Autism Spectrum Disorder Screening Research Articles

Investigating the Presence of Autistic Traits and Prevalence of Autism Spectrum Disorder Symptoms in Anorexia Nervosa: A Systematic Review and Meta-Analysis.

The present meta-analysis aims to assess whether individuals with anorexia nervosa (AN) demonstrate elevated autistic traits, to explore potential associations between autistic traits and eating disorder symptoms; as well as to estimate the prevalence of a positive screen for autism spectrum disorder (ASD) assessed via Autism Diagnostic Observation Schedule (ADOS), in AN. A systematic literature search was conducted in PsycINFO, MEDLINE, and Web of Science in August 2023 and later updated in April 2024 to identify relevant studies. Twenty-two studies with 1172 AN patients and 2747 healthy controls (HCs) met the inclusion criteria. There was a significant difference between AN and HC groups in autistic traits (g = 0.88, CI = 0.65-1.12), and a significant but modest correlation was found between autistic traits and severity of eating disorder symptoms (r = 0.28, CI = 0.11-0.44). Proportion meta-analysis indicated that 29% (CI = 0.19-0.38) of children and adults scored above the cut-off for ASD. The current findings suggest that AN frequently overlaps with both autistic traits and autistic symptomatology. Therefore, it is essential to evaluate autism and autistic traits in individuals with AN to tailor individualized treatment plans.

Validating the Child Behavior Checklist 1.5-5 as a screening tool for autism spectrum disorder.

Early identification of Autism Spectrum Disorder is very important, especially in low and middle-income countries, where access to resources is often limited. The Child Behavior Checklist 1.5-5 is a tool that has been used to help identify children with autism spectrum disorder through specific behavior patterns. However, its effectiveness in low- and middle-income country settings has not been thoroughly studied. This research focused on evaluating the Child Behavior Checklist 1.5-5 as a screening tool for autism spectrum disorder among Brazilian children. The study involved 1292 children aged 3-5 years from the general population and 70 children with autism spectrum disorder aged 1-5 years. Using advanced statistical methods, the study tested how well the Child Behavior Checklist identified children with autism spectrum disorder and how reliable it was in this context. The findings showed that the Child Behavior Checklist 1.5-5 performed well in identifying autism spectrum disorder, with high reliability and consistency in the results. Although one item in each of the autism spectrum problems and withdrawn syndrome subscales did not perform as strongly, the overall tool was effective. In summary, the Child Behavior Checklist 1.5-5 proves to be a reliable and valid tool for early autism spectrum disorder screening in Brazilian children. This can help ensure that more children in low- and middle-income country settings are identified early and receive the necessary support and interventions to help them thrive. Future research should continue to test this tool in different contexts to confirm its usefulness across various populations.

Predicting language, cognition, and social skills in individuals with Autism spectrum disorder using the Arabic validation of Gilliam Autism Rating scale-Third Edition

The Gilliam Autism Rating Scale-Third Edition (GARS-3) has emerged as a pivotal instrument in the diagnostic assessment of Autism Spectrum Disorder (ASD), with its utility extending across linguistic and cultural boundaries. This study endeavors to adapt and validate the GARS-3 for the Arabic-speaking population, addressing the paucity of culturally congruent diagnostic tools in the Arab world. The primary objective is to validate the Arabic version of GARS-3 (A-GARS-3) to assess children with and without ASD, ensuring the scale’s reliability and validity within the target population. A methodical cross-sectional study design was employed, encompassing a sample of 178 participants from both educational and clinical settings in Saudi Arabia. The process involved direct translation, expert panel review, back-translation, and pilot testing to ensure semantic equivalence and cultural relevance. Data were collected using the A-GARS-3, and analyses included Exploratory and Confirmatory Factor Analysis (EFA and CFA), internal consistency reliability measures, and a One-Way Analysis of Variance (ANOVA) to establish concurrent validity. The A-GARS-3 demonstrated high internal consistency (Cronbach’s alpha = 0.971; McDonald’s omega = 0.972) and strong construct validity, with factor loadings and model fit indices substantiating the factor structure. Predictive validity was confirmed through significant correlations between the Autism Index and the subscales, particularly in the domains of social interaction and communication. The A-GARS-3 is a psychometrically sound instrument that offers reliable assessment for ASD within the Arab cultural context. The positive validation outcomes indicate that the scale is an effective, culturally adapted tool for the localized diagnosis of ASD. The validated scale has significant implications for enhancing ASD screening and diagnostic practices in Arabic-speaking regions, potentially improving early diagnosis and intervention strategies.

Psychometric Properties of the Chinese Version of the Gilliam Autism Rating Scale-Third Edition (CV-GARS-3).

Gilliam Autism Rating Scale-Third Edition (GARS-3) serves as an effective screening tool for autism spectrum disorder (ASD) that is based on the latest and authoritative diagnostic criteria, however, there is a deficiency in adaptive research in China. We aimed to revise the Chinese version of GARS-3 (CV-GARS-3) and evaluate its psychometric characteristics, providing a theoretical basis for the improvement of ASD screening tools in China. This study developed CV-GARS-3 through translation and cultural adaptation of GARS-3. 362 ASD individuals, 126 typical development individuals, and 103 individuals with other disorders were recruited to analyze the psychometric characteristics of CV-GARS-3. The results showed that exploratory structural equation model demonstrated satisfactory goodness-of-fit. Within the non-verbal ASD samples, all items loaded on anticipated factors. Regarding verbal ASD samples, 3 items exhibited considerable cross-loadings and were categorized under unexpected factors. Meanwhile, acceptable criterion validity was reflected in the four subscales (r = 0.71) and the six subscales (r = 0.74). Satisfactory reliability was observed in the four subscales (Cronbach's α = 0.96, inter-rater consistency = 0.86, test-retest consistency = 0.87) and the six subscales (Cronbach's α = 0.94, inter-rater consistency = 0.81, test-retest consistency = 0.81). In addition, receiver operating characteristic analysis demonstrated that both the four subscales (sensitivity = 89%, specificity = 85%, accuracy = 88%) and the six subscales (sensitivity = 86%, specificity = 88%, accuracy = 86%) had outstanding screening effects. Therefore, the results suggested that the CV-GARS-3 is considered as a useful tool for the screening and auxiliary diagnosis of ASD. Notably, the expression of scale should be further improved to adapt the context of Chinese culture and achieve more precise diagnostic results.

Evaluating ASD in children through automatic analysis of paintings

Autism spectrum disorder (ASD) is a hereditary neurodevelopmental disorder affecting individuals, families, and societies worldwide. Screening for ASD relies on specialized medical resources, and current machine learning-based screening methods depend on expensive professional devices and algorithms. Therefore, there is a critical need to develop accessible and easily implementable methods for ASD assessment. In this study, we are committed to finding such an ASD screening and rehabilitation assessment solution based on children’s drawings. From an ASD painting database, 375 drawings from children with ASD and 160 drawings from typically developing children were selected, and a series of image signal processing algorithms based on typical characteristics of children with ASD were designed to extract features from images. The effectiveness of extracted features was evaluated through statistical methods, and they were then classified using a support vector machine (SVM) and XGBoost (eXtreme Gradient Boosting). In 5-fold cross-validation, the SVM achieved a recall of 94.93%, a precision of 86.40%, an accuracy of 85.98%, and an AUC of 90.90%, while the XGBoost achieved a recall of 96.27%, a precision of 93.78%, an accuracy of 92.90%, and an AUC of 98.00%. This efficacy persists at a high level even during additional validation on a set of newly collected paintings. Not only did the performance surpass that of participated human experts, but the high recall rate, as well as its affordability, manageability, and ease of implementation, indicates potentiality in wide screening and rehabilitation assessment. All analysis code is public at GitHub: dishangti/ASD-Painting-Pub.

Data Mining Technique for Diagnosing Autism Spectrum Disorder

Early detection of autistic symptoms can help lower overall medical expenses, which is beneficial given that autism is a developmental disease that is associated with high medical costs. To assess whether or not a kid may have autism spectrum disorder (ASD), screening for ASD involves asking the child's parents, caregivers, and other members of the child's immediate family a series of questions. The current methods for screening for autism, such as the autistic quotient (AQ), might require a significant number of questions in addition to careful question design, which can make an autism examination more time-consuming. The effectiveness and reliability of the test could be improved, for example, by employing data mining strategies. It could be possible to create a system that can foretell ASD at an early stage and give patients, caregivers, and medical professionals dependable and precise findings on the probable need for expert diagnostic services. This research aims to develop a reliable model for estimating the likelihood of an individual being diagnosed with autism spectrum disorder between the ages of 4 and 17. To identify varying degrees of autism, one such model was constructed by utilizing the stochastic gradient descent (SGD) algorithm. Mining data is typically understood to be a decision-making process that enables more effective utilization of available resources in terms of overall performance. The results showed that the suggested prediction model, which used the stochastic gradient descent (SGD) algorithm, could find ASD with an average error of 0.03% and an accuracy of up to 94.5%.

Suicide risk with ASD traits or ADHD traits in non-clinical young adults

The impact of attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) traits on suicidal thoughts and behaviors (STBs) remains inadequately explored. Hence, the present study aims to delve deeper into these intricate issues, contributing to a better understanding of these complex challenges among Chinese youth. The data of 90,882 participants was collected from 63 Chinese universities and colleges in Jilin, China. We aim to explore the risks of STBs on ASD, ADHD, and co-occurrence of ASD + ADHD by using the Autism Spectrum Quotient-10 items (AQ-10) and Adult ADHD Self Report Scale (ASRS). Propensity score matching (PSM) was implemented to balance demographical variables across the four groups. Binary logistic regression was then conducted to further analyze and compare suicide risk across the four groups, also accounting for sex differences. Suicide risks were significantly higher in the ADHD (OR = 1.24, p < 0.001) and ASD + ADHD(OR = 1.49, p < 0.001) traits group. However, there was no significant difference in suicide risk between the ASD traits group (OR = 1.00, p = 1.00) and the control group. The risk of suicide was significantly higher for females than for males across all four groups (p < 0.001). These results indicate the importance of screening for ASD + ADHD traits in young adults, especially females due to its higher risk of STBs. Further studies are warranted to delve deeper into this issue, emphasizing the necessity of formal diagnostic tests and directing focused attention toward addressing these complexities.

A machine learning model based on CHAT-23 for early screening of autism in Chinese children.

Autism spectrum disorder (ASD) is a neurodevelopmental condition that significantly impacts the mental, emotional, and social development of children. Early screening for ASD typically involves the use of a series of questionnaires. With answers to these questionnaires, healthcare professionals can identify whether a child is at risk for developing ASD and refer them for further evaluation and diagnosis. CHAT-23 is an effective and widely used screening test in China for the early screening of ASD, which contains 23 different kinds of questions. We have collected clinical data from Wuxi, China. All the questions of CHAT-23 are regarded as different kinds of features for building machine learning models. We introduce machine learning methods into ASD screening, using the Max-Relevance and Min-Redundancy (mRMR) feature selection method to analyze the most important questions among all 23 from the collected CHAT-23 questionnaires. Seven mainstream supervised machine learning models were built and experiments were conducted. Among the seven supervised machine learning models evaluated, the best-performing model achieved a sensitivity of 0.909 and a specificity of 0.922 when the number of features was reduced to 9. This demonstrates the model's ability to accurately identify children for ASD with high precision, even with a more concise set of features. Our study focuses on the health of Chinese children, introducing machine learning methods to provide more accurate and effective early screening tests for autism. This approach not only enhances the early detection of ASD but also helps in refining the CHAT-23 questionnaire by identifying the most relevant questions for the diagnosis process.

Socioeconomic Factors and Clinical Characteristics Associated with Age at Autism Spectrum Disorder Diagnosis

Objective: Investigate factors influencing autism spectrum disorder (ASD) diagnosis in children under and over 36 months, and determine the average age for ASD diagnosis at Charoenkrung Pracharak Hospital, Bangkok, Thailand, after implementing ASD screening. Materials and Methods: A retrospective cross-sectional study of 252 children with ASD aged 1-15 years from 2014 to 2023, classified into ≤36 months (n=104) and &gt;36 months (n=148) at diagnosis. Demographic, clinical data and child/family factors were analyzed using multivariate regression analysis. Results: Mean age at ASD diagnosis was 44.77 ± 19.91 months. Attending well-baby clinics (aOR 2.64, p=0.038), higher family income ≥ 814 US dollars per month (aOR 2.33, p=0.020), and higher parental education (aOR 3.43, p=0.011) were significantly associated with diagnosis before 36 months. Hyperactivity as the main complaint (aOR 0.07, p=0.001) and global developmental delay/intellectual disability (aOR 0.45, p=0.023) predicted later diagnosis. Child’s gender, being an only child, sibling order, and parental age had no significant impact. Conclusion: Over half of all ASD diagnoses occurred after age 3, with an average age around 44 months. Regular attendance in well-child clinics allowing ASD screening, and higher socioeconomic status and parental education facilitated earlier diagnosis. In contrast, concerns about hyperactivity and global developmental delay often lead to delayed ASD assessment. Enhancing awareness of ASD among families with limited resources can promote timely diagnosis and access to intervention.

Association between blood mitochondrial DNA copy number and mental disorders: A bidirectional two-sample mendelian randomization study

BackgroundMitochondria is essential for cellular energy production, oxidative stress, and apoptosis. Mitochondrial DNA (mtDNA) encodes essential proteins for mitochondrial function. Although several studies have explored the association between changes in mtDNA copy number (mtDNA-CN) and risk of mental disorders, the results remain debated. This study used a bidirectional two-sample Mendelian randomization (MR) analysis to examine the genetic causality between mtDNA-CN and mental disorders. MethodsGenome-wide association study (GWAS) data for mtDNA-CN were sourced from UK biobank, involving 383,476 European cases. GWAS data for seven mental disorders—attention deficit/hyperactivity disorder, autism spectrum disorder (ASD), schizophrenia, bipolar disorder, major depressive disorder, anxiety, and obsessive-compulsive disorder—were primarily obtained from the Psychiatric Genomics Consortium. Causal associations were assessed using inverse variance weighting, with sensitivity analyses via the weighted median and MR-Egger methods. Reverse MR considered the seven mental disorders as exposures. All analyses were replicated with additional mtDNA-CN GWAS data from 465,809 individuals in the Heart and Ageing Research in Genomic Epidemiology consortium and the UK Biobank. ResultsForward MR observed a 27 % decrease in the risk of ASD per standard deviation increase in genetically determined blood mtDNA-CN (OR = 0.73, 95%CI: 0.58–0.92, p = 0.002), with no causal effects on other disorders. Additionally, reverse MR did not indicate a causal association between any of the mental disorders and mtDNA-CN. Validation analyses corroborated these findings, indicating their robustness. ConclusionsOur study supports the potential causal association between mtDNA-CN and the risk of ASD, suggesting that mtDNA-CN could serve as a promising biomarker for early screening of ASD.

Prevalence of Developmental, Psychiatric, and Neurologic Conditions in Older Siblings of Children with and without Autism Spectrum Disorder: Study to Explore Early Development.

This study evaluated developmental, psychiatric, and neurologic conditions among older siblings of children with and without autism spectrum disorder (ASD) to understand the extent of familial clustering of these diagnoses. Using data from the Study to Explore Early Development, a large multi-site case-control study, the analyses included 2,963 children aged 2-5 years with ASD, other developmental disabilities (DD group), and a population-based control group (POP). Percentages of index children with older siblings with select developmental, psychiatric, and neurologic conditions were estimated and compared across index child study groups using chi-square tests and multivariable modified Poisson regression. In adjusted analyses, children in the ASD group were significantly more likely than children in the POP group to have one or more older siblings with ASD, developmental delay, attention-deficit/hyperactivity disorder, intellectual disability, sensory integration disorder (SID), speech/language delays, or a psychiatric diagnosis (adjusted prevalence ratio [aPR] range: 1.4-3.7). Children in the DD group were significantly more likely than children in the POP group to have an older sibling with most of the aforementioned conditions, except for intellectual disability and psychiatric diagnosis (aPR range: 1.4-2.2). Children in the ASD group were significantly more likely than children in the DD group to have one or more older siblings with ASD, developmental delay, SID, or a psychiatric diagnosis (aPR range: 1.4-1.9). These findings suggest that developmental disorders cluster in families. Increased monitoring and screening for ASD and other DDs may be warranted when an older sibling has a DD diagnosis or symptoms.

Linguistic summarization of visual attention and developmental functioning of young children with autism spectrum disorder

Diagnosing autism spectrum disorder (ASD) in children poses significant challenges due to its complex nature and impact on social communication development. While numerous data analytics techniques have been proposed for ASD evaluation, the process remains time-consuming and lacks clarity. Eye tracking (ET) data has emerged as a valuable resource for ASD risk assessment, yet existing literature predominantly focuses on predictive methods rather than descriptive techniques that offer human-friendly insights. Interpretation of ET data and Bayley scales, a widely used assessment tool, is challenging for ASD assessment of children. It should be understood clearly to perform better analytic tasks on ASD screening. Therefore, this study addresses this gap by employing linguistic summarization techniques to generate easily understandable summaries from raw ET data and Bayley scales. By integrating ET data and Bayley scores, the study aims to improve the identification of children with ASD from typically developing children (TD). Notably, this research represents one of the pioneering efforts to linguistically summarize ET data alongside Bayley scales, presenting comparative results between children with ASD and TD. Through linguistic summarization, this study facilitates the creation of simple, natural language statements, offering a first and unique approach to enhance ASD screening and contribute to our understanding of neurodevelopmental disorders.

Reliability and validity of the Polish version of Communication and Symbolic Behaviour Scales-Developmental Profile - Infant-Toddler Checklist

BackgroundEarly diagnosis of autism spectrum disorders allows to start a therapy tailored to the child - the earlier it happens, the better the results. This study marked a preliminary attempt to prepare culturally and linguistically adapted Polish version of the Communication and Symbolic Behavior Scales-Developmental Profile – Infant-Toddler Checklist (CSBS-DP ITC). MethodThe study was conducted among 1471 Polish parents of children from the general population aged 6 to 24 months, 490 of them participated in the follow-up. An additional 122 pairs of parents completed the questionnaire to determine reliability using the inter-rater method. The collected data were statistically processed to calculate the reliability and validity of the Polish version of the CSBS-DP ITC questionnaire. ResultsThe results indicated a very good fit of the one-factor and three-factor models in confirmatory factor analysis. Both one-factor and three-factor model showed satisfactory fit in both sex subsamples. The total score of CSBS-DP ITC demonstrated satisfactory internal consistency, Cronbach's α = .92 and McDonald’s ω = .92. In test-retest all the correlation coefficients between the first and second measurements were above.70, proving a satisfactory level of stability. ConclusionsThe Polish version of the CSBS-DP-ITC is a reliable tool for the early screening of autism spectrum disorder, but further studies on sensitivity and specificity are essential.

Pre and perinatal predictors on autism spectrum disorders: a case-control study in the west of Iran

IntroductionThe constellation of pre and perinatal predictors are introduced as predictor for autism spectrum disorders (ASD), however, the information about the direction and strength of these predictors are lacking in Western, Iran. The current study aimed to determine the pre and perinatal predictors of ASD among children in this region.MethodsThis case-control study was conducted in Hamadan, Western Iran during January to March 2022. The study included 100 children with ASD who referred to the autism center as case group. Hundred children without ASD from registration system of health service centers were selected as control group and were matched (1:1) to cases by age and place of residency. A structured questionnaire about pre and perinatal predictors of ASD was developed by an expert panel. The questionnaire was administered by interviewing the mothers of children.ResultsBoy gender (OR: 3.51, 95% CI: 1.74–7.10, p-value < 0.001), small for gestational age (SGA) (3.92, 1.64–9.39, 0.002), maternal diabetes (3.51, 1.03–24.95, 0.04) and family history of mental disorders (3.64, 1.18–11.27, 0.04) were identified as significant predictors in a multivariable analysis.ConclusionOur study emphasizes on the importance of screening and monitoring for ASD in the boys, those with history of SGA, from mothers with history of diabetes and with family history of mental disorders. Proposing the replication of findings emphasizes the necessity of conducting studies with larger sample sizes.

Early detection of autism spectrum disorder via deep-learning application of fMRI and machine learning for ASD children identifications

This study unveils an advanced convolutional-neural-network (CNN) algorithm that was meticulously engineered to examine resting-state functional magnetic resonance imaging (fMRI) for early ASD detection in pediatric cohorts. The CNN architecture amalgamates convolutional, pooling, batch-normalization, dropout, and fully connected layers, optimized for high-dimensional data interpretation. Rigorous preprocessing yielded 22,176 two-dimensional echo planar samples from 126 subjects (56 ASD, 70 controls) who were sourced from the Autism Brain Imaging Data Exchange (ABIDE I) repository. The model, trained on 17,740 samples across 50 epochs, demonstrated unparalleled diagnostic metrics – accuracy of 99.39%, recall of 98.80%, precision of 99.85%, and an F1 score of 99.32% – and thereby eclipsed extant computational methodologies. Feature map analyses substantiated the model’s hierarchical feature extraction capabilities. This research elucidates a deep learning framework for computer-assisted ASD screening via fMRI, with transformative implications for early diagnosis and intervention. And, this study addresses the critical need for early detection and intervention in autism spectrum disorder (ASD) using machine learning. Specific therapies are needed for ASD, a neurodevelopmental disease that affects social interaction and communication. To find trends in ASD, our research uses a variety of early childhood screening tests as training sets for machine learning algorithms. The methodology that has been suggested utilizes methods of machine learning to compute the ASD spectrum, considering its many expressions. By using multidisciplinary methods and sophisticated screening instruments, we want to create an accurate system for early ASD detection. Algorithmic transparency, data protection, and ethical considerations are essential. This study seeks to build precise instruments for early ASD detection by promoting collaboration between specialists in neurodevelopment, psychology, and machine learning. A robust instrument that enhances the knowledge of medical practitioners is machine learning. Results show how innovation may transform early interventions and help people on the autistic spectrum achieve enhanced results.

Combining developmental and sleep health measures for autism spectrum disorder screening: an ECHO study.

Sleep problems are reported for up to 80% of autistic individuals. We examined whether parsimonious sets of items derived from the Modified Checklist for Autism in Toddlers, Revised (M-CHAT-R) and the Brief Infant Sleep Questionnaire (BISQ) are superior to the standard M-CHAT-R in predicting subsequent autism spectrum disorder (ASD) diagnoses. Participants from 11 Environmental influences on Child Health Outcomes (ECHO) cohorts were included. We performed logistic LASSO regression models with 10-fold cross-validation to identify whether a combination of items derived from the M-CHAT-R and BISQ are superior to the standard M-CHAT-R in predicting ASD diagnoses. The final sample comprised 1552 children. The standard M-CHAT-R had a sensitivity of 44% (95% CI: 34, 55), specificity of 92% (95% CI: 91, 94), and AUROC of 0.726 (95% CI: 0.663, 0.790). A higher proportion of children with ASD had difficulty falling asleep or resisted bedtime during infancy/toddlerhood. However, LASSO models revealed parental reports of sleep problems did not improve the accuracy of the M-CHAT-R in predicting ASD diagnosis. While children with ASD had higher rates of sleep problems during infancy/toddlerhood, there was no improvement in ASD developmental screening through the incorporation of parent-report sleep metrics. Parental-reported sleep problems are common in autism spectrum disorder (ASD). We investigated whether the inclusion of parental-reports of infant/toddler sleep patterns enhanced the effectiveness of developmental screening for autism. We reported higher rates of difficulty falling asleep and resisting bedtime during infancy and toddlerhood among children later diagnosed with ASD; however, we did not find an improvement in ASD developmental screening through the incorporation of parent-report sleep metrics. In our sample, the standard M-CHAT-R had a sensitivity of 39% among children of mothers with government insurance compared with a sensitivity of 53% among children of mothers with employer-based insurance.

A Review on Autism Spectrum Disorder Screening by Artificial Intelligence Methods.

With the increasing prevalence of autism spectrum disorders (ASD), the importance of early screening and diagnosis has been subject to considerable discussion. Given the subtle differences between ASD children and typically developing children during the early stages of development, it is imperative to investigate the utilization of automatic recognition methods powered by artificial intelligence. We aim to summarize the research work on this topic and sort out the markers that can be used for identification. We searched the papers published in the Web of Science, PubMed, Scopus, Medline, SpringerLink, Wiley Online Library, and EBSCO databases from 1st January 2013 to 13th November 2023, and 43 articles were included. These articles mainly divided recognition markers into five categories: gaze behaviors, facial expressions, motor movements, voice features, and task performance. Based on the above markers, the accuracy of artificial intelligence screening ranged from 62.13 to 100%, the sensitivity ranged from 69.67 to 100%, the specificity ranged from 54 to 100%. Therefore, artificial intelligence recognition holds promise as a tool for identifying children with ASD. However, it still needs to continually enhance the screening model and improve accuracy through multimodal screening, thereby facilitating timely intervention and treatment.

Unraveling the Spectrum: A Comprehensive Review of Autism Spectrum Disorder in India.

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by deficits in social interaction, communication difficulties, and repetitive behaviors that profoundly impact the lives of affected individuals and their families. This article provides a comprehensive overview of ASD, focusing on screening, diagnosis, and intervention strategies. Early signs of ASD can manifest in infancy, but parents may not recognize them until their child falls behind in meeting social milestones. This delay in recognition is often due to a lack of awareness, societal stigma, and limited knowledge about developmental and behavioral disorders. Globally, ASD prevalence is increasingpotentially due to broader diagnostic criteria, increased awareness, and improved screening practices. Screening for ASD is crucial for early identification and intervention. Various tools are available such as the Modified Checklist for Autism in Toddlers (M-CHATs), Trivandrum Autism Behavioral Checklist (TABC), and the Social Communication Questionnaire (SCQ). Diagnosing ASD involves using established criteria such as the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), and specific diagnostic tools like the Autism Diagnostic Observation Schedule (ADOS) and the Indian Scale for the Assessment of Autism (ISAA). Interventions for ASD should be multidisciplinary, involving professionals such as developmental pediatricians, psychologists, psychiatrists, special educators, occupational therapists, physiotherapists, speech therapists, and social workers. Applied behavior analysis (ABA), naturalistic developmental behavioral interventions (NDBIs), and parent-mediated treatment are among the evidence-based approaches. Additionally, speech-language therapy, motor therapy, and sensory integration therapy play vital roles in addressing the diverse needs of individuals with ASD. Medical interventions should be used alongside behavioral and environmental strategies. Early screening, accurate diagnosis, and tailored interventions are essential for improving the lives of individuals with ASD. A multidisciplinary approach and increased awareness are crucial in addressing the growing prevalence of ASD worldwide.

Screening for Autism Spectrum Disorder in Children and Adolescents With Leber's Congenital Amaurosis

PurposeTo investigate autism spectrum disorder (ASD) indicators in children with Leber congenital amaurosis (LCA). MethodsCross-sectional, correlational, and prospective study. Participants included patients aged 2 to 16 years with LCA confirmed by genetic testing. There were 20 individuals with ciliopathies (LCA cilio) and 26 with other gene mutations (LCA other). The instrument used for ASD screening was the Autism Behavior Checklist (ABC). Marginal descriptive analyses, non-parametric tests, and a linear regression model were conducted. ResultsOf the 46 participants, 6 had ASD scores. There was no statistically significant correlation between the different groups (LCA cilio and LCA other) (p=0.438). There was no statistically significant correlation between age and ASD (p=0.308). However, there was a statistically significant correlation between visual acuity and ASD (p=0.008) and between male gender and ASD (p=0.025). ConclusionsThis study suggests that there is no correlation between LCA cilio, LCA other and ASD. These findings bring new insights to the existing literature, which previously lacked robust data on the relationship between LCA and ASD. These data demonstrate that visual acuity plays a crucial role in the development of children with visual impairment as poorer visual acuity is associated with a higher incidence of ASD. Based on this study, early interventions can be designed, especially for individuals without light perception, with the aim of maximizing their developmental outcomes. Furthermore, such data indicates that any improvement in visual acuity outcomes in treatment clinical trials become relevant for child development.

Cross-Cultural Generalizability of the First Year Inventory for Early ASD Screening in China.

The First Year Inventory (FYI) is a parent report screening measure, aimed at identifying the risk of autism spectrum disorder (ASD) in 12-month-old infants. This study aimed to investigate the utility of FYI within the Chinese community and develop a short version, encompassing both a low-risk sample and a high-risk sample comprising infants with older siblings diagnosed with ASD. Parents of 53 high-risk (HR) infants and 519 low-risk (LR) infants, aged 11 to 13 months, were recruited. After comparing response distributions across Chinese and American samples, a new factorial structure was developed according to the factor analyses. The construct validity and internal consistency of the two FYI versions were examined. The implementation of FYI in the HR sample was also assessed. Noteworthy disparities in response distribution were observed between the Chinese and American samples. Both FYI 2.0 and the FYI short version demonstrated moderate construct validity and internal consistency, with the FYI short version exhibiting better predictive ability in the HR sample. Significant lower risk scores was observed in the HR sample compared to the LR sample. These findings substantiate the applicability and validity of the Chinese short version of FYI. Future research should include follow-up assessments with the Chinese sample to evaluate cutoff scores, considering the cutoff between sensitivity and specificity and the sample?s characteristics.