Role of AI/ML in the Study of Autism Spectrum Disorders: A Bibliometric Analysis

被引：0

作者：

Jiran Meitei A. ^{[1
]}

Mohapatra B.B. ^{[1
]}

Khundrakpam B. ^{[2
]}

Tawfeeq Alee N. ^{[3
]}

Chauhan G. ^{[4
]}

机构：

[1] Maharaja Agrasen College, University of Delhi, New Delhi

[2] McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal

[3] Department of Applied Psychology, GITAM (Deemed to be University), Visakhapatnam

[4] Department of Operational Research, University of Delhi, New Delhi

来源：

Journal of Technology in Behavioral Science | 2024年 / 9卷 / 4期

关键词：

Artificial intelligence; Autism spectrum disorder; Co-citation; Co-occurrence; Collaborations;

D O I：

10.1007/s41347-024-00397-8

中图分类号：

学科分类号：

摘要：

This paper reviews the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques in studying autism spectrum disorders (ASD). A bibliometric analysis of 577 peer-reviewed English language articles published in 325 journals, sourced from two different databases: Scopus and Web of Science, is done with the objectives of having an overview of research, identifying relevant or impactful sources and their interconnections, assessing the contribution of AI/ML to ASD, in identifying the research gaps and mapping the future direction of research. The study finds that the USA, China, and Canada have the maximum contributions. Apparent collaborations between countries are also noticeable. The findings show that the use of AI/ML is possible in the diagnosis of ASD as evident from the many studies that have been conducted so far. Multiple ways and techniques have been explored and reported to be positive. The process is far quicker than the contemporary clinical diagnosis and offers a promising future. Perks of using AI/ML for the diagnosis of ASD include not only the possibility of low cost, easily accessible diagnosis or classification but also lightening the load on mental health professionals. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

引用

页码：809 / 824

页数：15

共 54 条

[1] Alex M., Pagnozzi E., Conti S., Calderoni J.F., Stephen E., Rose, A systematic review of structural MRI biomarkers in autism spectrum disorder: A machine learning perspective, International Journal of Developmental Neuroscience, 71, pp. 68-82, (2018)
[2] Diagnostic and Statistical Manual of Mental Disorders, (2013)
[3] Aria M., Cuccurullo C., Bibliometrix: An R-tool for comprehensive science mapping analysis, Journal of Informetrics, 11, 4, pp. 959-975, (2017)
[4] Baio J., Wiggins L., Christensen D.L., Prevalence of Autism Spectrum Disorder Among Children Aged 8 Years — Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2014, MMWR Surveillance Summaries, 67, SS-6, pp. 1-23, (2018)
[5] Black M.H., Kuzminski R., Wang J., Et al., Experiences of friendships for individuals on the Autism Spectrum: A scoping review, Review Journal of Autism and Developmental Disorders, (2022)
[6] Breiman L., Random forests, Machine Learning, 45, pp. 5-32, (2001)
[7] Camurri A., Ferrentino P., Interactive environments for music and multimedia, Multimedia Systems, 7, pp. 32-47, (1999)
[8] Chiarotti F., Venerosi A., Epidemiology of autism spectrum disorders: A review of worldwide prevalence estimates since 2014, Brain Sciences, 10, 5, (2020)
[9] Duda M., Kosmicki J., Wall D., Testing the accuracy of an observation-based classifier for rapid detection of autism risk, Translational Psychiatry, 4, (2014)
[10] Fidel R., Colis 4: Proceedings of the Fourth International Conference on Conceptions of Library and Information Science, Seattle, WA, USA, July 21–25, 2002., (2002)

← 1 2 3 4 5 6 →