to replace scoring functions of conventional screening methods.

Screening for Autism Spectrum Disorders in Underserved Populations: Global Versus Specific Surveillance Instruments

Objective: The identification of developmental delays during well-child visits is insufficient with clinical judgment alone (30-50%). Method: We analyzed the usefulness of clustering communication and social-personal areas from neurodevelopmental screening ASQ-3 and Denver II PRUNAPE questionnaires, compared with M-CHAT and ADOS as the initial screening for detection of Autism Spectrum Disorders (ASD) at well-child controls at primary-care settings in lowand middle-income countries (LMIC). Results: One hundred children at the Department of Pediatrics from 12 to 66 months (average: 35 months), were assessed by a developmental pediatrician, trained and supervised by ECHO-model. The assessment was completed with a full assessment for autism with a child neurologist, and a child-juvenile psychiatrist. Thirty-one of 100 children arrived at autism spectrum disorder diagnostic criteria. Conclusion: Failure at ASQ-3 communication plus social-personal clusters was in good agreement with M-CHAT to predict risk criteria for autism compared with a full assessment protocol. Clustering Communication and Personal-Social developmental skills from global screeners were sufficient at the pediatric visits, let differential diagnoses with global developmental delay, and communication developmental disorders. These scales covered a wide range of ages for early identification of children with an autism spectrum disorder in primary care settings within an on-site or online format

Prevalence of Autism Spectrum Disorder (ASD) in all Individuals Diagnosed with Down Syndrome (DS): A Systematic Review and Meta-analysis Protocol

Background: Traditionally, autism spectrum disorder in people with Down syndrome was believed to be uncommon. This misconception is rooted in the challenges that a dual diagnosis poses. In fact, evidence indicates that children with Down syndrome are at risk for autism spectrum disorder with a potentially higher prevalence than the typically developing population. The purpose of this review is to determine the reported prevalence rate of autism spectrum disorder in all individuals with Down syndrome in comparison to the prevalence rate of autism spectrum disorder in the typical population when specific diagnostic tools are used. Methods: A systematic review will be conducted of the prevalence and incidence data and perform a meta-analysis of these results. This study will consider all studies that reported on children and adults with an existing diagnosis of Down syndrome and diagnosed by the standardized assessments for autism spectrum disorder. The diagnoses made by team assessment (psychologist, psychiatrist & developmental pediatrician) will also be considered according to DSM-III, DSM-IV or DSM-V criteria for diagnosing autism spectrum disorder or if they use autism spectrum disorder screeners. Studies will be considered from all countries that have data reporting prevalence on this topic. The language restrictions will not be applied attempting to translate studies that are not in English. The five databases (MEDLINE, Embase, PsychINFO, Scopus, and CINAHL) will be searched. Two reviewers will conduct all screening and data extraction independently. The articles will be categorized according to key findings and a critical appraisal performed. Discussion: The results of this review will bring increased awareness of the presence of autism spectrum disorder in individuals with Down syndrome. In doing so, this may facilitate a recommendation for screening and diagnosis of autism spectrum disorder in all individuals with Down syndrome. Based on the research demonstrating the benefits of early identification and intervention on the outcomes of children with autism, we anticipate similar benefits in this population. This will guide the allocation of resources and direct future research.

Separate scoring algorithms for specific identification priorities optimize the screening properties of the Screening Tool for Autism in Toddlers (STAT).

The Screening Tool for Autism in Toddlers (STAT) is a validated stage-2 autism spectrum disorder (ASD) screening measure that takes 20 minutes to administer and comprises 12 play-based items that are scored according to specific criteria. This study examines an expanded version (STAT-E) that includes the examiner's subjective ratings of children's social engagement (SE) and atypical behaviors (AB) in the scoring algorithm. The sample comprised 238 children who were 24-35 months old. The STAT-E assessors had limited ASD experience to mimic its use by community-based non-specialists, and were trained using a scalable web-based platform. A diagnostic evaluation was completed by clinical experts who were blind to the STAT-E results. Logistic regression, ROC curves, and classification matrices and metrics were used to determine the screening properties of STAT-E when scored using the original STAT scoring algorithm versus a new algorithm that included the SE and AB ratings. Inclusion of the SE and AB ratings improved positive risk classification appreciably, while the specificity declined. These resultssuggest that the STAT-E using the original STAT scoring algorithm optimizes specificity, while the STAT-E scoring algorithm with the two new ratings optimizes the positive risk classification. Using multiple scoring algorithms on the STAT may provide improved screening accuracy for diverse contexts, and a scalable web-based tutorial may be a pathway for increasing the number of community providers who can administer the STAT and contribute toward increased rates of autism screening.

Vision-Based Approach for Autism Diagnosis Using Eye Tracking based on Machine Learning and Deep Learning

Although the early diagnosis of autism spectrum disorder (ASD) is highly sought, it is still a challenging task that requires a battery of cognitive tests and hours of clinical exams to be properly accomplished. ASD may manifest itself in various ways, which makes diagnosing it even more challenging. However, although diagnostic tests are generally devised by specialists, human mistakes are still possible. Computer-assisted technologies can be quite useful in this respect for aiding in the selection procedure. The use of eye tracking as a crucial component of ASD screening evaluation based on the distinctive characteristics of the eye gaze is continued in this article. This research adds to the growing body of evidence that eye tracking technologies may help in ASD screening. Eye tracking, visualization, using machine learning and deep learning are part of the suggested solution. Eye-tracking scan paths were first converted into a visual representation in the form of a series of pictures. After that, an image classification job was taught to a convolutional neural network. The findings of the experiments showed that using a visual representation simplified the diagnostic process while also achieving excellent accuracy. The convolutional neural network model, in particular, can attain promising classification accuracy. This shows that visuals are capable of encoding information about gaze movements and its underlying dynamics. Based on the greatest information coefficient, we examined potential relationships between autism severity and eye movement dynamics. The results demonstrate that using the great potential in combining eye tracking, visualization, machine learning, and deep learning to create an impartial tool to help in ASD screening. Generally, the approach we propose may be used to identify various diseases, including neurodevelopmental problems.

Mobile and online consumer tools to screen for autism do not promote equity.

Many parents wonder if their child might have autism. Many parents use their smartphones to answer health questions. We asked, "How easy or hard is it for parents to use their smartphones to find 'tools' to test their child for signs of autism?" After doing pretend parent searches, we found that only one in 10 search results were tools to test children for autism. These tools were not designed for parents who have low income or other challenges such as low literacy skills, low English proficiency, or not being tech-savvy.

Is traditional back translation enough? Comparison of translation methodology for an ASD screening tool.

Early identification of autism spectrum disorders (ASD) in non-English speaking children often relies on translation of English ASD screening measures. Most measures employ a forward-back translation approach, despite data suggesting that this method may result in poor psychometric properties. Some studies in non-ASD fields have suggested that a rigorous method of translation with cultural adaptation may yield better psychometric properties, but no studies in the ASD field have compared the two approaches directly. This investigation compares these two translation methodologies to determine if they produce psychometrically similar or dissimilar measures. Three hundred and eighty US-based Spanish-speaking caregivers of children 8-16 months old were randomly assigned to complete either a forward-back Spanish translation or a rigorous translation with cultural adaptation of a parent-report ASD screening tool. Measurement invariance analyses determined that the two translations were psychometrically dissimilar. Additional qualitative explanatory methods using cognitive interviews examined textual differences and participant preferences between non-invariant items. LAY SUMMARY: Autism screening questionnaires created in English need to be translated into other languages so non-English speaking parents can fill them out accurately. Traditionally, researchers have not considered cultural differences when they translate these questionnaires. When we compared a direct translation to a translation with cultural adaptations, the two questionnaires were statistically different. Parents interpreted and responded to the same questions differently, depending on which version they filled out.

Use of Oculomotor Behavior to Classify Children with Autism and Typical Development: A Novel Implementation of the Machine Learning Approach.

This study segmented the time series of gaze behavior from nineteen children with autism spectrum disorder (ASD) and 20 children with typical development in a face-to-face conversation. A machine learning approach showed that behavior segments produced by these two groups of participants could be classified with the highest accuracy of 74.15%. These results were further used to classify children using a threshold classifier. A maximum classification accuracy of 87.18% was achieved, under the condition that a participant was considered as 'ASD' if over 46% of the child's 7-s behavior segments were classified as ASD-like behaviors. The idea of combining the behavior segmentation technique and the threshold classifier could maximally preserve participants' data, and promote the automatic screening of ASD.

Bias in measurement of autism symptoms by spoken language level and non-verbal mental age in minimally verbal children with neurodevelopmental disorders.

Increasing numbers of children with known genetic conditions and/or intellectual disability are referred for evaluation of autism spectrum disorder (ASD), highlighting the need to refine autism symptom measures to facilitate differential diagnoses in children with cognitive and language impairments. Previous studies have reported decreased specificity of ASD screening and diagnostic measures in children with intellectual disability. However, little is known about how cognitive and language abilities impact the measurement of specific ASD symptoms in this group. We aggregated a large sample of young children (N = 1196; aged 31–119 months) to examine measurement invariance of ASD symptoms among minimally verbal children within the context of the Autism Diagnostic Observation Schedule (ADOS) Module 1. Using confirmatory factor analysis (CFA) and moderated non-linear factor analysis (MNLFA), we examined how discrete behaviors were differentially associated with the latent symptom domains of social communication impairments (SCI) and restricted and repetitive behaviors (RRB) across spoken language levels and non-verbal mental age groupings. While the two-factor structure of SCI and RRB held consistently across language and cognitive levels, only partial invariance was observed for both ASD symptom domains of SCI and RRB. Specifically, four out of the 15 SCI items and one out of the three RRB items examined showed differential item functioning between children with “Few to No Words” and those with “Some Words”; and one SCI item and one RRB item showed differential item functioning across non-verbal mental age groups. Moreover, even after adjusting for the differential item functioning to reduce measurement bias across groups, there were still differences in ASD symptom domain scores across spoken language levels. These findings further underscore the influence of spoken language level on measurement of ASD symptoms and the importance of measuring ASD symptoms within refined spoken language levels, even among those with minimal verbal abilities.

Children neuropsychological and behavioral scale-revision 2016 in the early detection of autism spectrum disorder.

BackgroundThe Children Neuropsychological and Behavioral Scale-Revision 2016 (CNBS-R2016) is a widely used developmental assessment tool for children aged 0–6 years in China. The communication warning behavior subscale of CNBS-R2016 is used to assess the symptoms of autism spectrum disorder (ASD), and its value of >30 points indicates ASD based on CNBS-R2016. However, we observed that children with relatively lower values were also diagnosed with ASD later on in clinical practice. Thus, this study aimed to identify the suitable cutoff value for ASD screening recommended by the communication warning behavior of CNBS-R2016.Materials and methodsA total of 90 typically developing (TD) children and 316 children with developmental disorders such as ASD, developmental language disorder (DLD), and global developmental delay (GDD; 130 in the ASD group, 100 in the DLD group, and 86 in the GDD group) were enrolled in this study. All subjects were evaluated based on the CNBS-R2016. The newly recommended cutoff value of communication warning behavior for screening ASD was analyzed with receiver operating curves.ResultsChildren in the ASD group presented with lower developmental levels than TD, DLD, and GDD groups in overall developmental quotient assessed by CNBS-R2016. We compared the consistency between the scores of communication warning behavior subscale and Autism Behavior Checklist (ABC), Childhood Autism Rating Scale (CARS), Autism Diagnostic Observation Schedule, second edition (ADOS-2), and clinical diagnosis for the classification of ASD at a value of 30 based on the previously and newly recommended cutoff value of 12 by the CNBS-R2016. The Kappa values between the communication warning behavior and ABC, CARS, ADOS-2, and clinical diagnosis were 0.494, 0.476, 0.137, and 0.529, respectively, with an agreement rate of 76.90%, 76.26%, 52.03%, and 82.27%, respectively, when the cutoff point was 30. The corresponding Kappa values were 0.891, 0.816, 0.613, and 0.844, respectively, and the corresponding agreement rate was 94.62%, 90.82%, 90.54%, and 93.10%, respectively, when the cutoff point was 12.ConclusionThe communication warning behavior subscale of CNBS-R2016 is important for screening ASD. When the communication warning behavior score is 12 points or greater, considerable attention and further comprehensive diagnostic evaluation for ASD are required to achieve the early detection and diagnosis of ASD in children.

MRI Findings in Prematurely-Born Adolescents and Young Adults Who Screen Positive for Autism Spectrum Disorder

Autism spectrum disorder (ASD) is three to four times more common in prematurely-born children (<37 weeks' gestation) than term-born children.1 The relationship between lower gestational age and ASD is independent of familial and environmental factors, suggesting a potential causal link.2 Term-equivalent magnetic resonance imaging (MRI) findings do not reliably predict ASD in preterm infants,3 and little is known about MRI findings in preterm children with ASD imaged later in childhood. To address this gap, we screened adolescents and young adults for ASD as part of a longitudinal cohort study of MRI in preterm infants4 and compared neonatal and follow-up MRI findings by ASD screening status.

Disparities in Early Intervention Program Participation by Children With Autism Spectrum Disorder in a US Metropolitan Area, 2006 to 2016

Multiple studies have shown the value of early interventions for autism spectrum disorder (ASD). In the US, the Early Intervention Program (EIP) is mandated by law (Part C of the Individuals With Disabilities Education Act [IDEA]) to provide services to all young children with delays or disabilities. However, the extent to which children with ASD participate in this key service system is unknown. To evaluate EIP use by children with ASD from 2006 to 2016 and to describe the factors associated with EIP participation. This cross-sectional study used repeated data collected from 2006 to 2016 by active ASD surveillance of the New York-New Jersey metropolitan area as reported in the New Jersey Autism Study. The New Jersey Autism Study identified 4050 children aged 8 years with ASD from 2006 to 2016. Demographic and clinical data were collected and participation in an EIP was assessed through active surveillance. Data were analyzed from June to December 2021. Sociodemographic factors associated with the outcome of EIP participation. Participation in an EIP assessed at age 8 years. Demographic, ecological, and clinical factors, as well as temporal patterns, were examined by using standard and multilevel logistic regression models. Among 4050 children aged 8 years with ASD by active surveillance, 1887 (46.6%) received EIP services. Of these children, 3303 (81.6%) were boys; 1105 (27.3%) were Hispanic, 801 (19.8%) were non-Hispanic Black, 1816 (44.8%) were non-Hispanic White, and 328 (8.1%) were non-Hispanic other (included Alaska Native or American Indian and Asian or Pacific Islander). In adjusted regression models, non-Hispanic Black children with ASD had lower odds of EIP participation (adjusted odds ratio [AOR], 0.67; 95% CI, 0.54-0.84) compared with their non-Hispanic White peers, and children residing in affluent areas had higher odds of receiving EIP services (AOR, 1.71; 95% CI, 1.36-2.15) compared with children residing in underserved areas. Children with ASD born in 2008 had higher odds of EIP participation than children born in 1998 (AOR, 2.64; 95% CI, 2.07-3.36). Early identification of ASD is an important public health priority and receipt of EIP services may improve ASD outcomes. Approximately half of the population of children aged 8 years with ASD received EIP services between 2006 and 2016, and EIP participation by children with ASD increased during the 10-year period. However, receipt of EIP services was marked by strong socioeconomic status- and race and ethnicity-based disparities. Universal ASD screening and additional strategies are needed to address disparities and to increase access to EIP services.

The Screening Program for Autism Spectrum Disorders in the West of Iran

Background: There are few epidemiological data regarding the autism spectrum disorders (ASD) screening program in Iran, and as yet, screening for children at the age of 18 months has not been performed. Aim: The present study aimed to perform a screening program for children with ASD in Iran. Method: This cross-sectional study was conducted for screening children with ASD. The age and stages Questionnaire-18 (ASQ-18) month questionnaire was sent to all parents of children via a mobile-based application.The Modified Checklist for Autism in Toddlers (MCHAT) questionnaire was mailed if the child's score was below the cutoff. For those with positive M-CHATs, the M-CHAT/F Follow-up interview was performed. Finally, children with ASD, according to The Autism Diagnostic Interview-Revised (ADI-R) and examination, were diagnosed. Result: Of these children, 1221 (48%) were girls and 1341 (52%) were boys. Of the parent respondents, 232 (9%) children had a positive ASQ-18 questionnaire. For this purpose, the MCHAT was sent to the parents of these children. Of the 232 children with ASQ positive, 179 (77.1%) parents completed M-CHAT. On the M-CHAT, 43 (24.0%) children screened positive and 136 (76.0%) children passed on M-CHAT-R screening. In this stage of the 43 screenpositive children, health care providers asked parents to refer to their children for performing the M-CHAT at follow up. For a total of 33 children, the M-CHAT follow-up was performed. Of these samples, 7 children had positive M-CHAT follow-up. Therefore, ADI-R was conducted for these children to diagnose ASD. In the end, based on ADI-R and diagnostic evaluation performed by the child and adolescent psychologists, 7 children had ASD. Conclusion: The findings of the present study show that a mobile-based application can be utilized for screening children at risk of ASD. Therefore, we suggest that the application be used for screening children with ASD.

The Validity of the Modified Checklist for Autism in Toddlers, Revised with Follow-Up (M-CHAT-R/F) Thai Version as the Autism Screening Application: A Pilot Study

Background: Autism spectrum disorder (ASD) is a neurodevelopmental disorder where symptoms first appear during the first two years of life. Early screening and diagnosis are the keys to identifying toddlers with ASD so that early intervention could be recommended. The Modified Checklist for Autism in Toddlers, Revised with Follow-up (M-CHAT-R/F) is a worldwide ASD screening instrument with adequate validity, which is used to screen ASD in toddlers between the ages of 16 and 30 months. Objective: To compute the sensitivity and specificity of the Thai version of the M-CHAT-R/F on the Autism Screening application (AS app) among children ranging from 16 to 30 months of age. Materials and Methods: The permission to translate M-CHAT-R/F into Thai and place it on an AS app on the computer website, on iOS, and on Android smartphones/tablets was granted. The forward translation of M-CHAT-R/F to Thai and the back translation into English were conducted by the language experts. The content was proofed and edited by two Child and Adolescent Psychiatrists and by one Developmental Pediatrician. The final Thai version of the M-CHAT-R/F was placed in the AS app and was used in the pilot study, which consisted of 15 toddlers with ASD and 15 toddlers with typical development. The screening results from the AS app were compared to the diagnoses from either Child and Adolescent Psychiatrists or Developmental Pediatricians to calculate the sensitivity and specificity. Results: The number of female(s) and male(s) among typical toddlers and toddlers with ASD were 1/14 and 2/13. The mean ages were 16 (SD 0)/26 (SD 4.31) and 23 (SD 9.90)/29.08 (SD 1.71) months old. The Thai M-CHAT-R/F gave the sensitivity and specificity of 100% at the cutoff score of 6 or greater from the full score of 20. Conclusion: The AS app had a high level of validity at the cutoff score of 6 or greater. Keywords: Application; Screening; Autism; Toddlers; Thai