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Early Autism Screening: A Comprehensive Review

 

The review article "Early Autism Screening: A Comprehensive Review" categorizes the identified screening methods into three main types: those targeting toddlers and children, adolescents and adults, and hybrid methods that cover all target ages. The study evaluated different screening methods, some with multiple versions, resulting in a total of 37 ASD screening instruments developed by various scholars.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6765988/ 

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects communication, social interactions, and behavior. Early detection of ASD is crucial for providing appropriate interventions and improving the quality of life for children with ASD. Various screening and diagnostic tools have been developed to identify autistic behaviors at an early stage, speed up the clinical diagnosis referral process, and improve the understanding of ASD for different stakeholders involved, such as parents, caregivers, teachers, and family members.

This article provides a comprehensive overview of the various assessment methods used for early autism screening, encompassing clinical, questionnaire-based, and emerging machine learning approaches. The assessment methods described in the study "Early Autism Screening: A Comprehensive Review" can be categorized into three main types: clinical judgment-based methods, parent questionnaires, and machine learning approaches.

Clinical Judgment-Based Methods

Clinical judgment-based assessment methods involve the direct evaluation of individuals by healthcare professionals. These methods include:
- Autism Diagnostic Interview-Revised (ADI-R)
- Autism Diagnostic Observation Schedule (ADOS)
- Childhood Autism Rating Scale (CARS)
- Joseph Picture Self-Concept Scale
- Social Responsiveness Scale

Parent Questionnaires

Parent questionnaires are designed to gather information from parents or caregivers about a child's behavior and development. These methods include:
- Modified Checklist for Autism in Toddlers (M-CHAT)
- First Year Inventory (FYI)
- Gilliam Autism Rating Scale (GADS)
- Autism Screening Instrument for Educational Planning (ASAS)

Machine Learning Approaches

Machine learning-based assessment methods utilize artificial intelligence to analyze data and identify patterns associated with autism. These approaches aim to improve the accuracy and efficiency of screening systems. The study "Autism Screening: An Unsupervised Machine Learning Approach" discusses an unsupervised machine learning approach for autism screening, which is based on artificial intelligence and data sampling.

In addition to the above-mentioned assessment methods, the literature study "Early Detection Assessment Tools in Children With Autism Spectrum Disorder" provides an overview of various types of assessments for early detection of ASD in children. The study highlights 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 review article "Diagnostic Assessment Techniques and Non-Invasive Biomarkers for Autism Spectrum Disorder" discusses various diagnostic methods and alternative and complementary therapies for ASD. The article highlights the emerging multi-modal neuro-imaging techniques that have correlated the brain's functional and structural measures and diagnosed ASD with more sensitivity than individual approaches.

The scoping review "The Role of Intelligent Technologies in Early Detection of Autism Spectrum Disorder (ASD)" evaluates the role of technology in ASD detection. The review highlights the extensive use of machine learning (ML) and Deep Learning (DL) to detect infants at risk of ASD and Other developmental delays (ODD) using multimodal structured and unstructured data.

The ASD screening instruments categorized by type, as described in the study "Early Autism Screening: A Comprehensive Review":

Toddlers and Children:

  1. Screening Tool for Autism in Toddlers and Young Children (STAT)
  2. Modified Checklist for Autism in Toddlers (M-CHAT)
  3. Childhood Autism Rating Scale (CARS)
  4. Autism Diagnostic Observation Schedule (ADOS)
  5. Social Communication Questionnaire (SCQ)
  6. Autism Spectrum Screening Questionnaire (ASSQ)
  7. Gilliam Autism Rating Scale (GARS)
  8. Social Responsiveness Scale (SRS)
  9. Autism Behavior Checklist (ABC)
  10. Autism Detection in Early Childhood (ADEC)
  11. Early Screening of Autistic Traits Questionnaire (ESAT)
  12. Autism Screening Instrument for Educational Planning (ASIEP)
  13. Autism Screening Questionnaire (ASQ)
  14. Developmental Behavior Checklist (DBC)
  15. Infant-Toddler Checklist (ITC)
  16. Modified Checklist for Autism in Toddlers-Revised (M-CHAT-R)
  17. Parent's Evaluation of Developmental Status (PEDS)
  18. Screening Test for Autism in Two-Year-Olds (STAT-2)
  19. Social Communication Assessment for Toddlers (SCAT)
  20. Social Communication Checklist (SCC)

Adolescents and Adults:

  1. Autism Spectrum Quotient (AQ)
  2. Diagnostic Interview for Social and Communication Disorders (DISCO)

Hybrid Methods:

  1. Autism Spectrum Quotient-Child (AQ-Child)
  2. Autism Spectrum Quotient-Adolescent (AQ-Adolescent)
  3. Autism Spectrum Quotient-Adult (AQ-Adult)
  4. Brief Infant-Toddler Social and Emotional Assessment (BITSEA)
  5. Child Behavior Checklist (CBCL)
  6. First Year Inventory (FYI)

In conclusion, early detection of ASD is crucial for providing appropriate interventions and improving the quality of life for children with ASD. Various screening and diagnostic tools have been developed to identify autistic behaviors at an early stage. The functionality and reliability of those screening tools vary according to different factors such as age, cultural differences, and the need for expert involvement. The comprehensive overview of the different screening methods for ASD can help healthcare practitioners and other stakeholders to identify and support children with ASD.

Citations:

[1] https://pubmed.ncbi.nlm.nih.gov/36092454/
[2] https://www.semanticscholar.org/paper/2cbfa21e0de305396e2ef796503e28a2bb99d03b
[3] https://pubmed.ncbi.nlm.nih.gov/36786236/
[4] https://www.semanticscholar.org/paper/a65feac384c6d888996c9c7315fc595dc80f12b1
[5] https://www.semanticscholar.org/paper/dad10b202c267f0a422b3ed9be022e15f294992e