Peer-reviewed article
Visual System Alterations for Identifying Teacher-Reported Academic Difficulties in Schoolchildren: A Machine Learning Analysis.
González-Jiménez R, Trillo JR, Bernárdez-Vilaboa R, Cedrún-Sánchez JE, Otero-Currás C et al. ·
Summary
Written by AI, not yet AICS's view · claude-haiku-4-5-20251001This study used machine learning to analyze whether visual system measurements could predict teacher-reported academic difficulties in 506 schoolchildren. The researchers tested five different visual domains, including eye movement control and accommodation (focusing ability). Accommodation and eye movement control showed the strongest ability to identify children with academic difficulties, while other visual measures performed less well.
Key points
- Accommodative system (eye focusing) had the highest predictive accuracy at 95.2%
- Clinical and instrumental oculomotor (eye movement) assessments showed strong performance
- Vergence system alterations had high specificity but very low sensitivity
- Abnormal axial length (eye size) showed limited discriminative value
- Authors note findings are exploratory and require external validation before clinical use
Limitations
The study does not indicate which country or countries the research was conducted in. The document is exploratory and does not yet provide validated clinical recommendations, requiring further validation with external cohorts and standardized academic outcome measures.
Abstract
Background/objectives Efficient visual processing is relevant for reading, writing, and sustained attention in schoolchildren. However, the relative discriminative value of different visual domains for identifying teacher-reported academic difficulties remains unclear. This study evaluated five visual system alteration domains for identifying teacher-reported academic difficulties using machine learning models. Methods An observational analytical study was conducted in 581 primary schoolchildren. After complete-case analysis, 506 participants were included in the machine-learning analyses. Academic functioning was rated by teachers using a 1-5 ordinal scale and dichotomized as a pragmatic school-based indicator of teacher-reported academic difficulties. Five predictor groups were analyzed: DIVE-based oculomotor function, clinical oculomotor assessment, accommodative system, vergence system, and axial length. Five classifiers were evaluated using stratified 5-fold cross-validation combined with model-complexity penalization through hyperparameter optimization. Results The accommodative system showed the highest cross-validated performance (XGBoost: accuracy 0.952 ± 0.021; macro-F1 0.919 ± 0.028), followed by clinically assessed and instrumentally assessed oculomotor predictors. Oculomotor alterations also showed strong performance, whereas vergence alterations showed high specificity but very low sensitivity, and abnormal axial length showed limited discriminative performance. Conclusions Functional visual domains, particularly accommodation and oculomotor control, showed stronger cross-validated classification performance than vergence or axial-length variables. These findings are exploratory and require external validation cohorts, standardized academic outcomes, and future combined-domain modeling before clinical or educational implementation.