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Early Autism Screening: A Comprehensive Review

Author

Listed:
  • Fadi Thabtah

    (Department of Psychology, School of Human and Health Sciences, University of Huddersfield, Huddersfield HD1 3DH, UK)

  • David Peebles

    (Department of Psychology, School of Human and Health Sciences, University of Huddersfield, Huddersfield HD1 3DH, UK)

Abstract

Autistic spectrum disorder (ASD) refers to a neurodevelopmental condition associated with verbal and nonverbal communication, social interactions, and behavioural complications that is becoming increasingly common in many parts of the globe. Identifying individuals on the spectrum has remained a lengthy process for the past few decades due to the fact that some individuals diagnosed with ASD exhibit exceptional skills in areas such as mathematics, arts, and music among others. To improve the accuracy and reliability of autism diagnoses, many scholars have developed pre-diagnosis screening methods to help identify autistic behaviours at an early stage, speed up the clinical diagnosis referral process, and improve the understanding of ASD for the different stakeholders involved, such as parents, caregivers, teachers, and family members. However, the functionality and reliability of those screening tools vary according to different research studies and some have remained questionable. This study evaluates and critically analyses 37 different ASD screening tools in order to identify possible areas that need to be addressed through further development and innovation. More importantly, different criteria associated with existing screening tools, such as accessibility, the fulfilment of Diagnostic and Statistical Manual of Mental Disorders ( DSM-5 ) specifications, comprehensibility among the target audience, performance (specifically sensitivity, specificity, and accuracy), web and mobile availability, and popularity have been investigated.

Suggested Citation

  • Fadi Thabtah & David Peebles, 2019. "Early Autism Screening: A Comprehensive Review," IJERPH, MDPI, vol. 16(18), pages 1-28, September.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:18:p:3502-:d:268759
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    References listed on IDEAS

    as
    1. Dennis P Wall & Rebecca Dally & Rhiannon Luyster & Jae-Yoon Jung & Todd F DeLuca, 2012. "Use of Artificial Intelligence to Shorten the Behavioral Diagnosis of Autism," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-8, August.
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    Cited by:

    1. Judit Biosca-Brull & Cristian Pérez-Fernández & Santiago Mora & Beatriz Carrillo & Helena Pinos & Nelida Maria Conejo & Paloma Collado & Jorge L. Arias & Fernando Martín-Sánchez & Fernando Sánchez-San, 2021. "Relationship between Autism Spectrum Disorder and Pesticides: A Systematic Review of Human and Preclinical Models," IJERPH, MDPI, vol. 18(10), pages 1-30, May.
    2. Michał T. Tomczak & Joanna Maria Szulc & Małgorzata Szczerska, 2021. "Inclusive Communication Model Supporting the Employment Cycle of Individuals with Autism Spectrum Disorders," IJERPH, MDPI, vol. 18(9), pages 1-12, April.
    3. Bijan Mehralizadeh & Bahar Baradaran & Shahab Nikkhoo & Pegah Soleiman & Hadi Moradi, 2023. "A Sensorized Toy Car for Autism Screening Using Multi-Modal Features," Sustainability, MDPI, vol. 15(10), pages 1-10, May.
    4. Maya Hayden-Evans & Benjamin Milbourn & Emily D’Arcy & Angela Chamberlain & Bahareh Afsharnejad & Kiah Evans & Andrew J. O. Whitehouse & Sven Bölte & Sonya Girdler, 2022. "An Evaluation of the Overall Utility of Measures of Functioning Suitable for School-Aged Children on the Autism Spectrum: A Scoping Review," IJERPH, MDPI, vol. 19(21), pages 1-29, October.

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