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

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 patte...

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Autores principales: Tsuchiya, Kenji J., Hakoshima, Shuji, Hara, Takeshi, Ninomiya, Masaru, Saito, Manabu, Fujioka, Toru, Kosaka, Hirotaka, Hirano, Yoshiyuki, Matsuo, Muneaki, Kikuchi, Mitsuru, Maegaki, Yoshihiro, Harada, Taeko, Nishimura, Tomoko, Katayama, Taiichi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7876254/
https://www.ncbi.nlm.nih.gov/pubmed/33584502
http://dx.doi.org/10.3389/fneur.2020.603085
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author Tsuchiya, Kenji J.
Hakoshima, Shuji
Hara, Takeshi
Ninomiya, Masaru
Saito, Manabu
Fujioka, Toru
Kosaka, Hirotaka
Hirano, Yoshiyuki
Matsuo, Muneaki
Kikuchi, Mitsuru
Maegaki, Yoshihiro
Harada, Taeko
Nishimura, Tomoko
Katayama, Taiichi
author_facet Tsuchiya, Kenji J.
Hakoshima, Shuji
Hara, Takeshi
Ninomiya, Masaru
Saito, Manabu
Fujioka, Toru
Kosaka, Hirotaka
Hirano, Yoshiyuki
Matsuo, Muneaki
Kikuchi, Mitsuru
Maegaki, Yoshihiro
Harada, Taeko
Nishimura, Tomoko
Katayama, Taiichi
author_sort Tsuchiya, Kenji J.
collection PubMed
description 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.
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spelling pubmed-78762542021-02-12 Diagnosing Autism Spectrum Disorder Without Expertise: A Pilot Study of 5- to 17-Year-Old Individuals Using Gazefinder Tsuchiya, Kenji J. Hakoshima, Shuji Hara, Takeshi Ninomiya, Masaru Saito, Manabu Fujioka, Toru Kosaka, Hirotaka Hirano, Yoshiyuki Matsuo, Muneaki Kikuchi, Mitsuru Maegaki, Yoshihiro Harada, Taeko Nishimura, Tomoko Katayama, Taiichi Front Neurol Neurology 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. Frontiers Media S.A. 2021-01-28 /pmc/articles/PMC7876254/ /pubmed/33584502 http://dx.doi.org/10.3389/fneur.2020.603085 Text en Copyright © 2021 Tsuchiya, Hakoshima, Hara, Ninomiya, Saito, Fujioka, Kosaka, Hirano, Matsuo, Kikuchi, Maegaki, Harada, Nishimura and Katayama. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neurology
Tsuchiya, Kenji J.
Hakoshima, Shuji
Hara, Takeshi
Ninomiya, Masaru
Saito, Manabu
Fujioka, Toru
Kosaka, Hirotaka
Hirano, Yoshiyuki
Matsuo, Muneaki
Kikuchi, Mitsuru
Maegaki, Yoshihiro
Harada, Taeko
Nishimura, Tomoko
Katayama, Taiichi
Diagnosing Autism Spectrum Disorder Without Expertise: A Pilot Study of 5- to 17-Year-Old Individuals Using Gazefinder
title Diagnosing Autism Spectrum Disorder Without Expertise: A Pilot Study of 5- to 17-Year-Old Individuals Using Gazefinder
title_full Diagnosing Autism Spectrum Disorder Without Expertise: A Pilot Study of 5- to 17-Year-Old Individuals Using Gazefinder
title_fullStr Diagnosing Autism Spectrum Disorder Without Expertise: A Pilot Study of 5- to 17-Year-Old Individuals Using Gazefinder
title_full_unstemmed Diagnosing Autism Spectrum Disorder Without Expertise: A Pilot Study of 5- to 17-Year-Old Individuals Using Gazefinder
title_short Diagnosing Autism Spectrum Disorder Without Expertise: A Pilot Study of 5- to 17-Year-Old Individuals Using Gazefinder
title_sort diagnosing autism spectrum disorder without expertise: a pilot study of 5- to 17-year-old individuals using gazefinder
topic Neurology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7876254/
https://www.ncbi.nlm.nih.gov/pubmed/33584502
http://dx.doi.org/10.3389/fneur.2020.603085
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