ArticleScientific reports2021
Computer-aided autism diagnosis based on visual attention models using eye tracking.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it, 63 citations in OpenAlex.
- The Diagnostic Value of Image-Based Machine Learning for Osteoporosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- A bibliometric analysis of research trends of artificial intelligence in the treatment of autistic spectrum disorders.Frontiers in psychiatry · 2022Pooled it
- Improving Autism Diagnosis Across Ages Using Eye-Tracking and Temporal Transformer Models.Sensors (Basel, Switzerland) · 2026Article
- Home-based eye tracking for early autism screening: a scoping review of approaches, evidence, and implementation challenges.BMC psychiatry · 2026Article
- ODDM: Integration of SMOTE Tomek with Deep Learning on Imbalanced Color Fundus Images for Classification of Several Ocular Diseases.Journal of imaging · 2025Article
- Article
- Modified Meta Heuristic BAT with ML Classifiers for Detection of Autism Spectrum Disorder.Biomolecules · 2023Article
- Computer-aided autism diagnosis using visual attention models and eye-tracking: replication and improvement proposal.BMC medical informatics and decision making · 2023Article
- A Review of and Roadmap for Data Science and Machine Learning for the Neuropsychiatric Phenotype of Autism.Annual review of biomedical data science · 2023Review
- Pediatricians' focus of sight at pain assessment during a neonatal heel puncture.Revista paulista de pediatria : orgao oficial da Sociedade de Pediatria de Sao Paulo · 2023Article
- Global trends and hotspots in the digital therapeutics of autism spectrum disorders: a bibliometric analysis from 2002 to 2022.Frontiers in psychiatry · 2023Article
- Investigation of Eye-Tracking Scan Path as a Biomarker for Autism Screening Using Machine Learning Algorithms.Diagnostics (Basel, Switzerland) · 2022Article
- Zebrafish Modeling of Autism Spectrum Disorders, Current Status and Future Prospective.Frontiers in psychiatry · 2022Review
- Combining Implicit and Explicit Feature Extraction for Eye Tracking: Attention Classification Using a Heterogeneous Input.Sensors (Basel, Switzerland) · 2021Article
- Application of Eye Tracking Technology in Medicine: A Bibliometric Analysis.Vision (Basel, Switzerland) · 2021Article
- Classification of Children With Autism and Typical Development Using Eye-Tracking Data From Face-to-Face Conversations: Machine Learning Model Development and Performance Evaluation.Journal of medical Internet research · 2021Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.