Background: An advanced age and the female sex are widely recognized risk factors for both cataract and dementia. We investigated the effect of cataract surgery on the incidence of dementia in a Korean population aged ≥ 45 years with a previous diagnosis of cataract. Methods: This nationwide cohort study was performed using Korean National Health Insurance Service data collected from 2006 to 2017. A total of 300,327 subjects aged ≥ 45 years with a history of cataract diagnosis but no previous diagnosis of dementia were analyzed. The relationship between cataract surgery and dementia was evaluated, applying a time-varying analysis to evaluate the hazard ratio (HR) and 95% confidence interval (CI) values according to dementia. It was calculated via a multivariable Cox regression model, with adjustments for age, sex, visual acuity (VA), ocular and systemic comorbidities, and social factors (including body mass index, income, smoking, and drinking). Results: In the multivariate analysis, the cataract surgery group showed a marginal difference in dementia development (HR 1.10 [95% CI 1.02–1.19]) because both cataract and dementia share common risk factors. However, in the subgroup analysis, men (HR 0.49 [95% CI 0.26–0.90]) and patients under 65 years of age (HR 0.88 [95% CI 0.79–0.99]) in the group with cataract surgery and good VA showed a significantly lower incidence of dementia. Conclusion: Through visual improvement, together with timely surgical intervention, the procedure can alleviate the risk of dementia in visually impaired patients, especially in younger and male patients.

Background: Optic disc tilt is a morphological change in myopic eyes that complicates clinical interpretation and artificial intelligence (AI)–based analysis of fundus images. Accurate detection of optic disc tilt is necessary to avoid misinterpretation of disc morphology and enhance diagnostic reliability across different disease types. Objective: This study developed and externally validated an end-to-end AI-based pipeline for optic disc segmentation and quantitative tilt classification in color fundus photographs (CFPs), offering an objective alternative to manual segmentation and subjective clinical assessments. Methods: We trained a nnU-Net–based optic disc segmentation model on the Standardized Multi-Channel Dataset for Glaucoma (SMDG; n=3103 CFPs) and externally validated it on the Samsung Medical Center (SMC) dataset (n=2448 CFPs from n=1370 patients). Model generalizability was assessed using both a fixed 80:20 random split and 5-fold cross-validation. Tilt was classified using the ratio of the long-axis diameter to the short-axis diameter, with a ratio of ≥1.3 indicating tilt. Segmentation performance was evaluated using the Dice similarity coefficient, intersection over union, and pixel accuracy on the SMDG dataset and using the clinical acceptance rate determined by 2 independent ophthalmologists on the external SMC dataset. Results: Using the SMDG dataset, nnU-Net achieved consistently high performance, with mean Dice similarity coefficients of 0.956 (SD 0.042) across 5-fold cross-validation and 0.961 (SD 0.055) for the best-performing single-fold model across 8 datasets. On the SMC dataset, 2 independent expert reviews yielded mean clinical acceptance rates of 98.61% and 98.86% across disease types, with acceptance rates ranging from 81.63% and 93.88% for edema to 99.59% and 99.17% for pallor, respectively. Tilt was detected in 7.5% (186/2448) of images, with rates of 9.7% (118/1215) for normal images, 3.9% (35/894) for glaucoma, 7.8% (19/241) for pallor, and 14.2% (14/98) for edema. Segmentation errors were observed in 1.39% (34/2448) and 1.14% (28/2448) of cases by the 2 reviewers, mainly due to edema-related swelling, peripapillary atrophy, and vessel confusion. Conclusions: Our pipeline provides objective and reproducible detection of optic disc tilt in CFPs, with strong generalizability to clinical images. By replacing manual segmentation and subjective assessments, the pipeline supports tilt-aware AI diagnostics and scalable screening for myopia-related conditions, with future refinements needed to address edema-related challenges.

BACKGROUND Optic disc tilt is a morphological change in myopic eyes that complicates clinical interpretation and artificial intelligence (AI)-based analysis of fundus images. Accurate detection of optic disc tilt is necessary to avoid misinterpretation of disc morphology and enhance diagnostic reliability across different disease types. OBJECTIVE This study developed and externally validated an end-to-end AI-based pipeline for optic disc segmentation and quantitative tilt classification in color fundus photographs (CFPs), offering an objective alternative to manual segmentation and subjective clinical assessments. METHODS We trained a nnU-Net-based optic disc segmentation model on the Standardized Multi-Channel Dataset for Glaucoma (SMDG; 3,103 CFPs) and externally validated it on the Samsung Medical Center dataset (2,448 CFPs; 1,958 patients). Tilt was classified using the ratio of the long-distance diameter to short-distance diameter, with a ratio ≥ 1.3 indicating tilt. Segmentation performance was evaluated using the dice similarity coefficient (DSC), intersection over union (IoU), and pixel accuracy on the SMDG and the clinical acceptance rate via expert review. RESULTS Using the SMDG, nnU-Net achieved outstanding performance (mean ± SD: DSC, 0.961 ± 0.055; IoU, 0.927 ± 0.057) across eight datasets. With the SMC dataset, expert review showed a mean clinical acceptance rate of 98.61% across disease types, ranging from 86.40% (edema) to 99.59% (pallor). Tilt was detected in 7.5% (186/2,448) of images, with rates of 9.7% (normal), 3.9% (glaucoma), 7.8% (pallor), and 14.2% (edema). Segmentation errors occurred in 1.4% (34/2,448) of cases, mainly due to edema-related swelling, peripapillary atrophy, and vessel confusion. CONCLUSIONS Our pipeline provides objective and reproducible detection of optic disc tilt on CFPs, with strong generalization to clinical images. Replacing manual segmentation and subjective assessments, the pipeline supports tilt-aware AI diagnostics and scalable screening for myopia-related conditions, with future refinements needed for edema-related challenges.