Skip to main content
SpringerLink
Log in

Autism AI: a New Autism Screening System Based on Artificial Intelligence

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

Autistic spectrum disorder (ASD) is a neurodevelopment condition normally linked with substantial healthcare costs and time-consuming assessments where early detection of ASD traits can help limit the development of the condition. The existing conventional ASD screening methods contain a large number of items and are based on domain expert rules which may be criticized of being lengthy and subjective. More importantly, these methods use basic scoring functions to pinpoint to autistic traits rather intelligently learning patterns from cases and controls which can be more accurate and efficient. One promising solution to deal with the above issues and speed up ASD assessment referrals is to develop intelligent artificial intelligence screening methods that not only provide accurate pre-diagnostic classifications but also improve the efficiency and accessibility of the screening process. This paper proposes a new autism screening system that replaces the conventional scoring functions in classic screening methods with deep learning algorithms. The system is composed of a mobile application that provides the user interface capturing questionnaire data; an intelligent ASD detection web service that interfaces with a Convolutional Neural Network (CNN) trained with historical ASD cases; and a database that enables the CNN to learn new knowledge from future users of the system. The CNN classification method was evaluated against a large autism dataset consisting of adult, adolescent, child, and toddler cases and controls. The results obtained from the CNN were compared with other intelligent algorithms in which superior performance was achieved by the CNN. Particularly, the proposed CNN-based ASD classification system revealed higher accuracy, sensitivity, and specificity when compared with conventional screening methods. This indeed will be of high benefit for busy medical clinics and diagnosticians and could possibly be a new direction to change the way ASD diagnosis process is conducted in the future.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. S. A. Schoen et al., “A systematic review of Ayres sensory integration intervention for children with autism,” Autism Research. 2019.

  2. Towle PO, Patrick PA. Autism spectrum disorder screening instruments for very young children: a systematic review: Autism Res. Treat; 2016.

  3. F. Thabtah and Fadi, “Autism spectrum disorder screening: machine learning adaptation and DSM-5 fulfillment,” in Proceedings of the 1st International Conference on Medical and Health Informatics 2017 - ICMHI ’17, 2017.

  4. Wall DP, Dally R, Luyster R, Jung JY, DeLuca TF. Use of Artificial Intelligence to Shorten the Behavioral Diagnosis of Autism. In: Use of artificial intelligence to shorten the behavioral diagnosis of autism: PLoS One; 2012.

  5. M. Duda, J. A. Kosmicki, and D. P. Wall, “Testing the accuracy of an observation-based classifier for rapid detection of autism risk,” Translational psychiatry. 2015.

  6. M. Marlow, C. Servili, and M. Tomlinson, “A review of screening tools for the identification of autism spectrum disorders and developmental delay in infants and young children: recommendations for use in low- and middle-income countries,” Autism Research. 2019.

  7. Duda M, Ma R, Haber N, Wall DP. Use of machine learning for behavioral distinction of autism and ADHD. In: Use of machine learning for behavioral distinction of autism and ADHD. Psychiatry: Transl; 2016.

    Chapter  Google Scholar 

  8. Bone D, Bishop SL, Black MP, Goodwin MS, Lord C, Narayanan SS. Use of machine learning to improve autism screening and diagnostic instruments: effectiveness, efficiency, and multi-instrument fusion. J. Child Psychol. Psychiatry. Aug. 2016;57(8):927–37.

    Article  Google Scholar 

  9. Thabtah F. Machine learning in autistic spectrum disorder behavioral research: a review and ways forward. Care: Informatics Heal. Soc; 2018.

    Google Scholar 

  10. Little SG, Akin-Little A, Harris GM. Autism spectrum disorder: screening and diagnosis. In: Behavioral interventions in schools: Evidence-based positive strategies. 2nd ed; 2019.

    Chapter  Google Scholar 

  11. S. R. Shahamiri and S. S. B. Salim, “A multi-views multi-learners approach towards dysarthric speech recognition using multi-nets artificial neural networks,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 22, no. 5, pp. 1053–1063, 2014, A Multi-Views Multi-Learners Approach Towards Dysarthric Speech Recognition Using Multi-Nets Artificial Neural Networks.

  12. Sremath S, et al. Speaker identification features extraction methods : a systematic review. Expert Syst. Appl. 2017;90:250–71.

    Article  Google Scholar 

  13. Baron-Cohen S, Allen J, Gillberg C. Can autism be detected at 18 months? The needle, the haystack, and the CHAT. Br. J. Psychiatry. 1992;161:839–43.

    Article  Google Scholar 

  14. Robins DL, Fein D, Barton ML, Green JA. The modified checklist for autism in toddlers: an initial study investigating the early detection of autism and pervasive developmental disorders: J. Autism Dev. Disord; 2001.

  15. C. Allison, B. Auyeung, and S. Baron-Cohen, “Toward brief ‘Red Flags’ for autism screening: the short autism spectrum quotient and the short quantitative checklist in 1,000 cases and 3,000 controls,” J. Am. Acad. Child Adolesc. Psychiatry, vol. 51, no. 2, pp. 202-212.e7, Feb. 2012.

  16. Wong V, et al. A Modified Screening Tool for Autism (Checklist for Autism in Toddlers [CHAT-23]) for Chinese Children. In: A modified screening tool for autism (Checklist for Autism in Toddlers [CHAT-23]) for Chinese children: Pediatrics; 2004.

  17. Baron-Cohen S, Wheelwright S, Skinner R, Martin J, Clubley E. The autism-spectrum quotient (AQ): evidence from Asperger syndrome/high-functioning autism, males and females, scientists and mathematicians. J. Autism Dev. Disord. Feb. 2001;31(1):5–17.

    Article  Google Scholar 

  18. Wheelwright S, et al. Predicting Autism Spectrum Quotient (AQ) from the Systemizing Quotient-Revised (SQ-R) and Empathy Quotient (EQ). In: Predicting autism spectrum quotient (AQ) from the systemizing quotient-revised (SQ-R) and empathy quotient (EQ): Brain Res; 2006.

  19. Auyeung B, Baron-Cohen S, Wheelwright S, Allison C. The autism spectrum quotient: children’s version (AQ-Child): J. Autism Dev. Disord; 2008.

  20. K.-C. Chu, H.-J. Huang, and Y.-S. Huang, “Machine learning approach for distinction of ADHD and OSA,” in 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2016, pp. 1044–1049.

  21. T. Wolfers, J. K. Buitelaar, C. F. Beckmann, B. Franke, and A. F. Marquand, “From estimating activation locality to predicting disorder: a review of pattern recognition for neuroimaging-based psychiatric diagnostics,” Neuroscience and Biobehavioral Reviews. 2015.

  22. J. L. Marcano, M. A. Bell, and A. A. (Louis. Beex, “Classification of ADHD and non-ADHD subjects using a universal background model,” Biomed. Signal Process. Control, 2018, Classification of ADHD and non-ADHD subjects using a universal background model.

  23. M. J. Maenner, M. Yeargin-Allsopp, K. Van Naarden Braun, D. L. Christensen, and L. A. Schieve, “Development of a machine learning algorithm for the surveillance of autism spectrum disorder,” PLoS One, vol. 11, no. 12, p. e0168224, Dec. 2016.

  24. Thabtah F, Kamalov F, Rajab K. A new computational intelligence approach to detect autistic features for autism screening. In: A new computational intelligence approach to detect autistic features for autism screening: Int. J. Med. Inform; 2018.

  25. F. Thabtah, “An accessible and efficient autism screening method for behavioural data and predictive analyses,” Health Informatics J., p. 146045821879663, Sep. 2018.

  26. Ltd HM. Awesomely Autistic Test: Google Play Store; 2016.

  27. “Autism Test.” Google Play Store, 2015.

  28. N. Sadka, W. Nadachowski, C. Dissanayake, and J. Barbaro, “Early childhood autism surveillance and assessment tool | ASDetect.” 2016.

    Google Scholar 

  29. S. R. Shahamiri and F. Thabtah, “Autism AI,” 2018. [Online]. Available: https://play.google.com/store/apps/details?id = com.rezanet.intelligentasdscreener.

  30. Shahamiri SR, Wan-Kadir WMN, Ibrahim S, Hashim SZM. Artificial neural networks as multi-networks automated test oracle. Autom. Softw. Eng. 2012;19(3):303–34.

    Article  Google Scholar 

  31. S. R. Shahamiri and F. Thabtah, “Intelligent Autistic Traits Detection Web Service,” 2018. [Online]. Available: http://rshahamiri.pythonanywhere.com/.

  32. Dong Q, Gong S, Zhu X. Imbalanced deep learning by minority class incremental rectification. IEEE Trans. Pattern Anal. Mach. Intell. 2018:1–1.

  33. Egger HL, et al. Automatic emotion and attention analysis of young children at home: a ResearchKit autism feasibility study. npj Digit. Med. Dec. 2018;1(1):1–10.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical, Computer, and Software Engineering, Faculty of Engineering, The University of Auckland, Auckland, New Zealand

    Seyed Reza Shahamiri

  2. Manukau Institute of Technology, Auckland, New Zealand

    Fadi Thabtah

Authors
  1. Seyed Reza Shahamiri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fadi Thabtah
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Seyed Reza Shahamiri.

Ethics declarations

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Conflict of Interest

Seyed Reza Shahamiri declares that he has no conflict of interest. Fadi Thabtah declares that he has no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shahamiri, S.R., Thabtah, F. Autism AI: a New Autism Screening System Based on Artificial Intelligence. Cogn Comput 12, 766–777 (2020). https://doi.org/10.1007/s12559-020-09743-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-020-09743-3

Keywords

Search

Navigation