Autism Spectrum Disorder In Young Children Research Articles

Screening and Prediction of Autism in Toddlers Using SORF in Videos of Brief Family Interactions.

The purpose of this study was to validate the utility of the Systematic Observation of Red Flags (SORF) for autism screening during 10-minute parent-child interactions at ages 15-24 months. A total of 54 children participated in this study, including 19 with autism spectrum disorder (ASD), 23 with developmental delay, and 12 typically developing children. Coders coded 10-minute videos of parent-child interactions based on the defined scoring criteria. The discriminative ability for outcome diagnosis was evaluated for total score, social communication score, restricted repetitive behavior score, number of red flags, and composite score. SORF scores demonstrated good discriminative ability between ASD and non-ASD children, with the composite score (AUC = 0.884) showing the best discriminative ability for outcome diagnosis and predicting likelihood of ASD in young children. The composite score represented a simplified measurement, with the cutoff score of 7 and sensitivity and specificity of 0.789 and 0.800, respectively.

Associations of neighborhood greenspace, and active living environments with autism spectrum disorders: A matched case-control study in Ontario, Canada

BackgroundIncreasing evidence links early life residential exposure to natural urban environmental attributes and positive health outcomes in children. However, few studies have focused on their protective effects on the risk of autism spectrum disorder (ASD). The aim of this study was to investigate the associations of neighborhood greenspace, and active living environments during pregnancy with ASD in young children (≤6 years). MethodsWe conducted a population-based matched case-control study of singleton term births in Ontario, Canada for 2012–2016. The ASD and environmental data was generated using the Ontario Autism Spectrum Profile, the Better Outcomes Registry & Network Ontario, and Canadian Urban Environmental Health Research Consortium. We employed conditional logistic regressions to estimate the odds ratio (OR) between ASD and environmental factors characterizing selected greenspace metrics and neighborhoods conducive to active living (i.e., green view index (GVI), normalized difference vegetation index (NDVI), tree canopy, park proximity and active living environments index (ALE)). ResultsWe linked 8643 mother-child pairs, including 1554 cases (18%). NDVI (OR 1.034, 0.944–1.024, per Inter Quartile Range [IQR] = 0.08), GVI (OR 1.025, 95% CI 0.953–1.087, per IQR = 9.45%), tree canopy (OR 0.992, 95% CI 0.903–1.089, per IQR = 6.24%) and the different categories of ALE were not associated with ASD in adjusted models for air pollution. In contrast, living closer to a park was protective (OR 0.888, 0.833–0.948, per 0.06 increase in park proximity index), when adjusted for air pollution. ConclusionsThis study reported mixed findings showing both null and beneficial effects of green spaces and active living environments on ASD. Further investigations are warranted to elucidate the role of exposure to greenspaces and active living environments on the development of ASD.

Measuring Developmental Delays: Comparison of Parent Report and Direct Testing.

Developmental assessment is part of a comprehensive autism evaluation. During in-person evaluations, developmental assessment is completed via direct testing by an examiner. In telehealth evaluations, developmental assessment relies on caregiver-report instruments. This study examined correspondence between caregiver report and direct testing of developmental skills. Participants were 93 children, aged 18-42 months, undergoing evaluation for possible autism spectrum disorder (ASD). Caregivers were interviewed with the Developmental Profile, 4th edition (DP-4) via telehealth platform and children were tested in person 2-4 weeks later using the Mullen Scales of Early Learning (MSEL). Correlations between the DP-4 and MSEL were high (ranging from 0.50 to 0.82) across standard scores, age equivalents, and functional categories, as well as across individual subtests and overall composite scores. The high convergent validity found in this study suggests that the DP-4 provides a suitable proxy for direct developmental testing using the MSEL in the context of telehealth evaluations for ASD in young children, delivering a good estimate of both developmental functioning and presence of delays. Data were obtained from registered clinical trial NCT05047224, date of registration 2021-09-07.

Screening for Autism Spectrum Disorder in Young Children: Still Not Enough Evidence.

Early detection of autism spectrum disorder (ASD) has the potential to significantly reduce the impact of the condition, however previous reviews have found little evidence to support screening programs for ASD in young children. We conducted a review with the aim of updating evidence on 3 aspects: (a) diagnostic stability of ASD in young children; (b) accuracy of ASD screening tools in young children; and (c) the benefits of early interventions in screen-detected young children with ASD. A total of 33 studies were included in our review. Five studies looking at diagnostic stability reported estimates ranging from 71.9% to 100%, however the majority only included a follow-up of 24 months and all studies raised concerns regarding the risk of bias due particularly to lack of blinding, sample size, and patient flow. A total of 25 studies, reported in 26 articles, were identified that reported accuracy data on 11 screening tools. Most of the reports were concerned with versions of M-CHAT, reporting sensitivity estimates from 0.67 to 1.0; however, many of these were deemed to be of high risk of bias due to lack of blinding and follow-up. Four studies reported on early interventions in screen-detected children; however, the majority did not find significant improvements on the relevant outcomes. Overall, the evidence on screening for ASD in young children captured by this review is not conclusive regarding the 3 aspects of screening in this population. Future studies should attempt to ensure blinded diagnostic assessments, include longer follow-up periods and limit attrition.

Прогнозування ризику розвитку розладів аутистичного спектра в дітей з епілептичними енцефалопатіями

Predicting the risk of developing autism spectrum disorders (ASD) in children with epileptic encephalopathies (EE) is an important task, which makes it possible to identify the most significant risk factors and develop ways of their modification. Purpose - to develop a model for predicting the risk of developing ASD in young children with EE. Materials and methods. 75 children aged 0-3 years with the onset of epileptic seizures in the first year of life, clinical manifestations of EE were examined. The most informative clinical, neurophysiological, and neuroimaging indicators for predicting the risk of clinical manifestations of ASD were determined: age of onset of epileptic seizures, index of spike-wave activity during NREM sleep, frequency and amplitude of alpha rhythm, frequency and amplitude of epileptiform activity, fractional anisotropy and mean diffusion coefficient in the Broca’s center, fractional anisotropy in the Wernicke center and knee of the corpus callosum. The multiple linear regression method was used to predict the risk of developing ASD symptoms. Results. A model for predicting the risk of developing clinical manifestations of ASD in children with EE has been developed. It was established that the risk of developing clinical manifestations of ASD increases with a decrease in the age of onset of epileptic seizures, decrease in the frequency and amplitude of the alpha rhythm according to electroencephalography (EEG) data, increase of the index of spike-wave activity, frequency and amplitude of epileptiform activity, increase in the index of fractional anisotropy in the anterior part of the arcuate tract, decrease in the average diffusion coefficient in the area of the Broca’s center, fractional anisotropy in the Wernicke center and the knee of the corpus callosum according to magnetic resonance tractography. Conclusions. A regression model for predicting the risk of clinical manifestations of ASD in children with EE was developed with an average error of approximation of 13.2% and a coefficient of determination of 0.74, which is recommended for use in clinical practice in order to form groups of children at high risk of ASD. Such children require further dynamic monitoring and early intervention by specialists of a multidisciplinary team for timely identification and correction of ASD symptoms The research was carried out in accordance with the principles of the Helsinki Declaration. The study protocol was approved by the Local Ethics Committee of the participating institution. The informed consent of the patient was obtained for conducting the studies. No conflict of interests was declared by the author.

Early detection of autism spectrum disorder in young children with machine learning using medical claims data

ObjectivesEarly diagnosis and intervention are keys for improving long-term outcomes of children with autism spectrum disorder (ASD). However, existing screening tools have shown insufficient accuracy. Our objective is to predict...

Development of a visual attention based decision support system for autism spectrum disorder screening

Visual attention of young children with autism spectrum disorder (ASD) has been well documented in the literature for the past 20 years. In this study, we developed a Decision Support System (DSS) that uses machine learning (ML) techniques to identify young children with ASD from typically developing (TD) children. Study participants included 26 to 36 months old young children with ASD (n = 61) and TD children (n = 72). The results showed that the proposed DSS achieved up to 87.5% success rate in the early assessment of ASD in young children. Findings suggested that visual attention is a unique, promising biomarker for early assessment of ASD. Study results were discussed, and suggestions for future research were provided.

Information and Communication Technologies to Support Early Screening of Autism Spectrum Disorder: A Systematic Review.

The aim of this systematic review is to identify recent digital technologies used to detect early signs of autism spectrum disorder (ASD) in preschool children (i.e., up to six years of age). A systematic literature search was performed for English language articles and conference papers indexed in Pubmed, PsycInfo, ERIC, CINAHL, WoS, IEEE, and ACM digital libraries up until January 2020. A follow-up search was conducted to cover the literature published until December 2020 for the usefulness and interest in this area of research during the Covid-19 emergency. In total, 2427 articles were initially retrieved from databases search. Additional 481 articles were retrieved from follow-up search. Finally, 28 articles met the inclusion criteria and were included in the review. The studies included involved four main interface modalities: Natural User Interface (e.g., eye trackers), PC or mobile, Wearable, and Robotics. Most of the papers included (n = 20) involved the use of Level 1 screening tools. Notwithstanding the variability of the solutions identified, psychometric information points to considering available technologies as promising supports in clinical practice to detect early sign of ASD in young children. Further research is needed to understand the acceptability and increase use rates of technology-based screenings in clinical settings.

Autism Spectrum Disorder: Investigating Predictive Adaptive Behavior Skill Deficits in Young Children.

Autism spectrum disorder (ASD) is a lifelong neurodevelopmental disorder that consists of difficulties with social communication and language, as well as the presence of restricted and repetitive behaviors. These deficits tend to present in early childhood and usually lead to impairments in functioning across various settings. Moreover, these deficits have been shown to negatively impact adaptive behavior and functioning. Thus, early diagnosis and intervention is vital for future success within this population. The purpose of this study was to further examine the subscales that comprise the adaptive behavior section of the Bayley®-III to determine which of the ten subscales are predictive of ASD in young children (i.e., ≤ three years of age). A retrospective file review of 273 children participating in Kentucky's early intervention program, First Steps, was completed. The children ranged in age from 18 to 35 months. A binary logistic regression was used to assess the subscales that comprise the adaptive behavior of the section of the Bayley®-III to determine which of the ten subscales are predictive of ASD in young children (i.e., ≤ three years of age). The results indicated that individual lower raw scores in communication, community use, functional preacademics, home living, health and safety, leisure, self-care, self-direction, and social subscales were predictive of an autism diagnosis.

Toward Novel Tools for Autism Identification: Fusing Computational and Clinical Expertise.

Barriers to identifying autism spectrum disorder (ASD) in young children in a timely manner have led to calls for novel screening and assessment strategies. Combining computational methods with clinical expertise presents an opportunity for identifying patterns within large clinical datasets that can inform new assessment paradigms. The present study describes an analytic approach used to identify key features predictive of ASD in young children, drawn from large amounts of data from comprehensive diagnostic evaluations. A team of expert clinicians used these predictive features to design a set of assessment activities allowing for observation of these core behaviors. The resulting brief assessment underlies several novel approaches to the identification of ASD that are the focus of ongoing research.

Use of the TELE-ASD-PEDS for Autism Evaluations in Response to COVID-19: Preliminary Outcomes and Clinician Acceptability.

The COVID-19 pandemic has caused unprecedented disruptions to healthcare, including direct impacts on service delivery related to autism spectrum disorder (ASD). Caregiver-mediated tele-assessment offers an opportunity to continue services while adhering to social distancing guidelines. The present study describes a model of tele-assessment for ASD in young children, implemented in direct response to disruptions in care caused by the COVID-19 pandemic. We present preliminary data on the outcomes and provider perceptions of tele-assessments, together with several lessons learned during the period of initial implementation.

Symptom improvement in children with autism spectrum disorder following bumetanide administration is associated with decreased GABA/glutamate ratios

Bumetanide has been reported to alter synaptic excitation–inhibition (E-I) balance by potentiating the action of γ-aminobutyric acid (GABA), thereby attenuating the severity of autism spectrum disorder (ASD) in animal models. However, clinical evidence of its efficacy in young patients with ASD is limited. This was investigated in the present clinical trial of 83 patients, randomised to the bumetanide group (bumetanide treatment, 0.5 mg twice daily) or the control group (no bumetanide treatment). Primary [Children Autism Rating Scale (CARS)], secondary [Clinical Global Impressions (CGI)], and exploratory [inhibitory (γ-aminobutyric acid, GABA) and excitatory (glutamate, Glx) neurotransmitter concentrations measured in the insular cortex (IC) and visual cortex (VC) by magnetic resonance spectroscopy (MRS)] outcome measures were evaluated at baseline and at the 3-month follow-up. Side effects were monitored throughout the treatment course. Compared with the control group, the bumetanide group showed significant reduction in symptom severity, as indicated by both total CARS score and number of items assigned a score ≥ 3. The improvement in clinical symptoms was confirmed by CGI. GABA/Glx ratio in both the IC and VC decreased more rapidly over the 3-month period in the bumetanide group than that in the control group. This decrease in the IC was associated with the symptom improvement in the bumetanide group. Our study confirmed the clinical efficacy of bumetanide on alleviating the core symptoms of ASD in young children and it is the first demonstration that the improvement is associated with reduction in GABA/Glx ratios. This study suggests that the GABA/Glx ratio measured by MRS may provide a neuroimaging biomarker for assessing treatment efficacy for bumetanide.

Predicting Autism Spectrum Disorder in Young Children: The Predictive Utility of the Ghuman-Folstein Screen for Social Interaction and Sociodemographic Factors

Background: Autism Spectrum Disorder (ASD) is characterized by having deficits in social interactions. Screening measures for ASD are often used for children over five years of age, ultimately leading to diagnosis later in development. Identification of ASD in young children is critical for early intervention. Aims: The purpose of the present study was to examine whether prediction of ASD could be improved in young children by combining social interaction scores, as measured by the Ghuman-Folstein Screen for Social Interaction (GF-SSI), with the presence of selected demographic variables (sex, age, ethnicity, mother’s educational level, and socioeconomic status). Methods and Procedures: One-hundred and seventy-one clinically referred children with previously diagnosed ASD or non-ASD developmental disorders and their caregivers were included in the study. Caregivers completed a sociodemographic survey and the GF-SSI. Outcomes and Results: Results demonstrated that the final model correctly identified 74% of the cases, and the GF-SSI was found to be the greatest predictor of children having ASD. The selected demographic variables were not found to be significant predictors of the diagnosis of ASD. Conclusions and Implications: These results are discussed in relation to the literature on predicting ASD in young children. Future directions for research are also discussed.

Physician View and Experience of the Diagnosis of Autism Spectrum Disorder in Young Children.

Introduction: Clinicians working with children with autism spectrum disorder (ASD) occupy an important position between parents and the wide-ranging research findings. However, it is not widely known how clinicians view and experience ASD in children, even though their perspective has been shown to significantly influence their work. Material and methods: Sixteen physicians working with preschool children without a diagnosis of (intellectual or other) disability with a (presumed) diagnosis of ASD participated in a semistructured interview. They described their professional views on ASD, and how they experienced its use in their clinical practice. The data were analyzed by applying the qualitative research method of interpretative phenomenological analysis of the data through Nvivo 11. Results: The main topic of the interviewed physicians’ views and experiences of ASD in a young child comprised three inductively established themes: 1) physicians’ views on ASD are multifaceted but fit within their personal clinical styles; 2) the ASD diagnosis is a “descriptive” part of a clinical trajectory; and 3) ASD treatment is a mix of “standard” approaches and a personalized search. These physicians’ perspectives on ASD are composed of multiple and sometimes ambiguous facets. However, their views are embedded in their personal clinical styles in general (i.e., beyond ASD) and are experienced as clinically “workable.” With the aim of finding an adequate approach to the problems parents bring to their office, many interviewed physicians say that—complementary to or rather than a classificatory diagnosis—they prefer using a personalized “profile” of a child in a therapeutic “process.” Conclusions: The interviewed physicians consider doubts and concerns to be an inherent part of their clinical work with ASD in young children, but do not experience this ambiguity as an obstacle to clinical care. These physicians deal with the multiplicity of their views on ASD by basing their eclectic views on their generally adopted clinical styles, and by selecting what works for them, and for the parents and child, from what they regard as the ‘textbook knowledge’ on ASD. We discuss the implications of these findings for translating research results to the clinic.

Diagnosis of Autism Spectrum Disorders in Young Children Based on Resting-State Functional Magnetic Resonance Imaging Data Using Convolutional Neural Networks

Statistics show that the risk of autism spectrum disorder (ASD) is increasing in the world. Early diagnosis is most important factor in treatment of ASD. Thus far, the childhood diagnosis of ASD has been done based on clinical interviews and behavioral observations. There is a significant need to reduce the use of traditional diagnostic techniques and to diagnose this disorder in the right time and before the manifestation of behavioral symptoms. The purpose of this study is to present the intelligent model to diagnose ASD in young children based on resting-state functional magnetic resonance imaging (rs-fMRI) data using convolutional neural networks (CNNs). CNNs, which are by far one of the most powerful deep learning algorithms, are mainly trained using datasets with large numbers of samples. However, obtaining comprehensive datasets such as ImageNet and achieving acceptable results in medical imaging domain have become challenges. In order to overcome these two challenges, the two methods of "combining classifiers," both dynamic (mixture of experts) and static (simple ‌Bayes) approaches, and "transfer learning" were used in this analysis. In addition, since diagnosis of ASD will be much more effective at an early age, samples ranging in age from 5 to 10years from global Autism Brain Imaging Data Exchange I and II (ABIDE I and ABIDE II) datasets were used in this research. The accuracy, sensitivity, and specificity of presented model outperform the results of previous studies conducted on ABIDE I dataset (the best results obtained from Adamax optimization technique: accuracy = 0.7273, sensitivity = 0.712, specificity = 0.7348). Furthermore, acceptable classification results were obtained from ABIDE II dataset (the best results obtained from Adamax optimization technique: accuracy = 0.7, sensitivity = 0.582, specificity = 0.804) and the combination of ABIDE I and ABIDE II datasets (the best results obtained from Adam optimization technique: accuracy = 0.7045, sensitivity = 0.679, specificity = 0.7421). We can conclude that the proposed architecture can be considered as an efficient tool for diagnosis of ASD in young children. From another perspective, this proposed method can be applied to analyzing rs-fMRI data related to brain dysfunctions.

Autism spectrum disorder symptoms from ages 2 to 19 years: Implications for diagnosing adolescents and young adults.

This study explored change in social-communicative symptoms in 140 individuals with childhood autism spectrum disorder (ASD) diagnoses. Trajectories of caregiver-reported social-communicative symptoms were examined for three groups (verbal, delayed speech, minimally verbal) from ages 2 to 19 years. Groups showed comparable levels of social-communicative impairment at 2 years and significant decreases in overall symptom levels across the 17-year period (P < .001). Across three subdomains, main effects of time and language (P < .001) reflected patterns of overall improvement, although children with more impaired language tended to have more caregiver-reported symptoms relative to verbal peers. A significant time-by-language interaction (P < .001) reflected that trajectories of socioemotional reciprocity symptoms differed according to patterns of language development. In contrast, improvements in the nonverbal communication domain were seen across language groups, whereas deficits in the development and maintenance of relationships improved for only verbal children. Verbal adults showed significant reductions in the prevalence of kseveral symptoms exhibited during childhood. Improvements suggest that symptoms indicative of ASD in young children may no longer be diagnostic markers in adolescents and adults. Relative stability of several items suggests that impaired facial expression may be a core ASD symptom that warrants more systematic study across the lifespan. Research investigating the manifestation of ASD in older individuals is needed to foster development of appropriate assessment tools and interventions. Differential relationships to developmental factors within the broader social-communication domain underscores a need to focus on more narrowly defined symptom constructs when exploring links between pathophysiology and observable phenotypes. Autism Research 2019, 12: 89-99. © 2018 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: In a sample of 140 participants with autism spectrum disorder (ASD) followed from 2 to 19 years old, this study found that overall social-communicative symptoms improve across childhood and adolescence. However, timing and amount of change varied for different symptom categories and participants with different language abilities. Findings suggest that some older adolescents and adults with ASD may not exhibit the same difficulties observed in young children with ASD. More research is needed to better understand the strengths and needs of young adults with ASD.

Symptom profiles of autism spectrum disorder in tuberous sclerosis complex.

To determine the extent to which deficits associated with autism spectrum disorder (ASD) in toddlers with tuberous sclerosis complex (TSC) overlap with those in toddlers with nonsyndromic ASD (nsASD) and to examine cognitive function and epilepsy severity in toddlers with TSC and comorbid ASD. This is the endpoint analysis from a longitudinal investigation of ASD risk factors in children with TSC. Measures included the Autism Diagnostic Observation Schedule (ADOS), the Mullen Scales of Early Learning, and clinical epilepsy variables. A repeated-measures analysis of variance was performed with between-subjects factor of group (typically developing, TSC/no ASD, TSC/ASD, nsASD) and within-subjects factors of individual ADOS item scores in the social communication and repetitive behavior/restricted interest domains. Within the TSC group, comparisons of epilepsy characteristics and cognitive domains were performed using independent-samples t tests. Children with TSC/ASD demonstrated a profile of social communication impairment that had complete convergence with nsASD. Measured social communication impairments included gestures, pointing, eye contact, responsive social smile, and shared enjoyment. This convergence was observed despite the high comorbidity between ASD and cognitive impairment in TSC. This study supports the clinical diagnosis of ASD in young children with TSC and demonstrates remarkable convergence of autism symptoms between TSC/ASD and nsASD. Our results strongly suggest the need for early intervention in toddlers with TSC, with treatment strategies targeting social communication function as well as broader developmental domains, before the onset of autism symptoms.

Timing of the Diagnosis of Attention-Deficit/Hyperactivity Disorder and Autism Spectrum Disorder.

Symptoms of inattention, hyperactivity, and impulsivity are core features of attention-deficit/hyperactivity disorder (ADHD). However, children with autism spectrum disorder (ASD) often present with similar symptoms and may receive a diagnosis of ADHD first. We investigated the relationship between the timing of ADHD diagnosis in children with ASD and the age at ASD diagnosis. Data were drawn from the 2011-2012 National Survey of Children's Health, which asked parents to provide the age(s) at which their child received a diagnosis of ADHD and/or ASD. Using weighted prevalence estimates, we examined the association between a previous diagnosis of ADHD and the age at ASD diagnosis, while controlling for factors known to influence the timing of ASD diagnosis. Our study consisted of 1496 children with a current diagnosis of ASD as reported by parents of children ages 2 to 17 years. Approximately 20% of these children had initially been diagnosed with ADHD. Children diagnosed with ADHD before ASD were diagnosed with ASD ∼3 years (95% confidence interval 2.3-3.5) after children in whom ADHD was diagnosed at the same time or after ASD. The children with ADHD diagnosed first were nearly 30 times more likely to receive their ASD diagnosis after age 6 (95% confidence interval 11.2-77.8). The delay in ASD diagnosis was consistent across childhood and independent of ASD severity. To avoid potential delays in ASD diagnosis, clinicians should consider ASD in young children presenting with ADHD symptoms.

Early Predictors of ASD in Young Children Using a Nationally Representative Data Set

Current clinical diagnosis of Autism Spectrum Disorders (ASD) occurs between 3 and 4 years of age, but increasing evidence indicates that intervention begun earlier may improve outcomes. Using secondary analysis of the Early Childhood Longitudinal Study–Birth Cohort data set, the current study identifies early predictors prior to the diagnosis of ASD at 4 years for approximately 100 children. Children with ASD were compared with children with other disabilities and children who were typically developing. Multinomial logistic regression analyses identified limited unique characteristics (e.g., self-regulation and sleep patterns) at the 9-month time point. A majority of the differences in communication and language, mental/cognitive function, motor function, social interaction, and self-regulation were found at the 2-year time point. Implications for research and practice are presented.

Developing a schedule to identify social communication difficulties and autism spectrum disorder in young children with visual impairment

Available observational tools used in the identification of social communication difficulties and diagnosis of autism spectrum disorder (ASD) rely partly on visual behaviours and therefore may not be valid in children with visual impairment. A pilot observational instrument, the Visual Impairment and Social Communication Schedule (VISS), was developed to aid in identifying social communication difficulties and ASD in young children with visual impairment affected by congenital disorders of the peripheral visual system (disorders of the globe, retina, and anterior optic nerve). The VISS was administered to 23 consecutive children (age range 1 y 9 mo-6 y 11 mo, mean 4 y 1 mo [SD 1.6]; 12 males, 11 females) with visual impairment (nine with severe and 14 with profound visual impairment). Item analysis was carried out by fit of the items to the Rasch model. Validity of the VISS was explored by comparison with the Childhood Autism Rating Scale (CARS) score, and the clinical ASD diagnosis (n=9). Correlation between the VISS and CARS total scores was highly significant (Spearman's rho=-0.89; p=0.01). Below threshold rating on the VISS (score of 35) showed good agreement with the clinical ASD diagnosis (sensitivity 89%, specificity 100%). This preliminary study shows the VISS to be a promising schedule to aid the identification of ASD in young children with visual impairment.