Machine Learning-Based Prediction of Strabismus with and without Amblyopia from Fixation Eye Movements.

Objective: To evaluate whether machine learning (ML) models trained on fixation eye movement (FEM) features can differentiate strabismic amblyopia, strabismus without amblyopia, and visually normal controls. Design: Cross-sectional study. Participants: A total of 159 subjects were recruited from a single Cleveland Clinic site: 49 with moderate/severe strabismic amblyopia, 24 with treated/mild strabismic amblyopia, 47 with strabismus without amblyopia, and 39 controls. Methods: Fixation eye movements-horizontal and vertical eye-position traces from both eyes-were recorded during monocular viewing: fellow-eye and amblyopic-eye viewing for amblyopic subjects and right-eye and left-eye viewing for strabismic subjects without amblyopia and controls. Fixation and vergence instability, fast and slow FEM amplitude and velocity, fast-FEM frequency, and time-based eye deviation control metrics were computed using density-based clustering scans. Features were categorized as monocular, interocular within-viewing (nonviewing vs. viewing eye), interocular across-viewing (comparing the viewing eye across fellow-eye viewing vs. amblyopic-eye viewing or right-eye viewing vs. left-eye viewing), and binocular-coordination metrics (vergence instability and eye deviation measures). Six binary classification tasks were evaluated and trained using multiple ML models. Shapley Additive exPlanations (SHAP) identified the most predictive features. Main Outcome Measures: Accuracy, area under the receiver operating characteristic curve (AUROC), and SHAP-derived feature importance for each classification task. Results: Classification was strongest in distinguishing moderate/severe disease: amblyopia versus strabismus: accuracy 0.81 ± 0.09, AUROC 0.85 ± 0.09; strabismus versus controls: accuracy 0.90 ± 0.07, AUROC 0.92 ± 0.07; amblyopia versus controls: accuracy 0.90 ± 0.07, AUROC 0.97 ± 0.03. Performance was reduced for treated/mild amblyopia versus strabismus (accuracy 0.70 ± 0.12, AUROC 0.60 ± 0.19) but remained strong for treated/mild amblyopia versus controls (accuracy 0.94 ± 0.06, AUROC 0.87 ± 0.13). Classification of all amblyopia and strabismus combined versus controls achieved an accuracy of 0.89 ± 0.05 and AUROC 0.93 ± 0.04. Shapley Additive exPlanations analysis showed that abnormalities in the viewing amblyopic eye relative to the viewing fellow eye were the strongest predictors of amblyopia, whereas nonviewing-eye FEM metrics and binocular-coordination features best predicted strabismus with or without amblyopia compared with controls. Conclusions: This proof-of-concept study demonstrates that FEM-derived features combined with ML can objectively distinguish strabismic amblyopia from strabismus and controls, with strongest performance in moderate/severe disease. Future work will extend validation to younger populations and incorporate portable eye-tracking devices to support scalable, point-of-care screening. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.