Autism Spectrum Disorder Screening Tools Research Articles

Machine learning algorithms for identifying autism spectrum disorder through eye-tracking in different intention videos

To investigate the differences in visual perception between children with autism spectrum disorder (ASD) and typically developing (TD) children when watching different intention videos, and to explore the feasibility of machine learning algorithms in objectively distinguishing between ASD children and TD children. A total of 58 children with ASD and 50 TD children were enrolled and were asked to watch the videos containing joint intention and non-joint intention, and the gaze duration and frequency in different areas of interest were used as original indicators to construct classifier-based models. The models were evaluated in terms of the indicators such as accuracy, sensitivity, and specificity. When using eight common classifiers, including support vector machine, linear discriminant analysis, decision tree, random forest, and K-nearest neighbors (with K values of 1, 3, 5, and 7), based on the original feature indicators, the highest classification accuracy achieved was 81.90%. A feature reconstruction approach with a decision tree classifier was used to further improve the accuracy of classification, and then the model showed the accuracy of 91.43%, the specificity of 89.80%, and the sensitivity of 92.86%, with an area under the receiver operating characteristic curve of 0.909 (P<0.001). The machine learning model based on eye-tracking data can accurately distinguish ASD children from TD children, which provides a scientific basis for developing rapid and objective ASD screening tools.

Development of Deep Ensembles to Screen for Autism and Symptom Severity Using Retinal Photographs

ImportanceScreening for autism spectrum disorder (ASD) is constrained by limited resources, particularly trained professionals to conduct evaluations. Individuals with ASD have structural retinal changes that potentially reflect brain alterations, including visual pathway abnormalities through embryonic and anatomic connections. Whether deep learning algorithms can aid in objective screening for ASD and symptom severity using retinal photographs is unknown.ObjectiveTo develop deep ensemble models to differentiate between retinal photographs of individuals with ASD vs typical development (TD) and between individuals with severe ASD vs mild to moderate ASD.Design, Setting, and ParticipantsThis diagnostic study was conducted at a single tertiary-care hospital (Severance Hospital, Yonsei University College of Medicine) in Seoul, Republic of Korea. Retinal photographs of individuals with ASD were prospectively collected between April and October 2022, and those of age- and sex-matched individuals with TD were retrospectively collected between December 2007 and February 2023. Deep ensembles of 5 models were built with 10-fold cross-validation using the pretrained ResNeXt-50 (32×4d) network. Score-weighted visual explanations for convolutional neural networks, with a progressive erasing technique, were used for model visualization and quantitative validation. Data analysis was performed between December 2022 and October 2023.ExposuresAutism Diagnostic Observation Schedule–Second Edition calibrated severity scores (cutoff of 8) and Social Responsiveness Scale–Second Edition T scores (cutoff of 76) were used to assess symptom severity.Main Outcomes and MeasuresThe main outcomes were participant-level area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. The 95% CI was estimated through the bootstrapping method with 1000 resamples.ResultsThis study included 1890 eyes of 958 participants. The ASD and TD groups each included 479 participants (945 eyes), had a mean (SD) age of 7.8 (3.2) years, and comprised mostly boys (392 [81.8%]). For ASD screening, the models had a mean AUROC, sensitivity, and specificity of 1.00 (95% CI, 1.00-1.00) on the test set. These models retained a mean AUROC of 1.00 using only 10% of the image containing the optic disc. For symptom severity screening, the models had a mean AUROC of 0.74 (95% CI, 0.67-0.80), sensitivity of 0.58 (95% CI, 0.49-0.66), and specificity of 0.74 (95% CI, 0.67-0.82) on the test set.Conclusions and RelevanceThese findings suggest that retinal photographs may be a viable objective screening tool for ASD and possibly for symptom severity. Retinal photograph use may speed the ASD screening process, which may help improve accessibility to specialized child psychiatry assessments currently strained by limited resources.

2 Infant Imitation: Detecting Risk in the First Year with PediaTrac™

Objective:Imitation has pervasive associations with social and communicative development. However, few methods have been developed to measure this construct in typically developing infants, and even less is available for at-risk populations, such as infants born preterm. Autism spectrum disorder (ASD), a particular risk of premature birth, is associated with atypical imitation and social communication. Although imitation emerges in infancy, most current screening and diagnostic tools for ASD cannot be utilized prior to 12 months. The present study aimed to develop and validate a caregiver-report measure of infant imitation, characterize imitation profiles at 4, 6, and 9 months in term and preterm infants, and explore the relationship between imitation and scores on an ASD screening questionnaire at 18 months.Participants and Methods:Participants (N = 571) were recruited from a larger multi-site study of PediaTrac™ v3.0, a web-based tool for monitoring and tracking infant development, and were surveyed longitudinally at birth, 2, 4, 6, 9, 12, 15, and 18 months. Participants completed the online PediaTrac™ survey and several reliable and validated questionnaires via pen-and-paper format. For the purposes of this study, only the Ages and Stages Questionnaire (3rd ed.; ASQ-3), Communication and Symbolic Behavior Scales-Developmental Profile (CSBS-DP), Brief Infant Sleep Questionnaire (BISQ), and the Modified Checklist for Autism in Toddlers - Revised with Follow-Up (M-CHAT-R/F) were examined. The following hypotheses were tested: (1) proposed imitation items will represent a unitary latent construct, for which convergent and discriminant validity will be demonstrated, (2) there will be measurement invariance between term status groups at each assessment period, (3) preterm infants will obtain lower caregiver-reported imitation scores compared to term infants, and (4) imitation abilities at the assessment period with the most robust imitation factor will predict M-CHAT-R/F scores at 18 months.Results:Distinct imitation factors at 4, 6, and 9 months were modeled with confirmatory and exploratory factor analyses. Relationships between the factors and established measures of infant communication (CSBS; ASQ) and sleep (BISQ) revealed convergent and discriminant validity, respectively. Strict measurement invariance was demonstrated for the 4- and 9-month factors, and metric invariance for the 6-month measure. Full term infants scored higher on imitation at 9 months, though variance in this outcome was related to term status differences in sensorimotor skills. Lastly, the 9-month imitation factor, coupled with 6-month sensorimotor skills, predicted 18-month ASD risk over and above gestational age.Conclusions:This study provides support for the assessment of infant imitation, utilizing imitation to detect risk in preterm infants, and extending the age of identification for ASD risk into the first year. PediaTrac™ imitation, in combination with the PediaTrac™ sensorimotor domain, may be useful in detecting developmental risk, and specifically risk for ASD, within the first year, leading to earlier initiation of intervention. Further, with its minimal completion time and ease of dissemination through digital platforms, this measure can expand access to care and improve long-term outcomes for children and families.

Psychometric properties of Autism Spectrum Disorders Screening Assessment Tools: Systematic Review.

Due to the prevalence of autism spectrum disorder (ASD), these children must be screened as soon as possible and receive the necessary and appropriate treatment. The purpose of this study was to examine all the ASD screening tools and examine their psychometric properties in available languages. This was a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to review the articles published between 2000 and 2023 and were published in PubMed, EMBASE, ProQuest, and Scopus databases. English keywords were as follows: autism spectrum disorders (ASD), screening/screen, tools, psychometric properties, validity, reliability, translations, pervasive developmental disorders (PDD), and children. The COnsensus -based Standards for the selection of health Measurement Instruments (COSMIN) checklist were used to investigate the psychometric properties of the studies. Among the 476 primary studies, 35 ASD screening tools (132 articles related to the psychometric properties of 35 ASD screening tools in different languages) were identified based on our criteria, and their psychometric properties were examined. Various tools, including performance-based, direct observation, interactive play, and parent and teacher reports tools, were included in the list of tools. Considering that each of these tools has advantages and limitations, they need to be selected and used according to the goals of the researchers and the therapists. Another important point is that many of these tools still need more extensive studies in relation to their psychometric properties.

Screening tools interventions and strategies for early identification and management of autism spectrum disorders

Autism spectrum disorder is a neurodevelopmental disorder marked by social communication difficulties, restricted interests, and repetitive activities. Both genetic and environmental variables impacting the growing brain influence it. Around the world, 1 in 100 children is identified with autism spectrum disorder. Estimates of prevalence have increased over time and varied significantly inside and across sociodemographic groups. Autism spectrum disorder is a prevalent neurodevelopmental condition that significantly shortens life expectancy. Early diagnosis on a community level is a critical public health concern since mounting evidence shows that early diagnosis makes functional gains and has better outcomes. In healthcare settings, autism spectrum disorder screening tools can be utilized to precisely confirm the diagnosis of autism. The American Academy of Pediatrics recommends screening for ASDs between ages 18 and 24 months, although recent research indicates that abnormal behaviours may be visible in certain children even earlier. the checklist for autism in toddlers, the modified checklist for autism in toddlers, the psychological development questionnaire-1, and the autism behaviour checklist are some of the commonly used screening tools in practice for the diagnosis of autism spectrum disorder. The majority of interventions are non-pharmacological and depend on behavioural modification strategies. Pharmacological therapy should only be used to treat target symptoms that are resistant to behavioural treatment. Although there is no cure, prompt and suitable intervention can dramatically raise the quality of life for children. The purpose of this research is to review the screening tools, interventions, and strategies for early identification and management of autism spectrum disorder.

Maternal obesity and diabetes during pregnancy and early autism screening score at well-child visits in standard clinical practice.

Early intervention and treatment can help reduce disability in children diagnosed with autism spectrum disorder. Screening for autism spectrum disorder in young children identifies those at increased likelihood of diagnosis who may need further support. Previous research has reported that exposure to maternal obesity and diabetes during pregnancy is associated with higher likelihood of autism spectrum disorder diagnosis in children. However, little is known about whether these maternal conditions are associated with how very young children score on autism spectrum disorder screening tools. This study examined associations between exposure to maternal obesity and diabetes during pregnancy and offspring scores on the Quantitative Checklist for Autism in Toddlers, an autism spectrum disorder screening questionnaire administered between 18-24 months at well-child visits. A higher score on the Quantitative Checklist for Autism in Toddlers suggests a higher likelihood of autism spectrum disorder; children with scores 3 or greater are referred to developmental pediatricians for evaluation. Our study found that children of mothers with obesity or diabetes during pregnancy had higher scores than children whose mothers did not have these conditions. Associations with maternal obesity and gestational diabetes diagnosed at or before 26 weeks of pregnancy were also present in children who did not have later autism spectrum disorder diagnoses, suggesting that exposure to these conditions during early pregnancy may be associated with a broad range of social and behavioral abilities. Identifying associations between maternal health conditions and early Quantitative Checklist for Autism in Toddlers screening scores could influence future screening and provision of support for children of mothers with these conditions.

Validating the Malaysian modified checklist for autism in toddlers, revised with follow-up (M-CHAT-R/F): a cross-cultural adaptation

IntroductionThe Modified Checklist for Autism in Toddlers, Revised with Follow-Up (M-CHAT-R/F) is a two-stage parent-reported tool for screening autism spectrum disorder (ASD). Early detection of ASD is highly associated with improved social communication and reduced restricted and repetitive behaviors associated with ASD. However, there is limited availability of ASD screening tools in Malaysia and there are no relevant validation studies published. The process of modifying a screening instrument to align with the cultural and linguistic characteristics of the target population is a crucial component in establishing the instrument's validity.MethodsTherefore, this study translates and culturally adapts the M-CHAT-R/F into Malay and verifies its psychometric properties among the Malaysian population. 500 Malaysian toddlers aged between 18 and 48 months were recruited from different settings. The parents of the toddlers were asked to complete the Malaysian M-CHAT-R/F. The reliability of the screening tool was verified using Cronbach's alpha.ResultsBy comparing the screening outcomes of the Malaysian M-CHAT-R/F and clinical evaluation results, the prevalence of ASD was determined as 6.6% in the sample. High values of sensitivity (96.6%) and specificity (93.2%) and a satisfactory positive predictive value (47.5%) supported the validity of the Malaysian M-CHAT-R/F. Furthermore, the receiver operating characteristic analysis yielded three as the optimal cut-off score of the Malaysian M-CHAT-R/F.DiscussionThese results suggest that the Malaysian M-CHAT-R/F is an effective screening tool reliable for use in clinical practice. Further investigation using a representative sample of the whole country is recommended given the high prevalence rate obtained in the current sample.

The influence of loss to follow-up in autism screening research: Taking stock and moving forward.

How best to improve the early detection of autism spectrum disorder (ASD) is the subject of significant controversy. Some argue that universal ASD screeners are highly accurate, whereas others argue that evidence for this claim is insufficient. Relatedly, there is no clear consensus as to the optimal role of screening for making referral decisions for evaluation and treatment. Published screening research can meaningfully inform these questions-but only through careful consideration of children who do not complete diagnostic follow-up. We developed two simulation models that re-analyze the results of a large-scale validation study of the M-CHAT-R/F by Robins etal. (2014, Pediatrics, 133, 37). Model #1 re-analyzes screener accuracy across six scenarios, each reflecting different assumptions regarding loss to follow-up. Model #2 builds on this by closely examining differential attrition at each point of the multi-step detection process. Estimates of sensitivity ranged from 40% to 94% across scenarios, demonstrating that estimates of accuracy depend on assumptions regarding the diagnostic status of children who were lost to follow-up. Across a range of plausible assumptions, data also suggest that children with undiagnosed ASD may be more likely to complete follow-up than children without ASD, highlighting the role of clinicians and caregivers in the detection process. Using simulation modeling as a quantitative method to examine potential bias in screening studies, analyses suggest that ASD screening tools may be less accurate than is often reported. Models also demonstrate the critical importance of every step in a detection process-including steps that determine whether children should complete an additional evaluation. We conclude that parent and clinician decision-making regarding follow-up may contribute more to detection than is widely assumed.

From neurodevelopmental to neurodegenerative disorders: Investigating symptoms of attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) in patients with dementia

Dementia is characterized by a progressive cognitive decline that could be caused by several disorders. Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are two prevalent neurodevelopmental disorders that might overlap with dementia symptoms. Hence, this study aimed to evaluate the ASD and ADHD symptoms in dementia patients referred to a memory clinic in Iran. We recruited 65 dementia patients and instructed them to fill out the autism quotient (AQ) and the Conners’ Adult ADHD Rating Scales (CAARS) questionnaires. Considering the cutoff points of AQ and CAARS questionnaires, 18.5% of participants were at higher risk of ASD, and 35.4% were at higher risk of ADHD. The results indicated that ADHD and ASD symptoms might be common comorbidities in patients with dementia which can increase the disease burden. Specialized ADHD and ASD screening tools in the elderly population with dementia are needed to prevent misdiagnoses due to symptom overlaps.

Meta-analysis of the Modified Checklist for Autism in Toddlers, Revised/Follow-up for Screening.

The Modified Checklist for Autism in Toddlers, Revised with Follow-up (M-CHAT-R/F) is used worldwide to screen for autism spectrum disorder (ASD). To calculate psychometric properties of the M-CHAT-R/F for subsequent diagnosis of ASD. Systematic searches of Medline, Embase, SCOPUS, and Trip Pro databases from January 2014 to November 2021. Studies were included if they (1) used the M-CHAT-R/F (2) applied standard scoring protocol, (3) used a diagnostic assessment for ASD, and (4) reported at least 1 psychometric property of the M-CHAT-R/F. Two independent reviewers completed screening, full-text review, data extraction, and quality assessment, following Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. A random-effects model was used to derive pooled estimates and assess for between-study heterogeneity. Of 667 studies identified, 15 with 18 distinct samples from 10 countries (49 841 children) were used in the meta-analysis. Pooled positive predictive value (PPV), was 57.7% (95% confidence interval [CI] 48.6-66.8, τ2 = 0.031). PPV was higher among high-risk (75.6% [95% CI 66.0-85.2]) than low-risk samples (51.2% [95% CI 43.0-59.5]). Pooled negative predictive value was 72.5% (95% CI 62.5-82.4 τ2 = 0.031), sensitivity was 82.6% (95% CI 76.2-88.9) and specificity 45.7% (95% CI 25.0-66.4). Negative predictive value, sensitivity, and specificity were calculated based on small sample sizes because of limited or no evaluation of screen-negative children. These results support use of the M-CHAT-R/F as a screening tool for ASD. Caregiver counseling regarding likelihood of an ASD diagnosis after positive screen should acknowledge the moderate PPV.

Differential Performance of Social Communication Questionnaire Items in African American/Black vs. White Children.

Screening for autism spectrum disorder (ASD) is an essential early step in the identification process and inaccurate screening may lead to significant delays in the onset of treatment. Past research has highlighted discrepancies in the performance of ASD screening tools such as the Social Communication Questionnaire (SCQ) among certain racial and ethnic groups. The current study explored the functioning of the SCQ among African American/Black and White respondents based on item level performance on the measure. Differential Item Functioning (DIF) analyses showed that 16 (41%) items of the SCQ functioned differently for African American/Black respondents when compared to White respondents. Implications, such as the potential for delayed diagnosis and treatment, and the influence on downstream outcomes, are discussed.

Web-based autism screening using facial images and convolutional neural network

Developmental disabilities such as autism spectrum disorder (ASD) affect a person’s ability to interact socially, and communicate effectively and also cause behavioral issues. Children with ASD cannot be cured but they might benefit from early intervention to enhance their cognitive abilities, favorite their growth , and affect their lives and families in a positive way. Multiple standard ASD screening tools are used such as the autism diagnostic observational schedule (ADOS) and the autism diagnostic interview (ADI), which are known to be lengthy and challenging without specialist training to administrate and score. The process of ASD assessment can be time-consuming and costly, and the growing number of autistic cases worldwide indicates an urgent need for a quick, simple, and dependable self-administered autism screening tool that may be used if a child displays some of the common signs of autism, and to ensure whether or not he should seek professional full ASD diagnosis. According to a number of studies, ASD individuals exhibit facial phenotypes that are distinct from those of normally developing children. Furthermore, convolutional neural networks (CNN) have mostly found utility in image classification applications due to their high classification accuracy. Using facial images, a dense convolutional network (Densenet) model, and cloud-based advantages, in this paper we proposed a practical, fast, and easy-to-use ASD online screening approach. Easily available through the internet via the link “https://asd-detector.herokuapp.com/”, our suggested web-based screening instrument may be a practical and trustworthy tool for practitioners in their ASD diagnostic procedures with a 98 percent testing dataset classification accuracy.

Development and validation of the social-emotional rating scale for autism spectrum disorder (SERA) in an Indian sample

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that occurs during the developmental period and affects overall cognitive, emotional, social, and physical health. Comprehensive screening for ASD can assist in diagnosis and intervention planning. The primary aim of this study was to develop and validate a screening tool for ASD based on the diagnostic guidelines of the International Classification of Diseases (ICD-11). The Social-Emotional Rating Scale for Autism Spectrum Disorder (SERA) was validated with 504 participants, including healthcare professionals who have worked with individuals with ASD and parents/primary caregivers of children with ASD. A confirmatory factor analysis on 78 participants with ASD and 27 participants without ASD indicated a good fit for the data. As an intervention planning tool, the developed SERA appears promising for identifying a person’s social-emotional functioning level in line with autistic characteristics.

Autism Spectrum Disorder and Anxiety Among Transgender Youth: Use of the Social Communication Questionnaire (SCQ).

Autism spectrum disorder (ASD) and ASD symptoms are overrepresented among gender-diverse youth across studies. Gender-diverse and ASD youth are at risk for anxiety, but anxiety is unclear among gender-diverse youth with ASD. The Social Communication Questionnaire (SCQ) is a commonly used ASD screener, including in multidisciplinary gender-affirming programs, but scholars have disagreed on the most optimal cut-off score. To date, no study has investigated the sensitivity and specificity of its established cut-off score of 15 with a sample of transgender youth. Gaining more accurate information about the utility of ASD screening tools with gender diverse youth is critical in order to help refer youth to needed services. Among a sample of 325 transgender youth, this study sought to determine an optimal cut-off score for the SCQ, the prevalence of ASD and ASD symptoms, and the relationship between ASD and anxiety within this population. The current study found that a lower cut-off score of 11 yielded optimal sensitivity and specificity (i.e., the best balance of accurately identifying individuals with ASD based on the screener), and analyses found an overrepresentation of ASD (5.2%) and ASD traits (12.1%). Transgender youth with ASD or ASD symptoms had higher anxiety, and all youth perceived themselves as more anxious than their parents. Future research is needed to identify the needs of this population, with particular consideration for the lived experiences of gender diverse and transgender youth.

Early Detection Assessment Tools in Children With Autism Spectrum Disorder: A Literature Study

Abstract The high prevalence of Autism Spectrum Disorder (ASD) necessitates acquiring early detection tools that can lead to appropriate interventions for children and assist children in performing as many life functions as possible. In addition, early detection tools for ASD can realize quality early childhood development. This study aims to review various types of assessments for early detection of ASD in children using the literature review method. The following articles are from the website https://eric.ed.gov/ as search engine for scientific articles. Using the keywords “autism spectrum disorder assessment tools” and “early detection for autism,” then peer-reviewed only and year filters since 2018, 2,829 articles were found. Then from the identified articles, they are re-elected based on title, abstract, and time of publication to produce 16 articles whose early detection tool was tested on more than 50 participants and began at the earliest possible age. The results show that various screening and diagnostic tools for ASD prioritize areas such as social communication, behavioral problems, emotional problems, sensory regulatory issues, and engagement issues. The instrument’s limitations, which include an expensive price, a lengthy process, the need for expert involvement, and cultural differences, create a growing gap that must be bridged immediately.

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.

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.

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.

The implementation of the screening tool for autism in toddlers in Part C early intervention programs: An 18-month follow-up.

The early detection of autism spectrum disorder can lead to access to autism spectrum disorder-specific services that have been shown to have a large impact on a child's overall development. Although a stable diagnosis of autism spectrum disorder can be made by age 2 years, most children are not diagnosed until much later. To address this issue, this study examined the effectiveness of training Part C Early Intervention providers to use an interactive autism spectrum disorder screening tool, the Screening Tool for Autism in Toddlers. Sixty-nine providers attended a 1-day training workshop on the use of the Screening Tool for Autism in Toddlers. After the workshop, providers reported increased knowledge about recognizing the early signs of autism spectrum disorder, and about 45% of the providers reported using the Screening Tool for Autism in Toddlers with families in their caseloads 18 months after the training. These results suggest that the Screening Tool for Autism in Toddlers is feasible for use within Early Intervention settings. In addition, they suggest that specific providers might serve as a screening "point-person," rather than expecting the Screening Tool for Autism in Toddlers to be used by all providers. Future research should aim to identify specific characteristics of agencies or providers that might be best suited for using the Screening Tool for Autism in Toddlers.

Integrating a New Online Platform in Primary Care for Early Detection, Referral, and Intervention in Autism Spectrum Disorder: The First Italian Pivotal Project.

Autism spectrum disorder (ASD) is a heterogeneous condition characterized by deficits in social communication and a repetitive pattern of behavior, with recent Italian prevalence estimates of 1 in 77. Although the core behavioral features of ASD appear to emerge within the first two years of life, clinical diagnosis is often not received before the third birthday. The American Academy of Pediatrics (AAP) has recommended that primary care physicians routinely screen for ASD at the 18- and 24-month visits. In Italy, the Guidelines of the Italian National Institute for Health (ISS) recommend the Checklist for Autism in Toddlers (CHAT) as a screening tool for ASD, which compares parent responses with a semistructured observation by a healthcare provider. In the Italian National Health System, pediatricians have regular visits with all children; however, there is wide variability in following screening guidelines, and some barriers have been detected. For these reasons, other studies have highlighted the advantages of using telemedicine with the potential for enhancing ASD screening practices. The current study is an examination of the implementation of the first Italian online web-based screening tool (Web Italian Network for Autism Spectrum DisorderWIN4ASD), an innovative web app for pediatricians. We present the data obtained from the screening activity through the platform by a small group of pediatricians. The results of this study show that the implemented web-based platform appears to be an effective, efficient, and sustainable way to integrate screening services into primary care.