Computer-aided autism diagnosis based on visual attention models using eye tracking

Abstract An advantage of using eye tracking for diagnosis is that it is non-invasive and can be performed in individuals with different functional levels and ages. Computer/aided diagnosis using eye tracking data is commonly based on eye fixation points in some regions of interest (ROI) in an image....

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Autores principales:	Jessica S. Oliveira, Felipe O. Franco, Mirian C. Revers, Andréia F. Silva, Joana Portolese, Helena Brentani, Ariane Machado-Lima, Fátima L. S. Nunes
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5ee7394feb46413b89afc75a8016dd5a
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spelling	oai:doaj.org-article:5ee7394feb46413b89afc75a8016dd5a2021-12-02T17:16:00ZComputer-aided autism diagnosis based on visual attention models using eye tracking10.1038/s41598-021-89023-82045-2322https://doaj.org/article/5ee7394feb46413b89afc75a8016dd5a2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-89023-8https://doaj.org/toc/2045-2322Abstract An advantage of using eye tracking for diagnosis is that it is non-invasive and can be performed in individuals with different functional levels and ages. Computer/aided diagnosis using eye tracking data is commonly based on eye fixation points in some regions of interest (ROI) in an image. However, besides the need for every ROI demarcation in each image or video frame used in the experiment, the diversity of visual features contained in each ROI may compromise the characterization of visual attention in each group (case or control) and consequent diagnosis accuracy. Although some approaches use eye tracking signals for aiding diagnosis, it is still a challenge to identify frames of interest when videos are used as stimuli and to select relevant characteristics extracted from the videos. This is mainly observed in applications for autism spectrum disorder (ASD) diagnosis. To address these issues, the present paper proposes: (1) a computational method, integrating concepts of Visual Attention Model, Image Processing and Artificial Intelligence techniques for learning a model for each group (case and control) using eye tracking data, and (2) a supervised classifier that, using the learned models, performs the diagnosis. Although this approach is not disorder-specific, it was tested in the context of ASD diagnosis, obtaining an average of precision, recall and specificity of 90%, 69% and 93%, respectively.Jessica S. OliveiraFelipe O. FrancoMirian C. ReversAndréia F. SilvaJoana PortoleseHelena BrentaniAriane Machado-LimaFátima L. S. NunesNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jessica S. Oliveira Felipe O. Franco Mirian C. Revers Andréia F. Silva Joana Portolese Helena Brentani Ariane Machado-Lima Fátima L. S. Nunes Computer-aided autism diagnosis based on visual attention models using eye tracking
description	Abstract An advantage of using eye tracking for diagnosis is that it is non-invasive and can be performed in individuals with different functional levels and ages. Computer/aided diagnosis using eye tracking data is commonly based on eye fixation points in some regions of interest (ROI) in an image. However, besides the need for every ROI demarcation in each image or video frame used in the experiment, the diversity of visual features contained in each ROI may compromise the characterization of visual attention in each group (case or control) and consequent diagnosis accuracy. Although some approaches use eye tracking signals for aiding diagnosis, it is still a challenge to identify frames of interest when videos are used as stimuli and to select relevant characteristics extracted from the videos. This is mainly observed in applications for autism spectrum disorder (ASD) diagnosis. To address these issues, the present paper proposes: (1) a computational method, integrating concepts of Visual Attention Model, Image Processing and Artificial Intelligence techniques for learning a model for each group (case and control) using eye tracking data, and (2) a supervised classifier that, using the learned models, performs the diagnosis. Although this approach is not disorder-specific, it was tested in the context of ASD diagnosis, obtaining an average of precision, recall and specificity of 90%, 69% and 93%, respectively.
format	article
author	Jessica S. Oliveira Felipe O. Franco Mirian C. Revers Andréia F. Silva Joana Portolese Helena Brentani Ariane Machado-Lima Fátima L. S. Nunes
author_facet	Jessica S. Oliveira Felipe O. Franco Mirian C. Revers Andréia F. Silva Joana Portolese Helena Brentani Ariane Machado-Lima Fátima L. S. Nunes
author_sort	Jessica S. Oliveira
title	Computer-aided autism diagnosis based on visual attention models using eye tracking
title_short	Computer-aided autism diagnosis based on visual attention models using eye tracking
title_full	Computer-aided autism diagnosis based on visual attention models using eye tracking
title_fullStr	Computer-aided autism diagnosis based on visual attention models using eye tracking
title_full_unstemmed	Computer-aided autism diagnosis based on visual attention models using eye tracking
title_sort	computer-aided autism diagnosis based on visual attention models using eye tracking
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5ee7394feb46413b89afc75a8016dd5a
work_keys_str_mv	AT jessicasoliveira computeraidedautismdiagnosisbasedonvisualattentionmodelsusingeyetracking AT felipeofranco computeraidedautismdiagnosisbasedonvisualattentionmodelsusingeyetracking AT miriancrevers computeraidedautismdiagnosisbasedonvisualattentionmodelsusingeyetracking AT andreiafsilva computeraidedautismdiagnosisbasedonvisualattentionmodelsusingeyetracking AT joanaportolese computeraidedautismdiagnosisbasedonvisualattentionmodelsusingeyetracking AT helenabrentani computeraidedautismdiagnosisbasedonvisualattentionmodelsusingeyetracking AT arianemachadolima computeraidedautismdiagnosisbasedonvisualattentionmodelsusingeyetracking AT fatimalsnunes computeraidedautismdiagnosisbasedonvisualattentionmodelsusingeyetracking
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Computer-aided autism diagnosis based on visual attention models using eye tracking

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