Glaucoma, a leading cause of blindness, requires accurate early detection. We present an AI-based Glaucoma Screening (AI-GS) network comprising six lightweight deep learning models (total size: 110 MB) that analyze fundus images to identify early structural signs such as optic disc cupping, hemorrhages, and nerve fiber layer defects. The segmentation of the optic cup and disc closely matches that of expert ophthalmologists. AI-GS achieved a sensitivity of 0.9352 (95% CI 0.9277–0.9435) at 95% specificity. In real-world testing, sensitivity dropped to 0.5652 (95% CI 0.5218–0.6058) at ~0.9376 specificity (95% CI 0.9174–0.9562) for the standalone binary glaucoma classification model, whereas the full AI-GS network maintained higher sensitivity (0.8053, 95% CI 0.7704–0.8382) with good specificity (0.9112, 95% CI 0.8887–0.9356). The sub-models in AI-GS, with enhanced capabilities in detecting early glaucoma-related structural changes, drive these improvements. With low computational demands and tunable detection parameters, AI-GS promises widespread glaucoma screening, portable device integration, and improved understanding of disease progression.

Vogt-Koyanagi-Harada (VKH) disease after vaccination for coronavirus disease 2019 (COVID-19) is rare, and the prognosis for this condition and its effect on ocular blood flow remains unclear. The purpose of this paper is to present the first case of de novo VKH disease after the second vaccination for COVID-19 with an mRNA vaccine that was successfully treated with systemic steroid therapy and monitored with laser speckle flowgraphy (LSFG). A 30s-year-old Japanese woman reported bilateral decreased visual function 2 weeks after receiving a second dose of the BNT162b2 mRNA vaccine. Best-corrected visual acuity was 20/50 OD and 20/70 OS. Optical coherence tomography revealed bilateral serous retinal detachment over the swollen choroidal layer. LFSG showed decreased blood flow in the peripapillary region of both eyes. This case was diagnosed as complete VKH syndrome, and the patient underwent systemic steroid therapy. After treatment, the retinal detachment disappeared, choroidal thickness decreased, and ocular blood flow increased. Three months later, visual acuity recovered to 20/20 in both eyes. Caution should be exercised in cases with visual symptoms after COVID-19 vaccination, even after more than one dose and more than one week after vaccination. LSFG may be a useful way to measure the ocular blood flow response to treatment and determine the prognosis for VKH disease after COVID-19 vaccination.

Purpose: To investigate clinical factors associated with foveal avascular zone (FAZ) parameters obtained using OCT angiography (OCTA) with assistance from a previously developed artificial intelligence (AI) platform in eyes with open-angle glaucoma (OAG). Design: Retrospective longitudinal. Participants: This study followed up 885 eyes of 558 patients with OAG for >= 2 years; all eyes underwent >= 5 Humphrey visual-field (VF) tests and had 3.0 x 3.0 mm macular OCTA scans available. Methods: Average total deviation (TD) in the superior, superocentral, inferocentral, and inferior sectors of the Humphrey 24-2 program was calculated. We collected 3.0 x 3.0 mm macular OCTA images from each patient and used a previously developed AI platform with these images to obtain FAZ parameters, including FAZ area, FAZ circularity index (CI), and FAZ perimeter. Multivariable linear mixed-effects models were used to analyze the relationship between FAZ parameters, TD or TD slope in each quadrant, and systemic factors, adjusting for potential confounding factors, including axial length. Main Outcome Measures: Ophthalmic and systemic variables, FAZ parameters, and TD or TD slope in each quadrant. Results: The multivariable model showed that FAZ parameters were correlated with both TD and TD slope in the inferocentral quadrant (beta = -0.244 - 0.168, P < 0.001). Both upper-half and lower-half FAZ parameters were better associated with TD-inferocentral and TD-inferocentral slope than TD-superocentral or TD-superocentral slope in terms of beta size and statistical significance, indicating that there was no evident vertical anatomical correspondence between TD in the central quadrant and FAZ parameters. Foveal avascular zone area enlargement was associated with female gender (beta = 0.242, P = 0.003). Loss of FAZ circularity was associated with both aging and comorbid sleep apnea syndrome (SAS) (yes: 1, no: 0) (beta = -0.188, P < 0.001; beta = -0.261, P = 0.031, respectively). Foveal avascular zone perimeter elongation was associated with aging and female gender (beta = 0.084, P = 0.040; beta = 0.168, P = 0.042, respectively). Conclusions: Artificial intelligence-assisted OCTA-measured FAZ enlargement and irregular shape might be good markers of ocular hypoperfusion and associated inferocentral VF defect progression in eyes with OAG.

Background Accurate estimation of the retinal age, defined as the age predicted from fundus photographs by a deep-learning model trained on chronological age, provides a non-invasive biomarker of biological ageing and disease risk. Methods In this study, we trained an ensemble multitask learning model that integrates fundus photographs with glycated haemoglobin using 50,595 quality-controlled fundus photographs from 27,214 disease-free adults and validated it on an independent set of 7288 additional images from disease-free adults. Model performance was evaluated using mean absolute error. Prediction uncertainty was quantified by calculating the standard deviation across ensemble predictions for each eye, and eyes were stratified based on this standard deviation. Results Here we show that the model achieves mean absolute errors of 2.78 years in internal validation and 3.39 years and 8.63 years in two external cohorts comprising 135 and 4992 eyes, respectively. Eyes with ensemble standard deviations below the median demonstrate improved age-prediction accuracy (mean absolute error: 2.46 years internally; 2.87 years in the primary external cohort). In a systemic disease cohort of 8467 individuals, the retinal age gap (predicted minus chronological age) is significantly higher in participants with diabetes, cardiac disease, or stroke after adjustment for age and sex, indicating older-appearing retinas and supporting the biological relevance of retinal age. Conclusions Retinal age derived from a single fundus photograph provides a scalable and clinically deployed biomarker of biological ageing. This approach may enable opportunistic screening for cardiometabolic and other ageing-related diseases in other routine ocular imaging workflows.

Objectives: The objective of this study was to identify novel autoantibodies specific for open-angle glaucoma (OAG), including normal-tension glaucoma (NTG) and primary open-angle glaucoma (POAG), using proteome-wide autoantibody screening and to determine their utility for diagnosis. Methods: We conducted proteome-wide autoantibody screening by wet protein arrays. Autoantibody reactivity in the plasma of OAG patients (50 NTG and 69 POAG patients) was quantitatively analyzed and compared to that of controls (35 cataract patients). The area under the curve (AUC) of the receiver operating characteristic (ROC) and multivariate analyses were used to determine diagnostic potential in patients with OAG. Results: Based on differences in autoantibody titers and positivity rates, four autoantibodies against ETNK1, VMAC, NEXN, and SUN1 were selected as potential biomarkers to discriminate OAG and cataract. In discrimination between POAG and cataract, the AUCs of ETNK1 and VMAC were calculated to be 0.820 (95%CI: 0.733–0.907) and 0.889 (95%CI: 0.818–0.959), respectively. Furthermore, the combination of these four antibodies demonstrated diagnostic potential for OAG with an AUC of 0.828 (95%CI: 0.757–0.898) by multivariate analysis. Conclusions: Four new glaucoma-associated autoantibodies were identified in this study. The differences in autoantibody patterns in the plasma between glaucoma and cataract patients support their potential utility as biomarkers for glaucoma screening.

Artificial intelligence holds the potential to transform ophthalmic assessment, yet its translation into clinical practice remains hindered by its limited ability to perform complex reasoning, utilize specialized tools, or provide transparent justifications. We introduce OphAgent, a multi-agent framework powered by large language models that mimics clinical workflows through dynamic diagnostic planning, knowledge base consultation, and multimodal image interpretation via structured inter-agent debate. Validated across four imaging modalities, 30+ conditions, and 11 diverse real-world datasets, OphAgent achieved 85% average accuracy in retinal diagnosis, surpassing current state-of-the-art multimodal large language models. In a large-scale global reader study with 311 ophthalmologists from 86 centres across 22 countries, spanning seven languages and settings from tertiary hospitals to resource-limited clinics, OphAgent improved diagnostic accuracy by up to 20% across 14 clinically significant tasks. Critically, these gains remained stable across geographic regions, experience levels, and languages, demonstrating robustness, fairness, and generalisability. OphAgent exemplifies an emerging paradigm where medical AI evolves from isolated prediction to collaborative clinical reasoning. By integrating reasoned clinical consultation with state-of-the-art diagnostic performance, the framework offers a scalable pathway toward more equitable, transparent, and trustworthy AI-enabled clinical decision support, with the potential to extend expert-level ophthalmic assessment across diverse healthcare settings.