Abstract
Glaucoma remains a leading cause of irreversible blindness worldwide, with early detection crucial for preventing vision loss. This study developed and validated a novel eye-tracking algorithm to detect oculomotor abnormalities in primary open-angle glaucoma (POAG). We conducted a case-control study (March-June 2021), recruiting 16 patients with moderate POAG, 16 with preperimetric POAG, and 16 age-matched controls. The participants underwent a comprehensive ophthalmic examination and eye movement recording using a high-resolution infrared tracker during two tasks: saccades to static targets and saccades to moving targets. The patients with POAG exhibited a significantly increased saccadic latency and reduced accuracy compared to the controls, with more pronounced differences in the moving target task. Notably, preperimetric POAG patients showed significant abnormalities despite having normal visual fields based on standard perimetry. Our machine learning algorithm incorporating multiple saccadic parameters achieved an excellent discriminative ability between glaucomatous and healthy subjects (AUC = 0.92), with particularly strong performance for moderate POAG (AUC = 0.97) and good performance for preperimetric POAG (AUC = 0.87). These findings suggest that eye movement analysis may serve as a sensitive biomarker for early glaucomatous damage, potentially enabling earlier intervention and improved visual outcomes.