Using explainable machine learning and eye-tracking for diagnosing autism spectrum and developmental language disorders in social attention tasks
These findings highlight the potential of applying XML techniques to eye-tracking data collected through tasks designed to capture features characteristic of neurodevelopmental conditions. They also underscore the clinical relevance of such approaches for identifying the variables and parameters that differentiate between disorders.
Authors
- José J. Cañas (ORCID: https://orcid.org/0000-0002-0666-2123)
- Francisco J. Rodriguez‐Lozano (ORCID: https://orcid.org/0000-0003-3560-1407)
- Julia Vacas (ORCID: https://orcid.org/0000-0002-9836-3648)
- Carolina Pérez‐Dueñas (ORCID: https://orcid.org/0000-0002-9991-2733)
- Araceli Sánchez-Raya (ORCID: https://orcid.org/0000-0001-6264-3456)
- Juan Carlos Valenzuela Gámez (ORCID: https://orcid.org/0000-0002-9325-7408)
- Adoración Antolí (ORCID: https://orcid.org/0000-0001-7259-5080)
- Fátima Cuadrado (ORCID: https://orcid.org/0000-0003-1799-8236)
Institutions
- Universidad de Granada (ES)
- Instituto Maimónides de Investigación Biomédica de Córdoba (ES)
- University of Córdoba (ES)
Publication Details
- Journal
- Frontiers in Neuroscience
- Published
- 2025-06-24
- DOI
- https://doi.org/10.3389/fnins.2025.1558621
- Citations
- 2
- Primary Topic
- Autism Spectrum Disorder Research
- Type
- article
- Field-Weighted Citation Impact
- 2.86
Funders
- Ministerio de Ciencia e Innovación
- Universidad de Córdoba