Diagnosing Autism Spectrum Disorder Without Expertise: A Pilot Study of 5- to 17-Year-Old Individuals Using Gazefinder.

Gazefinder Japan adolescent autism spectrum disorder machine learning school-age children

Journal

Frontiers in neurology
ISSN: 1664-2295
Titre abrégé: Front Neurol
Pays: Switzerland
ID NLM: 101546899

Informations de publication

Date de publication:
2020
Historique:
received: 05 09 2020
accepted: 30 12 2020
entrez: 15 2 2021
pubmed: 16 2 2021
medline: 16 2 2021
Statut: epublish

Résumé

Atypical eye gaze is an established clinical sign in the diagnosis of autism spectrum disorder (ASD). We propose a computerized diagnostic algorithm for ASD, applicable to children and adolescents aged between 5 and 17 years using Gazefinder, a system where a set of devices to capture eye gaze patterns and stimulus movie clips are equipped in a personal computer with a monitor. We enrolled 222 individuals aged 5-17 years at seven research facilities in Japan. Among them, we extracted 39 individuals with ASD without any comorbid neurodevelopmental abnormalities (ASD group), 102 typically developing individuals (TD group), and an independent sample of 24 individuals (the second control group). All participants underwent psychoneurological and diagnostic assessments, including the Autism Diagnostic Observation Schedule, second edition, and an examination with Gazefinder (2 min). To enhance the predictive validity, a best-fit diagnostic algorithm of computationally selected attributes originally extracted from Gazefinder was proposed. The inputs were classified automatically into either ASD or TD groups, based on the attribute values. We cross-validated the algorithm using the leave-one-out method in the ASD and TD groups and tested the predictability in the second control group. The best-fit algorithm showed an area under curve (AUC) of 0.84, and the sensitivity, specificity, and accuracy were 74, 80, and 78%, respectively. The AUC for the cross-validation was 0.74 and that for validation in the second control group was 0.91. We confirmed that the diagnostic performance of the best-fit algorithm is comparable to the diagnostic assessment tools for ASD.

Identifiants

DOI: 10.3389/fneur.2020.603085 PMID: 33584502 PMC: PMC7876254
pubmed: 33584502
doi: 10.3389/fneur.2020.603085
pmc: PMC7876254
doi:

Types de publication

Journal Article

Langues

eng

Pagination

603085

Informations de copyright

Copyright © 2021 Tsuchiya, Hakoshima, Hara, Ninomiya, Saito, Fujioka, Kosaka, Hirano, Matsuo, Kikuchi, Maegaki, Harada, Nishimura and Katayama.

Déclaration de conflit d'intérêts

SH and MN are employed by JVCKENWOOD Corporation. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Kenji J Tsuchiya (KJ)

Research Center for Child Mental Development, Hamamatsu University School of Medicine, Hamamatsu, Japan.
Department of Child Development, United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University, and University of Fukui, Suita, Japan.

Shuji Hakoshima (S)

Healthcare Business Division, Development Center, JVCKENWOOD Corporation, Yokohama, Japan.

Takeshi Hara (T)

Center for Healthcare Information Technology, Tokai National Higher Education and Research System, Gifu, Japan.
Faculty of Engineering, Gifu University, Gifu, Japan.

Masaru Ninomiya (M)

Healthcare Business Division, Development Center, JVCKENWOOD Corporation, Yokohama, Japan.

Manabu Saito (M)

Department of Neuropsychiatry, Graduate School of Medicine, Hirosaki University, Hirosaki, Japan.
Research Center for Child Mental Development, Graduate School of Medicine, Hirosaki University, Hirosaki, Japan.

Toru Fujioka (T)

Department of Child Development, United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University, and University of Fukui, Suita, Japan.
Department of Science of Human Development, Faculty of Education, Humanities and Social Sciences, University of Fukui, Fukui, Japan.
Research Center for Child Mental Development, University of Fukui, Fukui, Japan.

Hirotaka Kosaka (H)

Department of Child Development, United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University, and University of Fukui, Suita, Japan.
Research Center for Child Mental Development, University of Fukui, Fukui, Japan.
Department of Neuropsychiatry, Faculty of Medical Sciences, University of Fukui, Fukui, Japan.

Yoshiyuki Hirano (Y)

Department of Child Development, United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University, and University of Fukui, Suita, Japan.
Research Center for Child Mental Development, Chiba University, Chiba, Japan.

Muneaki Matsuo (M)

Department of Pediatrics, Faculty of Medicine, Saga University, Saga, Japan.

Mitsuru Kikuchi (M)

Department of Child Development, United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University, and University of Fukui, Suita, Japan.
Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan.
Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan.

Yoshihiro Maegaki (Y)

Tottori University Hospital, Yonago, Japan.

Taeko Harada (T)

Research Center for Child Mental Development, Hamamatsu University School of Medicine, Hamamatsu, Japan.
Department of Child Development, United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University, and University of Fukui, Suita, Japan.

Tomoko Nishimura (T)

Research Center for Child Mental Development, Hamamatsu University School of Medicine, Hamamatsu, Japan.
Department of Child Development, United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University, and University of Fukui, Suita, Japan.

Taiichi Katayama (T)

Department of Child Development, United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University, and University of Fukui, Suita, Japan.

Classifications MeSH