British Journal of Ophthalmology

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.

BackgroundAchieving precise postoperative refractive outcomes remains a significant challenge in cataract surgery. While advanced intraocular lens (IOL) power calculation formulas exist, they are constrained by their singular algorithmic structures. This study investigated whether a stacking ensemble machine learning approach could overcome these limitations. MethodsA dataset of 1,710 eyes from patients who underwent cataract surgery with monofocal IOL implantation (Vivinex or SA60AT) was utilized. Following rigorous preprocessing and feature engineering, a stacking ensemble architecture was developed comprising three diverse base learners (Multi-Layer Perceptron, Support Vector Regressor with RBF kernel, and SplineTransformer with Linear Regression) and a Ridge Regressor meta-learner. The model was trained on 80% of the data using 5-fold cross-validation and evaluated on an independent 20% test set (n=341). Performance was compared against six standard IOL formulas. ResultsThe stacking ensemble model demonstrated excellent predictive accuracy, achieving a Mean Absolute Error (MAE) of 0.272 D on the independent test set (n=341). The model achieved lower MAE compared to all six standard IOL formulas, including Kane (MAE 0.295 D) and Barrett Universal II (MAE 0.318 D). Clinically, 85.1% of eyes achieved predictions within {+/-}0.50 D, compared to 82.5% for Kane formula and 81.8% for Barrett Universal II. ConclusionThe stacking ensemble machine learning model significantly enhances postoperative refraction prediction accuracy compared to established IOL calculation formulas. By leveraging algorithmic diversity and data-driven learning, this approach represents a promising advancement toward reducing refractive surprises and improving patient satisfaction in cataract surgery. External validation on independent datasets is required to confirm generalizability.

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

Purpose:To improve determination of the treatment area for the personalization of subliminal transscleral cyclophotocoagulation (SL-TSCPC) procedures in glaucoma treatment, we designed a biometry based model of the human eye to find the estimated cilary body (CB) arc length (ECBAL) and the calculated CB distance (CCBD). Methods: We developed a rotationally symmetric modified two-sphere eye model based on axial length (AL), mean keratometry (mean K), anterior chamber depth (ACD), lens thickness (LT), and white-to-white (WTW). ECBAL and CCBD were calculated for each eye. Fluence was calculated with standardized parameters. Results: Publicly accessible biometric measurements for 24,001 eyes were pooled for analysis. The mean ECBAL was 23.99+-1.8 mm. The correlations of ECBAL with AL and ACD were 0.723 and 0.754 respectively (p < 0.01). The number of eyes with an ECBAL 21.7-22.0 mm was 131 of 24,001 (0.55%). The mean CCBD was 4.21+-0.8 mm. The number of eyes with a CCBD of 3.8 mm was 1,445 of 24,001 (6.02%). Mean fluence was 120.33+-9.0 J/cm2. A mean difference of -8.18+-6.9%, ranging from -22.66% to +29.07% in fluence was observed with treating only the recommended 22 mm versus the ECBAL. Conclusions: This study demonstrated that use of 22.0 mm as the standard treatment arc length may under or overdose laser treatment in many eyes. Precise estimation or exact localization of the CB treatment area is required to accurately calculate fluence. Translational Relevance:The model proves that CB arc length is a variable while current guidelines consider it a constant

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.

PurposeTo evaluate the performance of machine learning models for automated glaucoma detection using multimodal clinical, structural, and functional data from a West African clinical cohort. MethodsIn this retrospective observational study, we analyzed clinical records from two major eye care centers in Ghana. A total of 605 eyes from 417 patients who underwent comprehensive glaucoma evaluation were included. Extracted features included demographic data, intraocular pressure, optical coherence tomography (OCT) structural parameters, and Humphrey visual field indices. We assessed the diagnostic performance of individual parameters using receiver operating characteristic (ROC) analysis. Supervised machine learning classifiers, including support vector machine (SVM), random forest (RF), gradient boosting machine (GBM), and a multi-layer perceptron (MLP), were trained using a forward feature selection approach and evaluated using five-fold cross-validation. We assessed model performance by computing performance metrics like sensitivity, specificity, and area under the ROC curve (AUC). ResultsAmong the 605 eyes analyzed, 361 (59.7%) were glaucomatous, and 244 (40.3%) were healthy. Individual structural and functional parameters demonstrated moderate discriminative ability, while age showed no significant diagnostic value (AUC = 0.49, p = 0.841). Among machine learning models, the MLP achieved the highest diagnostic performance (AUC = 0.90 [95% CI: 0.86-0.92], sensitivity = 0.88, specificity = 0.86), outperforming SVM (AUC = 0.82), RF (AUC = 0.78), and GBM (AUC = 0.77). Multimodal integration of clinical, structural, and functional features improved discrimination compared with individual parameters. ConclusionsMultimodal machine learning models can effectively automate glaucoma detection using routinely collected clinical data. In this West African cohort, an MLP-based approach demonstrated superior diagnostic performance compared with traditional machine learning models and individual clinical metrics. These findings highlight the potential of clinically grounded artificial intelligence tools to support glaucoma diagnosis and triage in resource-constrained eye care settings.

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.

Objective: The study aims to evaluate the real-world effectiveness of Highly Aspherical Lenslets spectacle (HAL; Essilor(R) Stellest(R)) in slowing myopia progression among Indian children and adolescents aged between 4 and 16 years. Methods and analysis: This was a multicentre retrospective study conducted across 10 leading ophthalmic centers. The study participants comprised children aged between 4 and 16 years who were prescribed HAL spectacles (Essilor(R) Stellest(R)). Data were extracted from electronic medical records at three time points: T1: One year prior to intervention; T2: Baseline at HAL spectacle prescription; T3: 6 to 24 months after prescription. The primary endpoint was the myopia progression and axial elongation in the year following prescription, compared with the untreated year and with published meta-regression models. Only data from the right eye were analysed, with the expected physiological progression estimated based on the individual progression trajectory after adjusting for age-related slowing as reported in published meta-regression models. Results: A total of 372 myopic children were included in the study. The annual myopia progression was -0.72 {+/-} 0.47 D/year during the untreated period, reducing to -0.11 {+/-} 0.29 D/year with HAL spectacle wear. The expected progression without treatment was -0.65 D/year, based on trajectory-adjusted modelling, indicating a treatment effect of 0.54 D/years and an estimated 83% slowing in myopia progression compared to expected progression. The expected axial elongation under physiological conditions was 0.29 mm/year, estimated using age-adjusted meta-regression models; with HAL lens wear, axial elongation was 0.11 {+/-} 0.16 mm/year, corresponding to a [~]62% relative slowing of elongation. Conclusion: The present study demonstrates the real-world evidence validating the efficacy of HAL lenses as an effective myopia control intervention in Indian children and adolescents. The retrospective design and limited follow-up period warrant future prospective, long-term studies to validate these findings.

PurposeTo systematically evaluate ocular biometric and systemic laboratory factors associated with cataract in highly myopic eyes and to characterize potential nonlinear associations using an interpretable machine learning approach, thereby providing deeper mechanistic insights into the pathogenesis of highly myopic cataract. DesignA cross-sectional study encompassed 770 eyes of 594 patients with high myopia from Eye & ENT Hospital of Fudan University. SubjectsThe non-cataract control group included 458 eyes while the cataract group contained 312 eyes. MethodsDemographic traits, ocular biometric and systemic laboratory factors were gathered while features with over 30% of missing data were excluded. Composite indices were obtained through calculation. Multiple machine learning models were compared to investigate the association between features and highly myopic cataract, and the random forest (RF) model was chosen and fine-tuned. Feature selection was carried out by means of Shapley additive explanations (SHAP) and non-linear relationships were probed using SHAP dependence diagrams and confirmed with partial dependence plots. Main Outcome Measures(1) The Area Under the Curve (AUC) and other metrics of multiple machine learning models; (2) Top feature importance of the final simplified RF model; (3) Overall trends between features and highly myopic cataract; (4) Potential inflection points of top continuous features. ResultsA simplified fine-tuned RF model with 17 features reached stable discriminative performance, with a mean AUC of 0.762 (95%CI: [0.731, 0.794]) among 10 independent testing sets. Age and axial length (AL) turned out to be the most influential features which had non-linear relationships highly myopic cataract, with an inflection point seen around 65.75 (95%CI: [63.72, 67.79]) years for age and 30.55 (95% CI: [29.22, 31.88]) mm for axial length respectively, while the ratio of anterior chamber depth to axial length (ACD/AL) was associated with highly-myopic cataract in a U-shape. Ocular biometric factors were more strongly related to highly myopic cataract than systemic laboratory factors. ConclusionsOcular biometric factors, especially age, AL, and composite indices like ACD/AL, have strong and non-linear connections with highly myopic cataract. These results emphasize the significance of ocular structural arrangement in cataract within highly myopic eyes and indicate that interpretable data-driven methods could offer clinically relevant understandings regarding its phenotypic description.

PurposeTo evaluate the three-year efficacy and safety of compound trabeculectomy for uveitic glaucoma (UG). MethodsThis retrospective study enrolled 51 patients (53 eyes) requiring compound trabeculectomy, divided into UG (25 eyes) and non-UG groups (28 eyes). Outcomes including intraocular pressure (IOP), medication use, surgical success rates, and complications were analyzed over 3 years. ResultsBaseline characteristics including age, sex, preoperative IOP and medication use were comparable (all P>0.05). At 36 months, postoperative IOP was showed no significant differences, which was 15.4{+/-}8.4 mmHg and 14.6{+/-}3.3 mmHg (P=0.73) with 54% and 55% reduction (P=0.88) in UG and non-UG groups respectively. The qualified success rate was 76.0% and 85.7% at 36 months in UG and non-UG group, and Kaplan-Meier analysis showed no significant difference. Medication reduction of UG group was significant lower than non-UG group (P=0.0058). Comparable complication rates were observed between groups (all P>0.05), yet bleb scarring and cataract progression showed elevated incidence in both cohort. ConclusionCompound trabeculectomy effectively reduced IOP and medications use in UG and non-UG. There was no significant difference in both qualified and completed success rate between UG and non-UG. Complications of filtering bleb fibrosis and cataract progression should be pay close attention for both groups.

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.

Purpose To evaluate patient satisfaction and preferences for portable versus table-mounted visual field (VF) devices in a rural telemedicine setting and identify influencing factors. Methods We conducted a sequential explanatory mixed methods study at three Federally Qualified Health Centers (FQHCs) within the Alabama Screening and Intervention for Glaucoma and eye Health through Telemedicine (AL-SIGHT) study. Participants completed VF testing with table-mounted Humphrey Field Analyzer (HFA), tablet-based Melbourne Rapid Fields (MRF), and virtual reality (VR)-based VisuALL perimeters. Participants rated satisfaction, comfort, ease of use, and future testing preference. Chi-square tests assessed differences in device preferences. Twelve participants completed semi-structured interviews to explore reasons underlying preferences. Qualitative data were analyzed in NVivo 14 using reflexive thematic analysis. Results Among 271 respondents (mean age 60.4 years; 62.4% women), 50.6% preferred VR-based, 35.1% tablet-based, and 14.4% table-mounted for future testing ({chi}2 (2) = 53.52, p<0.001, Cramers V = 0.31). Satisfaction was highest for VR-based (56.9% very satisfied), followed by tablet-based (49.4%), and HFA (38.0%). VR-based perimeter was most frequently selected as the most comfortable (55.7%; {chi}2 (2) = 63.33, p<0.001, V = 0.34) and easiest to use (54.6%; {chi}2 (2) = 71.96, p<0.001, V = 0.36). Preferences did not vary significantly across demographic variables (all p>0.05). Qualitative themes identified four key drivers: comfort and physical experience, visual experience, ease of use and interaction, and psychological and motivational factors. Portability and community suitability were valued. Conclusion Rural underserved patients strongly preferred portable visual field devices, particularly VR-based, over table-mounted HFA. Comfort, ergonomic flexibility, immersive visual experience, and simplicity of interaction were central determinants of preference. Portable perimetry may enhance patient-centered glaucoma monitoring within telemedicine programs and access in resource-limited settings.

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.

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.

Purpose: To identify the ocular biometric parameters associated with refractive outcomes in Chinese Primary angle closure glaucoma (PACG) patients receiving phacoemulsification and intraocular lens (IOL) implantation (PEI) surgery. Methods: 165 Chinese PACG patients receiving PEI and goniosynechialysis (GSL) and 53 cataract patients as controls only receiving PEI surgery were recruited. The prediction accuracy of IOL power calculation was assessed by the prediction error (PE), mean absolute error (MAE), median absolute error (MedAE), and proportions of eyes with a PE within {+/-} 0.25 diopters (D), {+/-} 0.50 D, {+/-} 0.75 D, and {+/-} 1.00 D. The association of different ocular biometric parameters with the PE of IOL calculation were evaluated. Results: The PACG patients had significantly higher absolute of PE as compared to the control subjects, especially the acute PACG patients. The axial length (AL), changes in aqueous depth pre- and post-surgery ({bigtriangleup}AD), and the ratio of {bigtriangleup}AD/AL were significantly associated with the PE in acute PACG patients. The association of {bigtriangleup}AD with the PE of IOL power calculation was found in PACG patients with AL [&ge;] 22 mm. Conclusions: This study revealed the association of AL and {bigtriangleup}AD with the PE of IOL calculation in Chinese PACG patients. Precisely predict the {bigtriangleup}AD is necessary for acute PACG patients, especially for those with AL [&ge;] 22 mm, to improve the refractive outcomes.

To determine whether achieving normal corrected visual acuity independently influences myopia progression in school-aged children wearing single-vision spectacles. In a one-year real-world cohort study, 9-year-old myopic children were classified into three groups: uncorrected, adequately corrected (normal visual acuity), and under-corrected (subnormal visual acuity). One-to-one propensity score matching was used to balance baseline characteristics, and annual axial length growth was compared. The adequately corrected group showed the slowest axial elongation (0.23 {+/-} 0.14 mm/year), significantly less than both the under-corrected (0.35 &{+/-} 0.14 mm/year) and uncorrected groups (0.37 &{+/-} 0.16 mm/year) (all P < 0.001). Although the under-corrected group exhibited marginally slower progression than the uncorrected group, this minimal benefit was not sustained in semiannual analyses and lacked clinical relevance. Simply prescribing spectacles is insufficient for myopia control; achieving normal corrected visual acuity is essential to meaningfully slow axial elongation.

BackgroundEpidermal Growth Factor Receptor (EGFR) inhibitors, while effective in oncology, are associated with under-characterized ocular adverse events (AEs). Prior studies have been limited in scope, lacking a comprehensive, class-wide analysis of the full spectrum of ocular toxicity, particularly for newer agents. MethodsWe conducted a disproportionality analysis of the FDA Adverse Event Reporting System (FAERS) (2001-2025). Twelve EGFR-targeted agents were evaluated against a pre-specified set of ocular MedDRA Preferred Terms. To ensure robust signal detection, a significant association was defined by [&ge;]3 co-reported cases, a Proportional Reporting Ratio (PRR) [&ge;]2.0, and a false-discovery-rate adjusted p-value <0.05. ResultsAmong 6,976,462 drug-event combinations, 20 met all signal criteria for Eyelash Abnormalities, Ocular Surface Disease, or Vision-Threatening and Intraocular Events. Trichomegaly demonstrated extreme disproportionality (e.g., panitumumab PRR= 465.3, 95% Confidence Interval [CI], 247.7-874.3). A consistent pattern of ocular surface toxicity (conjunctivitis, keratitis, blepharitis) was observed across multiple tyrosine kinase inhibitors and monoclonal antibodies, indicating a class-wide effect. Signals for serious events included corneal perforation (erlotinib, n= 7, PRR=13.9, 95% CI= 6.6-29.4) and optic neuropathy (erlotinib, n= 6, PRR= 2.9, 95% CI= 1.3-6.4). ConclusionThis analysis confirms a strong, class-wide signal for ocular toxicity across the spectrum of EGFR inhibitors, from characteristic eyelid changes to sight-threatening complications. These findings underscore the necessity for proactive ophthalmologic monitoring, including baseline assessment, in patients receiving these therapies to preserve vision and maintain quality of life during cancer treatment.

ImportanceGenome-wide association studies have identified hundreds of common single nucleotide polymorphisms (SNPs) and small insertions/deletions (indels) associated with primary open-angle glaucoma (POAG) risk, though these variants have modest effect sizes and individually may have minor contributions to disease development. As whole-genome sequencing data is becoming more readily available, structural variants and other complex genomic features can be interrogated for contribution to disease risk. ObjectiveTest the association of structural variants in known glaucoma loci with disease risk. DesignCross-sectional study. SettingA multicenter cohort of individuals from the United States who contributed genomic and electronic health record data to the All of Us Research Program. ParticipantsPOAG case/control cohorts were generated in the All of Us Researcher Workbench using age (>40 for cases, >65 for controls) and ICD 9/10 diagnosis codes. Main Outcomes and MeasuresLogistic regression analyses adjusted for age, sex, and the top 10 principal components of ancestry were used to test association of structural variants within 500 kilobases of 309 known open-angle glaucoma risk loci. The significance threshold after Bonferroni correction was set at p<1.6x10-4. Results516 POAG cases and 18,716 controls of European ancestry from the All of Us v8 data release were included in the analysis. Mean age was 77.0 years among cases and 74.7 years among controls. Females comprised 45.7% of cases and 56.5% of controls. An 8,732 base pair deletion upstream of PITX2 (chr4:110680827-110689558) was associated with 7.3-fold higher odds of POAG (95% confidence interval: 2.9-18.5, p= 2.4x10-5, variant carrier frequency= 1.6% in cases and 0.25% in controls). Functional annotation identified multiple enhancers overlapping the deletion, suggesting that this structural variant likely impacts gene regulation and expression. Conclusion and RelevanceWhole genome sequencing data captures rare structural variants with large effect sizes that are missed by conventional SNP and indel genotyping approaches, enabling improved POAG risk stratification. These data also expand the phenotypic spectrum of structural variation in the PITX2 locus from childhood glaucoma to adult-onset disease, where age at diagnosis and clinical severity may be influenced by the extent of disrupted regulatory elements.

PurposeThis study aimed to compare the reliability of myopia-related information from AI chatbots using a set of commonly asked questions by parents and patients on myopia, which is an emerging disease of the 21st-century. DesignProspective comparative reliability study MethodsThe study used ChatGPT(OpenAI(2025)GPT-5), Gemini(Gemini 2.0,Google,2025) and DeepSeek (DeepSeek-R1). Twenty myopia-related questions were framed from the perspective of parents and patients, covering general questions, prevention and control, and complications of myopia. Based on their experience in the field of myopia, two senior clinicians, one junior clinician and one researcher(all[&ge;]3 years of experience in myopia) rated the responses generated by AI chatbots on a 5-point Likert scale(1:very poor, 2:poor, 3: acceptable, 4:good and 5:very good). ResultsOverall, combined rating for tested chatbots had median score of 4("good"). Gemini received significantly lower ratings than other two chatbots (p[&le;]0.001), with a median rating of 3("acceptable"). ChatGPT and DeepSeek had median score of 4("good") and there was no significant difference in ratings (p=0.48). Both ChatGPT(66.0%) and DeepSeek(67.5%) had high proportions of "good" and "very good" ratings, compared to Gemini(40.0%). Combined "poor" and "very poor" ratings were highest for Gemini(7.5%), followed by ChatGPT(5.0%) and DeepSeek(4.0%). For general questions on myopia, ChatGPT and DeepSeek were rated "good"; for complications of myopia, ChatGPT was rated as "good", while others were rated "acceptable". ConclusionsChatGPT and DeepSeek demonstrated consistently high-quality responses, while ratings for Gemini were slightly lower but remained adequate. These findings suggest AI chatbots can support patients or parents in understanding myopia.

Purpose: To highlight the roles of intraoperative optical coherence tomography (iOCT) in managing acute corneal hydrops (ACH) and outcomes of iOCT-guided pneumodescemetopexy and corneal compression sutures. Methods: This was a retrospective, consecutive, interventional case series of patients with keratoconus who presented with significant ACH and underwent iOCT-guided pneumodescemetopexy (18% sulfur hexafluoride gas) and compression sutures at Birmingham and Midland Eye Centre, UK, between Aug 2023 and May 2025. Results: Five patients were included; mean age was 32.3+/-6.6 years old and 3 (60%) were male. The mean follow-up duration was 16.3+/-5.6 months. At presentation, the mean corrected-distance-visual-acuity (CDVA) was 1.90+/-0.67 logMAR, central corneal thickness (CCT) was 1187.6+/-372.6um, maximal corneal thickness was 1624.0+/-383.5um and maximal height and diameter of pre-Descemet layer/Descemet membrane (PDL/DM) detachment was 1014.6+/-366.4um and 4456.0+/-839.4um, respectively. The surgery successfully achieved complete PDL/DM attachment in all cases, with a mean time from surgery to ACH resolution of 17.8+/-8.0 days. iOCT successfully visualized the area of PDL/DM break/detachment, revealed the involvement of PDL (evidenced by a persistent taut type 1 DM detachment after gas tamponade), and guided the placement of compression sutures. Compared to preoperative, there was a significant improvement in the mean CDVA (0.52+/-0.32 logMAR; p=0.014) at last follow-up. One patient required a repeat procedure to fully attach the PDL/DM. Conclusions: This study demonstrated favorable outcomes of iOCT-guided pneumodescemetopexy and corneal compression sutures. iOCT revealed the involvement of PDL in ACH and provided plausible explanations why pneumodescemetopexy alone may not be able to resolve significant ACH rapidly in certain cases.