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Related Concept Videos

Learning Disabilities01:25

Learning Disabilities

Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
Dyslexia
Dyslexia is a...

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Updated: Jun 27, 2026

An Automated Method for Assessing Visual Acuity in Infants and Toddlers Using an Eye-Tracking System
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Visual System Alterations for Identifying Teacher-Reported Academic Difficulties in Schoolchildren: A Machine

Rut González-Jiménez1, José Ramón Trillo2, Ricardo Bernárdez-Vilaboa1

  • 1Optometry and Vision Department, Faculty of Optics and Optometry, Complutense University of Madrid, 28037 Madrid, Spain.

Children (Basel, Switzerland)
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

The accommodative and oculomotor systems are key indicators for identifying academic difficulties in schoolchildren. Machine learning models effectively identified these visual processing issues, offering potential for early intervention.

Keywords:
accommodationmachine learningoculomotor dysfunctionpediatric visionschool screeningschoolchildrenteacher-reported academic difficultiesvisual function

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Published on: September 27, 2020

Area of Science:

  • Ophthalmology
  • Educational Psychology
  • Machine Learning

Background:

  • Efficient visual processing is crucial for academic success in reading, writing, and attention.
  • The specific visual domains most predictive of academic difficulties are not well-established.
  • Teacher-reported academic difficulties are a significant concern in primary education.

Purpose of the Study:

  • To evaluate five visual system domains for their ability to predict teacher-reported academic difficulties in schoolchildren.
  • To compare the discriminative value of oculomotor function, accommodation, vergence, and axial length using machine learning.

Main Methods:

  • An observational study included 506 primary schoolchildren.
  • Academic functioning was teacher-rated and dichotomized to indicate academic difficulties.
  • Machine learning classifiers were trained and validated using 5-fold cross-validation to assess five visual predictor groups.

Main Results:

  • The accommodative system demonstrated the highest predictive performance (XGBoost: accuracy 0.952).
  • Oculomotor function (clinical and instrumental assessment) also showed strong predictive capabilities.
  • Vergence and axial length showed limited discriminative value for academic difficulties.

Conclusions:

  • Functional visual domains, especially accommodation and oculomotor control, are strong predictors of academic difficulties.
  • These findings highlight the potential of visual assessments in identifying children at risk for academic challenges.
  • Further validation with larger cohorts and standardized outcomes is necessary for clinical application.