Ophthalmology Science

BackgroundRetinopathy of prematurity (ROP) is a leading cause of preventable blindness among preterm infants. Accurate retinal vessel segmentation is crucial for detecting plus disease, which indicates progression to severe ROP. However, manual annotation of vessel masks is laborious and inconsistent, especially in low-resource clinical settings. This study aimed to evaluate a self-supervised vessel extraction pipeline using Frangi-Hessian filtering for automatic pseudo-annotation of unlabeled RetCam and Neo retinal images and to compare its performance against supervised and hybrid deep learning frameworks. MethodsTwo public datasets from the HVDROPDB-BV repository: RetCam_Vessels and Neo_Vessels were utilized. We implemented a three-stage pipeline: automatic self-annotation of unlabeled images through vessel-based mask generation; training of five segmentation architectures--BioSwinFuseNet, UNet, FPN, LinkNet, and SegFormer--under three regimes (GT-only, Self-only, and Hybrid GT+Self); and evaluation using Dice, IoU, sensitivity, specificity, PPV, NPV, F1, and AUC metrics. All models were trained with a topology-aware loss that combined binary cross-entropy and Dice losses with continuity penalties. ResultsHybrid supervision consistently outperformed both GT-only and Self-only training across all architectures. The SegFormer-Hybrid model achieved the highest Dice (0.61) and IoU (0.44), while FPN-Hybrid demonstrated the lowest variance. BioSwinFuseNet-Hybrid showed a 122% relative improvement in Dice compared to its GT-only version. Self-only models learned rudimentary vessel priors but lacked clinical precision. ConclusionsIncorporating self-annotated masks alongside limited ground truth improves segmentation accuracy and vessel continuity. The hybrid paradigm offers a scalable path for developing automated ROP screening tools where expert labeling is limited.

PurposeAnti-vascular endothelial growth factor (anti-VEGF) therapy is the standard of care for neovascular age-related macular degeneration (AMD), yet many patients exhibit persistent retinal degeneration, fibrosis, and incomplete therapeutic response. The molecular pathways underlying this incomplete response remain poorly understood. We sought to identify VEGF-independent signaling pathways active in the vitreous of anti-VEGF-treated AMD patients. MethodsWe performed multiplex antibody-based proteomic profiling of 1,000 human proteins in vitreous samples from patients with neovascular AMD receiving anti-VEGF therapy (n=8) and comparative controls (n=6). Differential protein expression was assessed using one-way ANOVA, followed by gene ontology and pathway enrichment analyses. Drug-target relationships were evaluated to identify potential opportunities for therapeutic repositioning. ResultsWe identified 107 differentially expressed proteins (p<0.05), including key regulators of immune signaling, angiogenesis, and metabolism. Notably, multiple components of cytotoxic lymphocyte pathways were dysregulated, including IL-21R, SIGLEC-7, CTLA4, and IL-2-associated signaling. Enrichment analyses revealed significant activation of pathways related to T-cell activation, interleukin signaling, and leukocyte-mediated cytotoxicity. These immune signatures persisted despite suppression of VEGF signaling. Several clinically available immunomodulatory agents--including abatacept, sirolimus, and dupilumab--targeted pathways identified in this dataset. ConclusionsAnti-VEGF-treated neovascular AMD exhibits persistent cytotoxic immune signaling in the vitreous, suggesting that VEGF-independent immune mechanisms may contribute to ongoing retinal damage and incomplete therapeutic response. These findings provide a rationale for combination therapeutic strategies targeting both angiogenic and immune pathways in AMD.

PurposeThere is a need for novel therapies for diabetic retinopathy (DR) because existing therapies treat only certain features of DR and do not work optimally for all patients. While proteomic studies provide insight into disease pathobiology, they are often limited to small sample sizes due to high costs, limiting their generalizability and reproducibility. Moreover, they often yield lists of tens to hundreds of proteins with differential expression, making it difficult to prioritize the most biologically relevant biomarkers beyond using arbitrary fold-change and false-detection rate cutoffs. Here, we applied a two-stage multimodal AI approach: first, we integrated EHR and proteomics data to rationally prioritize candidate protein biomarkers and, next, validated these biomarkers in an independent cohort. These protein biomarkers of DR are rooted in the EHR data and thereby more likely to be biological drivers of disease. MethodsWe obtained EHR data from a large number of patients with and without DR (N=319,997) from the STARR-OMOP database and obtained aqueous humor liquid biopsies from a subset of these patients (N=101) for high-resolution proteomic profiling. We developed Clinical and Omics Multi-Modal Analysis Enhanced with Transfer Learning (COMET) to perform integrated analysis of proteomics and all available EHR data to identify protein biomarkers of DR. The model was trained in two phases: first, it was pretrained using patients with EHR data alone (N=319,896), and then, it was fine tuned using patients with both EHR and proteomics data (N=101), allowing it to learn both clinical and molecular features associated with DR. Findings from COMET were then validated with liquid biopsies from an independent, validation cohort (N=164). Resultst-distributed stochastic neighbor embedding (t-SNE) analysis of EHR and proteomics data identified proteins clustering with related EHR features. Levels of STX3 and NOTCH2, proteins involved in retinal function, were correlated with a diagnosis of macular edema, a record of a visual field exam, and a prescription for latanoprost, highlighting protein-EHR alignment. The pretrained, multimodal COMET model was superior (AUROC=0.98, AUPRC=0.91) compared to models generated using either EHR or proteomics data alone or without pretraining (AUROC: 0.76 to 0.92; AUPRC: 0.47 to 0.74). The proteins SERPINE1, QPCT, AKR1C2, IL2RB, and SRSF6 were prioritized by the COMET model compared to the models without pretraining, supporting their potential role in DR pathobiology, and were subsequently validated in an independent cohort. ConclusionWe used multimodal AI to prioritize protein biomarkers of DR that are most strongly linked to EHR elements, as well as identifying other protein biomarkers associated with disease features like diabetic macular edema. These findings serve as a foundation for future mechanistic studies and highlight the synergistic value of using multimodal AI to fuse EHR and proteomics data for enhanced proteomics analysis.

PurposeTimely detection of disease activity in chronic retinal diseases improves visual outcomes but is limited by the lack of validated systems for continuous monitoring and care management. We evaluated the real-world performance of an integrated remote physiologic monitoring and principal care management program (RemoniHealth(R)) using a self-administered multimodal retinal function test (Macustat(R)) for home monitoring. MethodsThis single-arm real-world intervention study was conducted across 33 retina practices. A total of 2,216 adults with chronic retinal diseases performed weekly home retinal function testing with integrated care management support. Primary endpoints included the annualized rate of disease progression detection, time to intervention after first flag, true positive rate, and patient adherence. Descriptive statistics and data analyses were analyzed using chi-square tests and Clopper-Pearson confidence intervals. ResultsParticipants contributed 82,644 encounters and 16,805 patient-months of monitoring. The program generated 241 alerts, including 101 Macustat flags and 135 care management prompts. Among 73 adjudicated flags, 56 were true positives and 17 false positives (PPV 76.7%). The annualized detection rate was 4 per 100 patient-years. Of confirmed events, 93% led to intravitreal injection or other major management change. Mean adherence was 72.1%, and patients with [&ge;]80% adherence had higher odds of true positivity. DiscussionThis RPM-PCM model achieved high engagement and meaningful detection of asymptomatic progression between visits, supporting the value of home monitoring for timely intervention. Translational RelevanceThese findings support scalable integration of home vision testing and care management into routine retinal practice to enable earlier intervention and improved continuity of care.

After rejection, resubmission of scientific manuscripts often requires substantial journal-specific reformatting. We compared systematic review submission policies across high-impact ophthalmology journals and quantified policy similarity to support resubmission planning. We identified the top 50 ophthalmology journals by SCImago Journal Rank that publish systematic reviews and are not invite-only, extracted policies from author instructions using an a priori data dictionary, and computed pairwise similarity on a 0 to 1 scale using the Gower coefficient across mixed policy variables with available-case denominators for unstated fields. Policies were heterogeneous and frequently unstated. Only 29 of 50 journals (58%) stated a main-text word limit; among journals with numeric limits, the median was 4000 words (interquartile range 3500 to 5500; n = 23). Preferred Reporting Items for Systematic Reviews and Meta-Analyses compliance was explicitly required by 35 of 50 journals (70%), and prospective registration by 6 of 50 journals (12%). Across 1225 journal pairs, similarity was modest, with a median of 0.64 (interquartile range 0.57 to 0.71; range 0.05 to 0.98). Similarity among the top 5 highest-ranking journals ranged from 0.62 to 0.90 (median 0.75). Systematic review submission policies vary widely across high-impact ophthalmology journals, and most journal pairs show only modest similarity. Similarity-based guidance may help identify policy-aligned resubmission targets while anticipating common sources of reformatting burden.

Purpose: To develop and evaluate deep learning models for automated detection of corneal perforation in microbial keratitis using anterior segment optical coherence tomography (ASOCT) images. Methods: We enrolled 150 patients with microbiologically confirmed keratitis. Contralateral healthy eyes served as controls. Four convolutional neural network models using ResNet architecture were trained and evaluated using ASOCT images to classify the presence or absence of corneal perforation at the eye level. Ground truth labels for perforation were established following consensus grading by two masked ophthalmologist graders. Models differed in inclusion of healthy controls and masking of non-corneal anterior segment anatomy. Results: The best-performing model (Model 1), which included healthy controls and randomly applied masking of the inferior image portion during training, achieved an AUC of 0.965 (95% CI, 0.911-0.995), sensitivity of 84.0% (95% CI, 70.0%-97.1%), and specificity of 97.8% (95% CI, 96.1%-99.3%) for detection of corneal perforation. Models including healthy controls outperformed those without, and lens masking improved discrimination. Conclusions: Deep learning models achieved high diagnostic accuracy for detecting corneal perforation on ASOCT imaging in eyes with microbial keratitis. These findings support the potential role of automated ASOCT analysis as a clinical decision support tool for identifying this vision-threatening complication.

Automated glaucoma screening from optical coherence tomography (OCT) faces two persistent challenges: scarcity of expert-labeled data and unreliable model predictions on diagnostically ambiguous cases. We present a two-tier diagnostic pipeline that addresses both. In the first tier, an EfficientNetV2-S classifier trained under a semi-supervised pseudo supervisor framework achieves 0.84 AUC on 150 held-out test patients from the Harvard Glaucoma Detection and Progression dataset, using only 350 labeled training samples out of 700. In the second tier, 124 flagged cases are routed to a multi-agent system built on MedGemma 4B, where three specialist agents deliberate over three rounds before rendering a final diagnosis. On these flagged cases, the agent system achieves 100% sensitivity--detecting all 55 glaucoma cases with zero missed diagnoses--and 89.5% overall accuracy (111/124), compared to the classifiers 73.4% (91/124). Uncertainty analysis confirms that the classifiers output probability reliably separates confident predictions (96.3% accuracy, n = 27) from uncertain ones (74.0%, n = 123), producing a 22-percentage-point gap that serves as a triage signal. The agents fix 32 cases the classifier misclassifies while introducing 12 new errors, yielding a net improvement of 20 cases. These results are from a single training run without variance estimates and should be interpreted as preliminary evidence that uncertainty-gated routing to vision-language model agents can meaningfully improve diagnostic accuracy on the cases where automated classifiers are least reliable.

IntroductionAnti-vascular endothelial growth factor (anti-VEGF) therapies are standards of care for vision-threatening retinal diseases. This retrospective observational study describes demographics, utilization, best recorded visual acuity (BRVA), and safety among eyes with neovascular age-related macular degeneration (nAMD), diabetic retinopathy (DR), diabetic macular edema (DME), or retinal vein occlusion (RVO) treated with the biosimilar aflibercept-ayyh (PAVBLU(R)) in routine clinical practice. MethodsElectronic medical records from the Retina Consultants of America database of patients receiving aflibercept-ayyh (12/1/2024-10/31/2025) were analyzed, focusing on eyes with [&ge;]84 days of follow-up. The index date was the first documented aflibercept-ayyh injection. Postindex data were used to assess treatment patterns, BRVA (Wilcoxon signed rank test), and adverse events of special interest (AESIs). ResultsA total of 1,000 consecutive eyes from 989 patients received 3,730 injections of aflibercept-ayyh; most (91%) switched from prior anti-VEGF therapy and 9% were anti-VEGF treatment-naive. Disease distribution was 58% nAMD, 19% RVO, 16% DME, and 7% DR. Among switchers, median (IQR) number of prior injections was 21 (8-46). Median (IQR) follow-up was 6.0 months (4.6-7.1). Median (IQR) number of aflibercept-ayyh injections per eye was 4 (3-5). Among eyes with [&ge;]84 days of follow-up (n=889), mean BRVA expressed as logarithm of minimum angle of resolution (logMAR) remained stable for switchers (0.4 to 0.4; P=0.96) and improved from baseline in anti-VEGF-naive eyes (0.5 to 0.4; P<0.01). Confirmed AESIs included iritis (n=2; 0.05% of injections), with no events of vitreous cells, endophthalmitis, retinal detachment, retinal vasculitis, or vitreous hemorrhage. ConclusionIn this descriptive real-world analysis, aflibercept-ayyh was associated with stable visual acuity in previously treated eyes and vision improvement in treatment-naive eyes, with no new or unexpected safety findings, consistent with expectations for aflibercept. These findings add real-world experience to preexisting evidence demonstrating no clinically meaningful differences between aflibercept-ayyh (PAVBLU(R)) and reference aflibercept (EYLEA(R)). KEY SUMMARY POINTSO_ST_ABSWhy carry out this study?C_ST_ABSO_LIThe anti-vascular endothelial growth factor (VEGF) drug aflibercept, approved in 2011 and marketed in the United States as EYLEA(R),* has demonstrated efficacy in treating retinal diseases such as neovascular age-related macular degeneration (nAMD), diabetic retinopathy (DR), diabetic macular edema (DME), or retinal vein occlusion (RVO) and is a standard of care for these disorders. C_LIO_LIAflibercept-ayyh is a biosimilar to aflibercept that has demonstrated comparable efficacy and safety in the treatment of nAMD in a randomized controlled clinical trial. C_LIO_LIThis study describes the real-world use patterns, vision outcomes, and safety of aflibercept-ayyh in clinical settings in the United States for the treatment of nAMD, DR, DME, and RVO. C_LI What was learned from the study?O_LIIn this real-world study of 1,000 consecutive eyes treated with the biosimilar aflibercept-ayyh in patients with retinal diseases, we observed no new safety concerns and that aflibercept-ayyh maintained visual acuity in eyes switching anti-VEGF agents and improved vision in anti-VEGF-naive eyes, consistent with expected responses to aflibercept. C_LIO_LIThese findings support aflibercept-ayyh as a suitable treatment option when anti-VEGF therapy is indicated. *EYLEA(R) is a registered trademark of Regeneron Pharmaceuticals, Inc. PAVBLU(R) is a registered trademark of Amgen Inc. C_LI

Background and Objective As optical coherence tomography (OCT) has enabled the identification of an expanding set of age related macular degeneration (AMD) risk biomarkers and become central to routine clinical practice, there remains a need for a simplified grading scheme that allows physicians to communicate and synchronize AMD grading directly from standard OCT imaging rather than relying on traditional color fundus imaging. This study aims to establish a standardized OCT based AMD classification that balances diagnostic accuracy with practicality for use across clinical and research settings. Patients and Methods Spectral domain optical coherence tomography scans were independently graded by two retinal specialists following the newly proposed Stanford OCT Based AMD Classification (SOAC). Discrepancies were adjudicated by a third independent retinal specialist. Intergrader agreement was assessed using weighted kappa coefficients. Results Among the 109 eyes from 108 patients, AMD staging based on SOAC was distributed as follows: normal aging in 9 patients (8.3%), early AMD in 16 (14.7%), intermediate AMD in 32 (29.4%), neovascular AMD (nAMD) in 18 (16.5%), geographic atrophy (GA) in 20 (18.3%), and combined nAMD and GA in 14 (12.8%). The overall intergrader agreement demonstrated robust consistency, with a weighted kappa value of 0.95 (95% CI: 0.92 to 0.98), signifying excellent intergrader reliability and reinforcing the validity of SOAC. Conclusion SOAC provides a standardized, OCT based framework for AMD grading that demonstrates high intergrader agreement. By enabling consistent classification from commonly acquired OCT scans, SOAC supports reliable disease staging and facilitates integration across clinical studies and translational research. As imaging and molecular data continue to expand, SOAC can serve as a common OCT based reference for phenotype refinement and longitudinal AMD studies.

PurposeSubretinal drusenoid deposits (SDDs) are a distinct entity in age-related macular degeneration (AMD) and associated with photoreceptor impairment during progression. Their early impact on photoreceptors remains incompletely understood. This study examined photoreceptor reflectivity during the phase when SDDs were not clinically detectable on optical coherence tomography (OCT) using adaptive optics scanning laser ophthalmoscopy (AOSLO). DesignLongitudinal observational study. ParticipantsPatients with intermediate AMD. MethodsSix eyes of four patients with intermediate AMD and predominantly SDDs underwent multimodal imaging 3-4 times over 3.5 years. Individual SDDs were graded using an OCT-based 3-stage system at each time point. Cross-sectional retinal structure and photoreceptor reflectivity at the location where the new SDDs developed during follow-up were evaluated using OCT and AOSLO. Main Outcome MeasuresPhotoreceptor reflectivity change prior to and during SDD development. ResultsForty-eight retinal locations where new dot-type SDDs developed during follow-up were identified. AOSLO revealed reduced photoreceptor reflectivity in these regions before OCT demonstrated clinically evident deposits (stage [&ge;] 1) between the ellipsoid zone and the retinal pigment epithelium at the corresponding sites. The mean time to development of stage 1, stage 2, and stage 3 SDDs was 11.78 {+/-} 5.01, 17.40 {+/-} 6.08, and 18.72 {+/-} 4.08 months, respectively. ConclusionsHigh-resolution adaptive optics confocal imaging enables detection of photoreceptor optical property alterations at a stage when SDDs are not yet evident on OCT. This finding underscores the exceptional sensitivity of photoreceptors to minimal structural or functional perturbations during SDD formation and defines an early window for potential intervention.

PurposeQuantitative metrics obtained from retinal fundus images (such as vessel length, tortuosity and other scale-dependent measures) are increasingly used as potential biomarkers for systemic diseases, including cardio- and neurovascular conditions. However, with the increasing prevalence of myopia and related axial growth, this study aims to evaluate if axial length scaling significantly alters the overall distributions of the inferred biomarkers when compared to biomarker data obtained without axial length scaling and if these effects can be corrected. Methods2,309 clinic visits from patients aged [&le;]21 years were analysed and extracted for axial-length scaling analysis (range) 20 to 28 mm). The retinal fundus photographs were automatically segmented using Automorph to extract biometric data, including vascular metrics. The parameters were further corrected for axial length using correction factors based on the Bennett-Littmann formula and true axial length. ResultsAxial length significantly influenced biometric parameters (vessel metrics) derived from fundus photography. The magnitude of error in diameter and length of blood vessels was approximately 4-5% for each 1 mm deviation from the reference axial length of 24 mm, whereas the error in vessel area was approximately 9-10% per 1 mm, consistent with the geometric expectation that area scales with the square of linear dimensions. The scaling corrections for different axial lengths are presented. ConclusionsAxial-length-related magnification introduces systematic bias into retinal vascular metrics from fundus photographs. Bennett-Littmann correction using true axial length reduces these errors and should be adopted in quantitative fundus imaging and Al biomarker development.

ObjectiveTo assess how whole genome sequencing and varying phenotype definitions influence genetic discovery for primary open-angle glaucoma (POAG) in a diverse population. DesignAncestry-stratified genome-wide association studies (GWASs) and cross-ancestry meta-analyses of POAG cases and controls using two phenotype definitions. ParticipantsCases (age>40) and controls (age>65) were identified in the National Institutes of Health All of Us Research Program v8 data release and sub-divided into genetically inferred ancestral groups. Using the relaxed phenotype (ICD codes only), case/control counts were: European (1,846/84,654), African (1,042/15,966), and Latino/Admixed American (305/10,167). Using the stringent phenotype (ICD codes and evidence of glaucoma treatment in the electronic health record), case/control counts were: European (1,528/79,276), African (862/14,076), and Latino/Admixed American (250/9,668). Cross-ancestry meta-analyses included 3,193 cases/110,787 controls for the relaxed phenotype and 2,640 cases/103,020 controls for the stringent phenotype. MethodsGWASs were conducted within European, African, and Latino/Admixed American ancestry groups individually using firth logistic regression with age, sex, and the top 10 genotype principal components included as covariates. The ancestry-stratified GWASs were then meta-analyzed using a fixed-effects, inverse variance-weighted approach. Main Outcome MeasuresIdentification of genome-wide significant loci (P < 5x10-8) for POAG using different phenotype definitions and ancestry groups. ResultsKnown POAG risk loci (e.g., TMCO1, CDKN2B-AS1, and GMDS) reached genome-wide significance in both the European GWASs and cross-ancestry meta-analyses (odds ratio (OR) range: 1.19-1.38). A novel risk locus near CYP2A7 (rs76935404[T], OR = 1.35) was identified in the African ancestry GWAS using the stringent phenotype definition. Effect sizes for known POAG risk loci from prior large-scale meta-analyses strongly correlated with effect sizes in this study (Pearson r = 0.75-0.84, P < 1 x 10- for all). The strength and consistency of these correlations support the robustness of the findings. ConclusionsThis study demonstrates the value of whole genome sequencing, diverse ancestry inclusion, and phenotypic refinement in uncovering novel POAG genetic risk loci. The findings underscore the need to prioritize both genetic diversity and refined case/control definitions to advance understanding of this complex ocular disease. PrecisThis study identifies a novel primary open-angle glaucoma risk locus in individuals of African ancestry using whole genome sequencing and varying phenotype definitions in the diverse All of Us Research Program dataset.

Background: Mitochondrial dysfunction is an emerging metabolic hallmark of age-related diseases, yet tools to directly profile mitochondrial pathways and test metabolic interventions in the living human eye remain limited. Multi-omics ocular liquid biopsy enables real-time proteomic and metabolomic profiling of the intraocular microenvironment, complementing systemic biomarkers and imaging surrogates. Here, we used this approach to define mitochondrial and tricarboxylic acid (TCA) cycle dysregulation in geographic atrophy (GA) and to assess whether oral -ketoglutarate (-KG) supplementation can modulate mitochondrial metabolites within the eye. Methods: Mitochondrial and TCA cycle-related proteins were profiled in aqueous humor (AH) samples from patients with GA using DNA-aptamer-based proteomics. In a phase 0 study, a second cohort undergoing sequential cataract surgery provided paired AH samples collected at first-eye surgery and at second-eye surgery after interim -KG supplementation. These samples underwent targeted metabolomic profiling using hydrophilic interaction liquid chromatography coupled with mass spectrometry. Results: In GA, 64 mitochondrial proteins were differentially expressed, including coordinated TCA-cycle deficiencies marked by reduced expression of enzymes regulating TCA entry and flux, including PDHB and DLST. In the phase 0 cohort, oral -KG supplementation significantly increased intraocular -KG levels and the -KG-to-succinate ratio (P < 0.05), with coordinated shifts across TCA intermediates consistent with enhanced TCA cycle flux. Conclusions: AH proteomics demonstrated mitochondrial pathway depletion in GA, consistent with reduced oxidative bioenergetic capacity. AH metabolomics provided first-in-human in vivo evidence that systemic -KG supplementation can modify intraocular metabolites and may enhance intraocular energy metabolism. These findings support ocular liquid biopsy as a precision-health framework for per-patient biomarker-guided metabolic trials in GA.

Aim: To evaluate the potential of a three-dimensional microscope combining Laser scanning confocal imaging and white-light interferometry for quantitative topographic characterisation of Descemet's membrane (DM) in Fuchs endothelial corneal dystrophy (FECD). Methods: Descemet's membranes were collected from 38 FECD patients undergoing endothelial keratoplasty and 4 healthy donors. After flat-mounting on glass slide and drying, specimens were analysed using the VK-X3000 system (KEYENCE). Entire samples were reconstructed by image stitching at low magnification (x10) in white-light interferometry mode (0.01nm axial resolution). Higher magnifications (x20-x150) in confocal mode (12nm axial resolution) enabled detailed structural analysis. Three-dimensional height maps were generated to calculate standardised surface roughness parameters. Guttae and other DM features were classified according to spatial organisation and elevation profiles. Results: White-light interferometry enabled full-field mapping of whole 8mm diameter DMs with nanometric vertical resolution (~2 hours/sample). Surface roughness (Sa) was higher in FECD than in controls (median{+/-}IQR: 0.571{+/-}0.259 m vs 0.239{+/-}0.161 m ; p = 0.0018). In FECD, three zones were identified: central (guttae buried in the posterior fibrillar layer; Sa 0.442 {+/-} 0.112 m), paracentral (large uncovered guttae; Sa 0.562{+/-}0.170 m ; p = 0.0423), and outer zone (no confluent guttae; Sa 0.261{+/-}0.143 m ; p < 0.0001). Confocal 3D imaging revealed radial striae, embossments and furrows in the DM, confluent central guttae, and fused or buried structures. Conclusions: Combining white-light interferometry and confocal microscopy enables label-free, high-resolution surface characterisation of DM in FECD, providing quantitative metrics to compare histological subtypes and supporting the predominance of radial structural organisation.

PurposeTo evaluate the intra- and inter-grader concordance of anterior segment optical coherence tomography (ASOCT) grading for detection of endothelial plaque in microbial keratitis, and to compare endothelial plaque detection via ASOCT grading versus in-person slit lamp examination. MethodsDiagnostic concordance study of 150 consecutive patients with microbiologically confirmed bacterial or fungal keratitis at a high-volume tertiary eye hospital in India. Two masked ophthalmologist graders independently evaluated ASOCT images for presence of two morphologically distinct endothelial plaque subtypes noted during image review (round and flat plaques). We assessed intra-grader and inter-grader concordance for each endothelial plaque morphology and for presence of either morphology. Diagnostic agreement between ASOCT and in-person slit lamp examination was evaluated using percent agreement, Cohens kappa, sensitivity, and specificity. Univariable and multivariable logistic regression was used to assess odds of disagreement between ASOCT and slit lamp examination for endothelial plaque detection. ResultsASOCT detection showed near perfect inter-grader agreement for round endothelial plaques (kappa 0.88, 94.7% agreement), flat endothelial plaques (kappa 0.84, 92.0% agreement), and either plaque (kappa 0.88, 94.0% agreement). Intra-grader agreement was substantial to near perfect for both graders across all plaque types (kappa 0.70-0.86). Ophthalmologist slit lamp examination identified endothelial plaque in 6.0% eyes, while ASOCT detected round plaques in 32.7%, flat plaques in 43.3%, and either plaque in 55.3% of eyes. Using ASOCT as reference, slit lamp examination demonstrated sensitivity of 16.3% for round plaques, 6.2% for flat plaques, and 9.6% for either plaque, with specificity exceeding 94% for all. Poor visual acuity (logMAR [&ge;]1.0) was associated with increased disagreement for round plaques (adjusted OR 5.04), flat plaques (adjusted OR 3.63), and either plaque (adjusted OR 3.98). Bacterial infection was associated with increased disagreement for any endothelial plaque (adjusted OR 4.56). ConclusionSlit lamp examination substantially under-detects endothelial plaque compared to ASOCT, while ASOCT enables reproducible detection with excellent intra- and inter-grader agreement. These findings support incorporation of ASOCT imaging into microbial keratitis evaluation protocols. Differences in round and flat endothelial plaque morphologies warrant further investigation.

Sight is one of the five senses essential to human experience, and the eyes are vital organs that require careful protection. These organs are also susceptible to a variety of diseases, some of which may develop without obvious external symptoms, necessitating specialized imaging and diagnostic techniques. Conversely, other conditions present visible signs that can be observed directly. This paper presents a practical approach to the identification of thirteen well-known eye diseases-cataract, corneal neovascularization, corneal ulcer, dry eye, endophthalmitis, globe rupture, Graves ophthalmopathy, ptosis, scleritis, strabismus, stye, uveitis, and xanthelasma-based on visual symptoms. Using transfer learning with the ResNet152V2 deep learning model, we demonstrate an average validation accuracy of 98.8%. The methodology is presented in a reproducible, step-by-step format suitable for educational purposes, allowing opticians, general practitioners, and learners to explore automated eye disease diagnosis. All code, datasets, and procedures are documented to facilitate practical learning and replication.

Purpose: To quantify whether fundus tracking in microperimetry improves psychometric parameter estimation (in vivo demonstration of improved stimulus-delivery precision), and to derive a psychometrically grounded criterion intensity for suprathreshold (defect-mapping) microperimetry. Methods: Twenty-five healthy volunteers underwent MAIA2-microperimetry at five loci: three outside and two inside the blind spot. Frequency-of-seeing (FoS) functions were measured in four blocks (2 tracking on; 2 tracking off). FoS-data were fit using cumulative-Gaussian psychometric functions estimating sensitivity parameters. Mixed-effect models assessed tracking effects, and posterior simulations defined the optimal criterion intensity for separating 'seeing' from 'non-seeing' loci. Results: Tracking had little effect on threshold estimates at loci outside the blind spot, but lowered threshold estimates within the blind spot (posterior median difference PMD [95% CrI] of -1.46 dB [-2.30, -0.62] at locus 4, and -1.02 dB [-1.94, -0.08] at locus 5). Tracking was associated with steeper psychometric slope parameters at loci 1-3 (PMD of -0.14 dB [-0.29, 0.01], -0.27 dB [-0.43, -0.12], and -0.22 dB [-0.40, -0.04]). Without tracking, false-positive responses were more frequent when fixation shifts displaced stimuli toward the 'seeing' retina. Simulation-based analysis identified 13 dB as nominally optimal criterion for suprathreshold microperimetry (Youden index: 0.76 [0.74, 0.79], comparable to 10 dB (0.74 [0.72, 0.76]). Conclusions: Even in healthy volunteers with stable fixation, fundus tracking measurably reduced sensitivity estimates at 'non-seeing' loci and sharpened FoS curves in the 'seeing' retina. A criterion intensity of 10 to 13 dB is a defensible choice for separating 'seeing' and 'non-seeing' retina in suprathreshold (defect-mapping) perimetry paradigms.

Primary open-angle glaucoma (POAG) disproportionately affects individuals of African ancestry, yet rare coding variation in this population remains understudied. To address this gap, we performed a multi-cohort exome-wide meta-analysis across POAAGG, PMBB, All of Us, and UK Biobank, including 4,815 POAG cases and 22,922 controls of genetically inferred African ancestry. Although no gene reached exome-wide significance, we identified several suggestive gene-level associations driven by rare variants (minor allele frequency [&le;]0.1% or singletons),including signals in SRF, BLTP3A, METTL2A, and KRT10. Among these, SRF demonstrated the strongest association and was driven by rare missense variants with moderate effect sizes. Given its role in cytoskeletal organization and actin dynamics; processes central to trabecular meshwork function and intraocular pressure regulation SRF represents a biologically plausible candidate gene. Notably, these genes have not been previously highlighted in predominantly European ancestry POAG association studies, suggesting potential ancestry-specific rare variant contributions. Overall, our findings highlight the critical importance of investigating rare coding variation in POAG, in disproportionately affected populations to deepen understanding of POAG etiology and genetic risk.

Purpose: To compare hypopyon detection using anterior segment optical coherence tomography (ASOCT) versus slit lamp examination (SLE) in microbial keratitis, and to evaluate intra-and inter-grader agreement for ASOCT hypopyon measurement. Methods: Two masked graders independently evaluated ASOCT images for hypopyon presence or absence in eyes with microbial keratitis, with disagreements resolved by consensus. A subset of hypopyon eyes underwent triplicate height measurement using two methods (endothelial length, vertical height). Cohen's kappa, intraclass correlation coefficients (ICC), sensitivity, and specificity were calculated comparing diagnostic performance of ASOCT versus SLE. Results: Inter-grader agreement for hypopyon detection on ASOCT was excellent (k=0.94; 95% CI 0.84-1.00) and intra-grader agreement was excellent (k=0.89-1.00). ASOCT detected hypopyon in 67.1% of eyes versus 57.0% by SLE (sensitivity 83.0%, specificity 96.2% using ASOCT as reference). Intra-grader reproducibility was excellent for both endothelial length and vertical height measurements (ICC 0.977-0.996). Inter-grader agreement was good for endothelial length (ICC 0.831) and vertical height (ICC 0.827), though a statistically significant inter-grader bias was identified for vertical height only (Wilcoxon p=0.008). Conclusions: ASOCT detected hypopyon with greater sensitivity than SLE and demonstrated excellent intra-grader and good inter-grader measurement reproducibility. Endothelial length showed slightly superior inter-grader concordance to vertical height measurement.

BackgroundThe visual field (VF) test results of many eyes with glaucoma progress despite treatment. This suggests that some eyes are either untreated or that the management of intraocular pressure (IOP) does not influence the outcome. In this work, we explore whether future VF parameters can be predicted from a baseline optical coherence retinal nerve fibre layer (OCT-RNFL) scan using a deep learning model. MethodsThe model was developed using 1792 eyes from 1610 patients, and externally validated on 151 eyes from a second centre using the same Zeiss Cirrus machine and 281 eyes from a third centre using scans obtained from a different (Heidelberg Spectralis) machine. The Vision Transformers (ViT)-based regression model was trained on baseline OCT-RNFL scans to predict three key VF indices (follow-up interval: 4.74 {+/-} 2.59 years). Model performance was evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), with 95% confidence intervals (CI). ResultsThe model achieved an overall MAE of 2.07 (95% CI: 1.91-2.22) and RMSE of 2.87 (95% CI: 2.60-3.14) on the internal validation set. On external validation, the model showed comparable performance with an MAE of 2.07 (95% CI: 1.8-2.35) for the external validation (Zeiss OCT) cohort and 2.11 (95% CI: 1.93-2.31) for the external validation (Heidelberg OCT) cohort. Saliency maps revealed that the inner and outer RNFL layers were key structures in driving the models predictions. ConclusionsOur ViT-based regression model effectively predicts key VF indices objectively from a single OCT-RNFL scan, with strong performance across two OCT devices, offering a novel tool for predicting glaucoma progression.