Use of Artificial Intelligence in the Diagnostics of Autism Spectrum Disorder

Authors

  • Gabriele Mustika Kresnia Department of Bioinformatics, School of Life Sciences, Indonesia International Institute for Life Sciences, Jakarta, Indonesia
  • Arli Aditya Parikesit Department of Bioinformatics, School of Life Sciences, Indonesia International Institute for Life Sciences, Jakarta, Indonesia

DOI:

https://doi.org/10.55175/cdk.v49i6.246

Keywords:

Artificial intelligence, autism spectrum disorder, EEG, social communication, genome

Abstract

Autism Spectrum Disorder (ASD) is a neurologic development disorder; it is listed in the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-V). Early diagnosis is critical in improving the life quality of individuals affected by ASD. Several studies show that Artificial Intelligence can be used in the diagnosis of ASD through biological means such as observing patient EEG data and surveying their genome. Articles were searched in the PUBMED database, ScienceDirect, and Springer Link between 2019 - 2020. Four papers were selected for review. The papers devised models that can accurately predict ASD in affected individuals, though some are based on old data and/or require testing on larger datasets to determine accuracy. As ASD diagnosis usually cannot be achieved before the individual shows symptoms, AI has the potential to improve ASD diagnosis in affected individuals. Further study to confirm the models and test on larger, more recent datasets would be required to develop more accurate models and achieve even better results.

Autism spectrum disorder (ASD) merupakan salah satu gangguan perkembangan saraf yang tercantum pada Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-V). Diagnosis dini sangat penting untuk meningkatkan kualitas hidup individu ASD. Beberapa penelitian menunjukkan bahwa kecerdasan buatan dapat digunakan untuk diagnosis ASD melalui metode berbasis biologis seperti mengamati data EEG pasien dan mensurvei genomnya. Review ini berbasis pencarian data antara tahun 2019 – 2020 di database PUBMED, ScienceDirect, dan Springer Link. Empat makalah kunci dipilih untuk ditinjau. Makalah-makalah tersebut mampu merancang model yang dapat memprediksi ASD secara akurat, meskipun beberapa aspek implementasinya didasarkan pada data usang dan/ atau memerlukan pengujian pada kumpulan data yang lebih besar untuk menentukan akurasi. Mengingat diagnosis ASD biasanya tidak dapat dilakukan sebelum individu menunjukkan gejala, kecerdasan buatan berpotensi meningkatkan ketepatan diagnosis ASD. Masih diperlukan studi lanjutan untuk mengkonfirmasi model dan pengujian pada kumpulan data yang lebih besar dan lebih baru untuk mengembangkan model yang memiliki presisi lebih baik dan hasil lebih akurat.

Downloads

Download data is not yet available.

References

Murray AL, Allison C, Smith PL, Baron-Cohen S, Booth T, Auyeung B. Investigating diagnostic bias in autism spectrum conditions: An item response theory analysis of sex bias in the AQ-10. Autism Res [Internet]. 2017 [cited 2022 Jan 20];10(5):790–800. Available from: https://pubmed.ncbi.nlm.nih.gov/27891820/

Bagatell N. From cure to community: Transforming notions of autism. Ethos [Internet]. 2010 [cited 2022 Jan 20];38(1):33–55. Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/j.1548-1352.2009.01080.x

Abdolzadegan D, Moattar MH, Ghoshuni M. A robust method for early diagnosis of autism spectrum disorder from EEG signals based on feature selection and DBSCAN method. Biocybern Biomed Eng. 2020;40(1):482–93.

Bahado-Singh RO, Vishweswaraiah S, Aydas B, Mishra NK, Yilmaz A, Guda C, et al. Artificial intelligence analysis of newborn leucocyte epigenomic markers for the prediction of autism. Brain Res. 2019;1724:146457.

Bajestani GS, Behrooz M, Khani AG, Nouri-Baygi M, Mollaei A. Diagnosis of autism spectrum disorder based on complex network features. Comput Methods Programs Biomed. 2019;177:277–83.

Gök M. A novel machine learning model to predict autism spectrum disorders risk gene. Neural Comput Appl 2018 3110 [Internet]. 2018 [cited 2022 Jan 20];31(10):6711–7. Available from: https://link.springer.com/article/10.1007/s00521-018-3502-5

Alpaydin E. Introduction to machine learning. [cited 2022 Jan 20];682. Available from: https://mitpress.mit.edu/books/introduction-machine-learning-fourth-edition

Kaderali L, Radde N. Inferring gene regulatory networks from expression data. Stud Comput Intell. 2008;94:33–74.

Noble WS. What is a support vector machine? Nat Biotechnol 2006 2412 [Internet]. 2006 [cited 2022 Jan 20];24(12):1565–7. Available from: https://www.nature.com/articles/nbt1206-1565

Zhang Y. Support vector machine classification algorithm and its application. Commun Comput Inf Sci [Internet]. 2012 [cited 2022 Jan 20];308 CCIS(PART 2):179–86. Available from: https://link.springer.com/chapter/10.1007/978-3-642-34041-3_27

Wu Y, Ianakiev K, Govindaraju V. Improved k-nearest neighbor classification. Pattern Recognit. 2002;35(10):2311–8.

Bengio Y, Goodfellow I, Courville A. MIT press. Deep learning. 2017;1:1-7

Guo Y, Liu Y, Oerlemans A, Lao S, Wu S, Lew MS. Deep learning for visual understanding: A review. Neurocomputing 2016;187:27–48.

Anurogo D, Parikesit AA. Troubled helix – Tinjauan multiperspektif genetika dalam bioetika. Cermin Dunia Kedokt [Internet]. 2021 [cited 2021 Mar 9];48(3):147–53. Available from: http://103.13.36.125/index.php/CDK/article/viewt/1331

Parikesit AA. Introductory chapter: The contribution of bioinformatics as blueprint lead for drug design. In: Glavic I, editor. Molecular insight of drug design [Internet]. London: InTech; 2018 [cited 2018 Aug 29]:7. Available from: http://www.intechopen.com/books/molecular-insight-of-drug-design/introductory-chapter-thecontribution-of-bioinformatics-as-blueprint-lead-for-drug-design

Satya PGAN, Parikesit AA. Revolution in detecting tuberculosis using radiology with application of deep learning algorithm. Cermin Dunia Kedokt [Internet].2021;48(4):261–3. Available from: http://www.cdkjournal.com/index.php/CDK/issue/view/98

Downloads

Published

01-06-2022

How to Cite

Kresnia, G. M., & Parikesit, A. A. (2022). Use of Artificial Intelligence in the Diagnostics of Autism Spectrum Disorder. Cermin Dunia Kedokteran, 49(6), 341–344. https://doi.org/10.55175/cdk.v49i6.246

Issue

Section

Articles