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

Autor(es)

Oliveira, Jessica S. ^[1] ; Franco, Felipe O. ^{[2, 3]} ; Revers, Mirian C. ^[3] ; Silva, Andreia F. ^[3] ; Portolese, Joana ^[3] ; Brentani, Helena ^{[2, 3]} ; Machado-Lima, Ariane ^{[1, 2]} ; Nunes, Fatima L. S. ^[1]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Sch Arts Sci & Humanities EACH, BR-03828000 Sao Paulo, SP - Brazil

^[2] Univ Sao Paulo, Inst Math & Stat IME, Interunit PostGrad Program Bioinformat, BR-05508090 Sao Paulo, SP - Brazil

^[3] Univ Sao Paulos Sch Med FMUSP, Dept Psychiat, BR-05403903 Sao Paulo, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: SCIENTIFIC REPORTS; v. 11, n. 1 MAY 12 2021.
• Citações Web of Science: 0

Resumo

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. (AU)

• Processo FAPESP: 11/50761-2 - Modelos e métodos de e-Science para ciências da vida e agrárias
• Beneficiário: Roberto Marcondes Cesar Junior
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 14/50889-7 - INCT 2014: em Medicina Assistida por Computação Científica (INCT-MACC)
• Beneficiário: José Eduardo Krieger
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 20/01992-0 - Sistema computacional de auxílio ao diagnóstico de transtornos psiquiátricos baseado em medidas antropométricas faciais
• Beneficiário: Ariane Machado Lima
• Modalidade de apoio: Auxílio à Pesquisa - Programa eScience e Data Science - Regular