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A Systematic Literature Review on the Application of Machine-Learning Models in Behavioral Assessment of Autism Spectrum Disorder
Autism spectrum disorder (ASD) is associated with significant social, communication, and behavioral challenges. The insufficient number of trained clinicians coupled with limited accessibility to quick and accurate diagnostic tools resulted in overlooking early symptoms of ASD in children around the...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8070763/ https://www.ncbi.nlm.nih.gov/pubmed/33919878 http://dx.doi.org/10.3390/jpm11040299 |
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author | Cavus, Nadire Lawan, Abdulmalik A. Ibrahim, Zurki Dahiru, Abdullahi Tahir, Sadiya Abdulrazak, Usama Ishaq Hussaini, Adamu |
author_facet | Cavus, Nadire Lawan, Abdulmalik A. Ibrahim, Zurki Dahiru, Abdullahi Tahir, Sadiya Abdulrazak, Usama Ishaq Hussaini, Adamu |
author_sort | Cavus, Nadire |
collection | PubMed |
description | Autism spectrum disorder (ASD) is associated with significant social, communication, and behavioral challenges. The insufficient number of trained clinicians coupled with limited accessibility to quick and accurate diagnostic tools resulted in overlooking early symptoms of ASD in children around the world. Several studies have utilized behavioral data in developing and evaluating the performance of machine learning (ML) models toward quick and intelligent ASD assessment systems. However, despite the good evaluation metrics achieved by the ML models, there is not enough evidence on the readiness of the models for clinical use. Specifically, none of the existing studies reported the real-life application of the ML-based models. This might be related to numerous challenges associated with the data-centric techniques utilized and their misalignment with the conceptual basis upon which professionals diagnose ASD. The present work systematically reviewed recent articles on the application of ML in the behavioral assessment of ASD, and highlighted common challenges in the studies, and proposed vital considerations for real-life implementation of ML-based ASD screening and diagnostic systems. This review will serve as a guide for researchers, neuropsychiatrists, psychologists, and relevant stakeholders on the advances in ASD screening and diagnosis using ML. |
format | Online Article Text |
id | pubmed-8070763 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-80707632021-04-26 A Systematic Literature Review on the Application of Machine-Learning Models in Behavioral Assessment of Autism Spectrum Disorder Cavus, Nadire Lawan, Abdulmalik A. Ibrahim, Zurki Dahiru, Abdullahi Tahir, Sadiya Abdulrazak, Usama Ishaq Hussaini, Adamu J Pers Med Review Autism spectrum disorder (ASD) is associated with significant social, communication, and behavioral challenges. The insufficient number of trained clinicians coupled with limited accessibility to quick and accurate diagnostic tools resulted in overlooking early symptoms of ASD in children around the world. Several studies have utilized behavioral data in developing and evaluating the performance of machine learning (ML) models toward quick and intelligent ASD assessment systems. However, despite the good evaluation metrics achieved by the ML models, there is not enough evidence on the readiness of the models for clinical use. Specifically, none of the existing studies reported the real-life application of the ML-based models. This might be related to numerous challenges associated with the data-centric techniques utilized and their misalignment with the conceptual basis upon which professionals diagnose ASD. The present work systematically reviewed recent articles on the application of ML in the behavioral assessment of ASD, and highlighted common challenges in the studies, and proposed vital considerations for real-life implementation of ML-based ASD screening and diagnostic systems. This review will serve as a guide for researchers, neuropsychiatrists, psychologists, and relevant stakeholders on the advances in ASD screening and diagnosis using ML. MDPI 2021-04-14 /pmc/articles/PMC8070763/ /pubmed/33919878 http://dx.doi.org/10.3390/jpm11040299 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Cavus, Nadire Lawan, Abdulmalik A. Ibrahim, Zurki Dahiru, Abdullahi Tahir, Sadiya Abdulrazak, Usama Ishaq Hussaini, Adamu A Systematic Literature Review on the Application of Machine-Learning Models in Behavioral Assessment of Autism Spectrum Disorder |
title | A Systematic Literature Review on the Application of Machine-Learning Models in Behavioral Assessment of Autism Spectrum Disorder |
title_full | A Systematic Literature Review on the Application of Machine-Learning Models in Behavioral Assessment of Autism Spectrum Disorder |
title_fullStr | A Systematic Literature Review on the Application of Machine-Learning Models in Behavioral Assessment of Autism Spectrum Disorder |
title_full_unstemmed | A Systematic Literature Review on the Application of Machine-Learning Models in Behavioral Assessment of Autism Spectrum Disorder |
title_short | A Systematic Literature Review on the Application of Machine-Learning Models in Behavioral Assessment of Autism Spectrum Disorder |
title_sort | systematic literature review on the application of machine-learning models in behavioral assessment of autism spectrum disorder |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8070763/ https://www.ncbi.nlm.nih.gov/pubmed/33919878 http://dx.doi.org/10.3390/jpm11040299 |
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