Advanced search
Start date
Betweenand
Related content
(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

Full text
Author(s):
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]
Total Authors: 8
Affiliation:
[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
Total Affiliations: 3
Document type: Journal article
Source: SCIENTIFIC REPORTS; v. 11, n. 1 MAY 12 2021.
Web of Science Citations: 0
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. (AU)

FAPESP's process: 11/50761-2 - Models and methods of e-Science for life and agricultural sciences
Grantee:Roberto Marcondes Cesar Junior
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 14/50889-7 - National Institute of Science and Technology Medicine Assisted by Scientific Computing (INCT-MACC)
Grantee:José Eduardo Krieger
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 20/01992-0 - Computer aid system for the diagnosis of psychiatric disorders based on facial anthropometric measurements
Grantee:Ariane Machado Lima
Support Opportunities: Research Grants - eScience and Data Science Program - Regular Program Grants