Early Diagnosis of Autism Spectrum Disorder: A Review and Analysis of the Risks and Benefits

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.

Decoding the genetic landscape of autism: A comprehensive review

BACKGROUND

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by heterogeneous symptoms and genetic underpinnings. Recent advancements in genetic and epigenetic research have provided insights into the intricate mechanisms contributing to ASD, influencing both diagnosis and therapeutic strategies.

AIM

To explore the genetic architecture of ASD, elucidate mechanistic insights into genetic mutations, and examine gene-environment interactions.

METHODS

A comprehensive systematic review was conducted, integrating findings from studies on genetic variations, epigenetic mechanisms (such as DNA methylation and histone modifications), and emerging technologies [including Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-Cas9 and single-cell RNA sequencing]. Relevant articles were identified through systematic searches of databases such as PubMed and Google Scholar.

RESULTS

Genetic studies have identified numerous risk genes and mutations associated with ASD, yet many cases remain unexplained by known factors, suggesting undiscovered genetic components. Mechanistic insights into how these genetic mutations impact neural development and brain connectivity are still evolving. Epigenetic modifications, particularly DNA methylation and non-coding RNAs, also play significant roles in ASD pathogenesis. Emerging technologies like CRISPR-Cas9 and advanced bioinformatics are advancing our understanding by enabling precise genetic editing and analysis of complex genomic data.

CONCLUSION

Continued research into the genetic and epigenetic underpinnings of ASD is crucial for developing personalized and effective treatments. Collaborative efforts integrating multidisciplinary expertise and international collaborations are essential to address the complexity of ASD and translate genetic discoveries into clinical practice. Addressing unresolved questions and ethical considerations surrounding genetic research will pave the way for improved diagnostic tools and targeted therapies, ultimately enhancing outcomes for individuals affected by ASD.

Machine Learning Prediction of Autism Spectrum Disorder From a Minimal Set of Medical and Background Information

Importance

Early identification of the likelihood of autism spectrum disorder (ASD) using minimal information is crucial for early diagnosis and intervention, which can affect developmental outcomes.

Objective

To develop and validate a machine learning (ML) model for predicting ASD using a minimal set of features from background and medical information and to evaluate the predictors and the utility of the ML model.

Design, Setting, and Participants

For this diagnostic study, a retrospective analysis of the Simons Foundation Powering Autism Research for Knowledge (SPARK) database, version 8 (released June 6, 2022), was conducted, including data from 30 660 participants after adjustments for missing values and class imbalances (15 330 with ASD and 15 330 without ASD). The SPARK database contains participants recruited from 31 university-affiliated research clinicals and online in 26 states in the US. All individuals with a professional ASD diagnosis and their families were eligible to participate. The model performance was validated on independent datasets from SPARK, version 10 (released July 21, 2023), and the Simons Simplex Collection (SSC), consisting of 14 790 participants, followed by phenotypic associations.

Exposures

Twenty-eight basic medical screening and background history items present before 24 months of age.

Main Outcomes and Measures

Generalizable ML prediction models were developed for detecting ASD using 4 algorithms (logistic regression, decision tree, random forest, and eXtreme Gradient Boosting [XGBoost]). Performance metrics included accuracy, area under the receiver operating characteristics curve (AUROC), sensitivity, specificity, positive predictive value (PPV), and F1 score, offering a comprehensive assessment of the predictive accuracy of the model. Explainable AI methods were applied to determine the effect of individual features in predicting ASD as secondary outcomes, enhancing the interpretability of the best-performing model. The secondary outcome analyses were further complemented by examining differences in various phenotypic measures using nonparametric statistical methods, providing insights into the ability of the model to differentiate between different presentations of ASD.

Results

The study included 19 477 (63.5%) male and 11 183 (36.5%) female participants (mean [SD] age, 106 [62] months). The mean (SD) age was 113 (68) months for the ASD group and 100 (55) months for the non-ASD group. The XGBoost (termed AutMedAI) model demonstrated strong performance with an AUROC score of 0.895, sensitivity of 0.805, specificity of 0.829, and PPV of 0.897. Developmental milestones and eating behavior were the most important predictors. Validation on independent cohorts showed an AUROC of 0.790, indicating good generalizability.

Conclusions and Relevance

In this diagnostic study of ML prediction of ASD, robust model performance was observed to identify autistic individuals with more symptoms and lower cognitive levels. The robustness and ML model generalizability results are promising for further validation and use in clinical and population settings.

Emerging Treatments and Therapies for Autism Spectrum Disorder: A Narrative Review

The prevalence of autism spectrum disorder (ASD) has increased over the last decade. In this regard, many emerging therapies have been described as ASD therapies. Although ASD does not have a cure, there are several management options available that can help reduce symptom severity. ASD is highly variable and, therefore, standard treatment protocols and studies are challenging to perform. Many of these therapies also address comorbidities for which patients with ASD have an increased risk. These concurrent diagnoses can include psychiatric and neurological disorders, including attention deficit and hyperactivity disorder, anxiety disorders, and epilepsy, as well as gastrointestinal symptoms such as chronic constipation and diarrhea. Both the extensive list of ASD-associated disorders and adverse effects from commonly prescribed medications for patients with ASD can impact presenting symptomatology. It is important to keep these potential interactions in mind when considering additional drug treatments or complementary therapies. This review addresses current literature involving novel pharmacological treatments such as oxytocin, bumetanide, acetylcholinesterase inhibitors, and memantine. It also discusses additional therapies such as diet intervention, acupuncture, music therapy, melatonin, and the use of technology to aid education. Notably, several of these therapies require more long-term research to determine efficacy in specific ASD groups within this patient population.

Short report: Integrated evaluations for autism spectrum disorder in pediatric primary care clinics

LAY ABSTRACT

Primary care providers often screen for autism during well child visits in the first few years of life and refer children for diagnostic evaluations when needed. However, most children do not receive a diagnosis until years later which delays access to services. Racism, socioeconomic status, and other systemic inequalities that limit access to health care further delay diagnostic evaluations. Mental health clinicians who work in primary care clinics can help address barriers to accessing diagnostic evaluation services once they are recommended by their primary care provider. However, mental health clinicians who work in primary care typically do not have training in diagnosing autism. The goal of this study was to evaluate a program training mental health professionals working in an urban primary care setting, primarily serving Black and Latinx families insured by Medicaid, to provide autism diagnostic evaluations. Two hundred and fifty children completed evaluations through the Autism in Primary Care (APC) program. The wait time to access an evaluation through APC was significantly shorter than through standard avenues of care (e.g. referring to a separate autism clinic). Referring primary care providers and caregivers endorsed high levels of satisfaction with the program. Conducting autism evaluations in primary care settings offers a promising opportunity to improve earlier diagnosis and treatment access for families, reduce inequities in care, and increase caregiver and child well-being.

Association of Screen Time Exposure With Autism Spectrum Disorder in Four to Six-Year-Old Children in Arar City, Saudi Arabia

Background Autism spectrum disorder (ASD) is a psychopathologic disorder caused by several factors. The early signs include poor interaction and communication, delayed milestones, and repeated behavior patterns. This study aimed to assess the relationship between screen time and ASD severity and investigate the types of electronic devices associated with ASD in children aged four to six years in Arar City, Kingdom of Saudi Arabia (KSA). Methodology A cross-sectional study was conducted in primary healthcare centers (PHCs) in Arar City, KSA. The study enrolled all parents with children aged four to six years attending the PHCs in Arar City, KSA. Results The total sample size was 199 participants. Regarding the relationship between screen time exposure and ASD, there were variable screen time exposure durations, with 22.6% of children exposed for less than an hour, 30.7% for one to two hours, and 46.7% for more than two hours. Moreover, the type of electronic devices to which children were exposed varied, with smartphones being the most prevalent (68.3%). In terms of the age of children since exposure to electronic devices, the data indicated that 30.2% were exposed before the age of two, 35.2% between two and three years, and 34.7% after three years of age. Regarding the relationship with sociodemographic characteristics, there was a statistically significant relationship with the mother's age at birth (p = 0.050), mother's education level (p = 0.009), father's education level (p = 0.049), whether the child was suffering from any chronic or neurological disease (p = 0.008), age since the child was exposed to electronic devices (p = 0.049), and screen time exposure duration (p = 0.040). Conclusions The study highlights the significant association between screen time exposure and the development of ASD in children. Public awareness of this associated risk among caregivers is recommended to follow the protective guidelines. Further research and interventions are needed to better understand and address the impact of screen media use on children's neurodevelopment and overall well-being.