PURPOSE: Keratoconus (KC) is bilateral noninflammatory corneal disorder characterized by progressive corneal thinning, protrusion, and scarring. The purpose of this study was to evaluate ganglion cell complex(GCC), macula thickness(MT) and optic head disc parameters in keratoconus patients. METHODS: A hospital based prospective clinical case series was performed in Inonu University School of Medicine. 52 eyes of 52 keratoconus patients and 50 eyes of 50 normal patients were enrolled. RESULTS: There is no statistically significant in MT between groups. GCC in nasal superior, temporal superior and temporal inferior 9 mm from macula were found statistically significant decrease in keratoconus group (p<0,05). In optic disc analysis fifth and the eleventh clock-hour quadrants of peripapiller retina nerve fiber layer and cup area ratio were found statistically significant decrease in keratoconic eyes (p<0,05). CONCLUSION: We thought that structural retinal changes seem in keratoconus eyes; keratoconus pathogenesis may affect not only cornea but also retina and optic nerve head.

To assess the different clinical findings and symptoms of patients with optic neuritis (ON) and uveitis, who were diagnosed with syphilis.The medical records of patients, who had ocular involvement...

Background/Objectives: Subjective refraction is the clinical gold standard but is time-consuming and examiner-dependent. Most artificial intelligence (AI)-based approaches rely on specialized imaging or biometric data not routinely available. This study aimed to predict subjective refraction using only routine, non-cycloplegic autorefraction and keratometric data and to identify factors associated with reduced prediction accuracy. Methods: This retrospective study included 1856 eyes from 1006 patients. A multi-output histogram gradient-boosting model predicted subjective spherical equivalent, cylindrical power, and astigmatic axis. Performance was evaluated on an independent test dataset using R² and mean absolute error, with circular statistics for axis prediction. Prediction failure was assessed using clinically relevant tolerance thresholds (sphere/cylinder ≤ 0.50 D; axis ≤ 10°) and multivariable logistic regression. Results: The model achieved high accuracy for spherical and cylindrical prediction (R² = 0.987 and 0.933; MAE = 0.126 D and 0.137 D). Astigmatic axis prediction demonstrated strong circular agreement (ρ = 0.898), with a mean absolute angular error of 4.65° (median, 0.96°). Axis errors were higher in eyes with low cylinder magnitude (<0.75 D) and oblique astigmatism. In multivariable analysis, steeper keratometry (K2; OR = 7.25, 95% CI 1.62–32.46, p = 0.010) and greater objective cylindrical power (OR = 2.79, 95% CI 1.87–8.94, p = 0.032) were independently associated with poor prediction. Conclusions: A machine-learning model based solely on routine, non-cycloplegic autorefractor and keratometric measurements can accurately estimate subjective refraction, supporting AI as a complementary decision-support tool rather than a replacement for conventional subjective refraction.

Prevalence of Retinal Vascular Diseases in a Tertiary Care Hospital in Türkiye: A Hospital-Based Epidemiologic Study - Turkish Journal of Ophthalmology