Ophthalmology Science

Diabetic retinopathy (DR) screening commonly relies on fixed follow-up intervals, although progression risk differs across patients. We developed a preliminary image-clinical framework to support personalized follow-up recommendations from retinal fundus images and systemic risk factors. Public DR datasets were harmonized into a binary task distinguishing absence of DR from DR of any grade. An ImageNet-pretrained ResNet50 and a foundation model-based feature extraction pipeline were compared. The selected image model was integrated with literature-derived severe retinopathy progression curves and clinical risk modifiers to estimate personalized cumulative risk and assign follow-up intervals using a predefined acceptable risk threshold. The ResNet50-based model was selected for subsequent analyses. In the target cohort, the integrated model assigned 90.0\% of patients to follow-up within 12 months. Clinical adjustment substantially modified image-only recommendations, generally shifting patients toward shorter intervals. These preliminary findings support the feasibility of combining image-derived estimates of baseline DR status with clinical modifiers to inform personalized screening intervals. Larger longitudinal studies are needed to validate calibration, clinical utility, and safety before real-world implementation.

Purpose: To evaluate the safety, procedural performance, sample recovery, and surgeon preference of an ophthalmic needle designed specifically for anterior chamber (AC) paracentesis. Methods: In this multicenter study, AC paracentesis was performed in clinic and operating-room settings using a 32-gauge x 4-mm needle with low dead space. The procedure was evaluated using a standardized physician survey. Prespecified outcomes included procedure-related adverse events (primary outcome), needle entry and handling, aspiration and sample recovery, comparative performance versus a 30-gauge needle, and physician preference for future use. Results: A total of 110 needle uses by eight surgeons were included. No ocular complications occurred, including lens or iris injury, hyphema, AC collapse, wound leak, hypotony, infection, or retinal complication, and no procedure required needle exchange or conversion to another device. Two technical events without ocular sequelae were noted, in which needle entry was partial thickness and did not reach the AC (1.8%; exact 95% CI, 0.2%-6.4%). Physicians rated needle entry, handling and sample recovery as good or excellent. Compared with a 30-gauge needle, the study needle was rated as at least comparable across all assessed domains. All surgeons rated it better or much better for intra-procedural safety and preferred it for future AC taps. Conclusions and Relevance: This short, 32-gauge low-dead-space ophthalmic needle demonstrated a favorable safety profile and was preferred over a 30-gauge needle by all surgeons. As aqueous humor liquid biopsy expands in clinical diagnostics and trials, an ophthalmic-specific needle design may help improve the consistency and safety of aqueous humor collection for molecular analysis and broader clinical use. Keywords: Anterior chamber paracentesis; Aqueous humor; Liquid biopsy; Low dead space; Ophthalmic needle

Purpose: New therapeutic strategies such as optogenetics have created a need for accurate tracking of inner retina degeneration in Retinitis pigmentosa (RP) patients. We introduce two tailored deep learning models to segment the RNFL (retinal nerve fibre layer), GCIPL (ganglion cell inner plexiform layer), INL (inner nuclear layer), CFT (central foveal thickness) and RPE (retinal pigment epithelium) in RP: The first is based on a Segment Anything Model (SAM), the second on nnU-Net. To our knowledge, SAM has not yet been applied to retinal layers in OCT data. Methods: SD-OCT images of a retrospective cohort of 37 RP patients were included. Data for four training cycles were prepared semi-automatically in MATLAB, then assessed and corrected by three expert graders. 1,700 segmented B-Scans from two open datasets were used for pretraining. For post-processing, semantic retinal boundary detection was developed. The final models, OCT-SAM and nnU-Net, were trained on 228 annotated RP scans. Detected layer thicknesses were validated against manual segmentation at 90 random points in 30 OCT B-Scans. Finally, OCT-SAM was tested on three RP cases with retrospective, longitudinal OCT data. Results: nnU-Net achieved a precision, recall and F-1 score of 0.96 while OCT-SAM performance resulted in slightly lower values of 0.93, 0.8 and 0.85, respectively. OCT-SAM measurements had low bias and good agreement with manual annotations, confirming reliability. Conclusions: OCT-SAM enabled fast data annotation and tool integration, whereas nnU-Net provided the best segmentation performance. OCT-SAM demonstrated longitudinal reproducibility and detected RP-characteristic pathologies and degenerative changes. Future work will extend OCT-SAM to 3D OCT segmentation.

Purpose: To investigate how artificial intelligence (AI) systems detect referrable diabetic retinopathy (DR) from retinal photographs by analysing heatmap patterns and determining their overlap with DR features. Methods: Fifty-four AI systems were developed using 27 backbone architectures, with each implemented as both binary-referable and multi-class grading models based on the International Clinical Diabetic Retinopathy (ICDR) grading scale. Models were trained on images from DDR, BRSET and Kaggle datasets. After training, each model analysed 749 images with DR feature annotations, with Grad-CAM heatmaps generated and compared to pixel-level annotations of microaneurysms, haemorrhages, exudates, cotton wool spots, venous beading, intraretinal microvascular abnormalities and neovascularisation. Results: All models achieved acceptable predictive performance (AUROC >0.8 for most architectures). Heatmap analysis revealed consistent attention to the macular region with relative neglect of the optic disc. Exudates and cotton wool spots were highlighted most frequently by the heatmaps, with venous beading and neovascularisation at the disc showing poor overall coverage for binary referable classifiers. Models grading per the ICDR scale demonstrated high coverage for all features. Substantial variability was observed between architectures, suggesting different feature detection capabilities. Interestingly, the heatmap analysis indicated that the models were using different logic to the ICDR grading scale definitions. Conclusion: AI models do not uniformly rely on all DR features when detecting referable DR, limiting their predictive performance in unusual presentations. Heatmap aggregation analysis provides a scalable method for analysing model behaviour, allowing strengths and weaknesses to be identified. These findings may help improve clinician's trust and acceptance of AI.

Background: Interocular asymmetry of axial elongation predicts accelerated myopia progression in adults, but childhood prevalence and dynamics are uncharacterised, with no standardised metric. A scale-invariant metric is proposed and characterised in a paediatric cohort with progressive myopia. Methods: A retrospective cohort of 267 children (5-16 years; 913 follow-up intervals) with progressive myopia and routine optical biometry collected during 2015-2026 was analysed. The Relative Asymmetry Index was defined per interval as the absolute interocular axial length difference normalised to the larger eye's change; its patient-level median defined the Cumulative Relative Asymmetry Index (CRAI). An annualised Absolute Asymmetry Index (AAI) was introduced as a complementary rate metric. Results: Median CRAI was 22.7 % [interquartile range 12.8-38.8]; median AAI 0.073 mm/year. Statistically detectable asymmetry (AAI > minimal detectable change at 95 % confidence) was present in 15 % of patients; 13.9 % showed pronounced asymmetry (CRAI > 50 %; AAI 0.218 mm/year). CRAI rose with age (Spearman rho = +0.28; partial rho = +0.25 after adjustment for net axial length change rate, both p < 0.001), and was independent of the overall rate after age adjustment (partial rho = -0.08, p = 0.20), consistent with scale invariance. The overall net axial length change rate decreased approximately three-fold across age strata (rho = -0.23, p < 0.001). Conclusions: CRAI and AAI provide complementary approximately scale-invariant and absolute metrics of interocular asymmetry. Interocular asymmetry appears common in this cohort and shows a marked age-related rise independent of overall progression rate.

Abstract Purpose: To expand the clinical and mechanistic understanding of the +57C>T seed-region mutation in miR-184 causing EDICT (endothelial dystrophy, iris hypoplasia, congenital cataract, and stromal thinning) syndrome. Design: Cross-sectional analysis and laboratory confirmation Participants: 18 members of a four-generation family with known +57C>T miR-184 status Methods: We used optical biometry, corneal topography, and medical history to characterize the clinical phenotype. Carrier effects on ocular biometric measurements were estimated using polygenic linear mixed models incorporating a pedigree-derived kinship matrix, adjusted for age and sex. Patient-derived and control induced pluripotent stem cells (iPSCs) were generated and differentiated into corneal endothelial cells (CECs). Main Outcome Measures: Axial length, keratometry (in diopters), white-to-white corneal diameter, topography mapping, central and peripheral corneal thickness, and history of retinal detachment or corneal transplant were compared between mutation carriers and noncarriers, adjusting for age and sex. Cellular analysis was conducted with immunostaining (ZO-1, ATP1A1), morphometric quantification, qRT-PCR of endothelial markers, and transendothelial electrical resistance (TEER). Results: 10 of 18 family members were heterozygous for +57C>T, with retinal detachment occurring in 5/10 affected individuals compared to 0/8 unaffected individuals (p=0.04). Affected eyes had 2.2 mm shorter axial length (p=0.02), 9.3 D steeper mean keratometry (p=0.004), 1.6 mm smaller horizontal corneal diameter (p=0.0001), and 139-micrometer thinner central corneas (p=0.003). Mutant iPSC-derived CECs were associated with irregular borders, increased cell and nucleus area, widened intercellular gaps, disrupted ATP1A1 membrane localization, and reduced barrier function on TEER (all p<0.05). Gene expression analysis showed downregulation of COL4A1, COL4A3, and AQP1 with upregulation of COL8A1. Conclusions: The miR-184 +57C>T mutation produces a broad ocular phenotype that includes smaller, thinner corneas and microphthalmia. Mechanistically, it disrupts CEC junctional integrity, extracellular matrix and pump-related genes, supporting a role for miR-184 in coordinated anterior-posterior eye morphogenesis.

Purpose: To validate an automated pipeline to detect and quantify focal retinal and choroidal pulsation areas that are synchronous with the cardiac cycle in video indocyanine green angiography (ICGA). Design: Retrospective, observational, hypothesis-generating validation study Subjects, Participants: Consecutive patients with a diagnosis of central serous chorioretinopathy (CSCR) in one or both eyes. Methods: Videos were acquired on Heidelberg HRA+OCT. The pipeline consisted of three steps: signal extraction, foci detection, and quantification. After registration of the constituent frames, each pixel's intensity signal was analyzed at the presumed cardiac frequency (tested from a sample of three detectable frequencies). A synchrony score combining local phase coherence with oscillation amplitude was then derived and computed using a standard deviation ({sigma}) above each video's background oscillation value. Two masked graders marked the retinal and choroidal pulsation areas twice. We compared detection of the pulsation areas against grader consensus using a receiver operating characteristic curve (using multiple grid sizes to divide the scan area) and, separately, using a signal-based area-reduction method to obtain an optimum {sigma} value. Main Outcome Measures: Agreement between the automated algorithm and human graders in detection of pulsation foci, and the optimum threshold multiplier ({sigma}). Results: We studied 20 ICGA videos from 20 eyes. At the 16-pixel grid size, the pipeline achieved a mean area under the curve (AUC) of 0.914, sensitivity of 0.86, and specificity of 0.80. Grader agreement improved with larger grid size, reaching substantial-to-strong levels for choroidal annotations. The two independent validation methods demonstrated similar {sigma} values that differed by 0.62{sigma}, supporting {sigma}=4.0 as the optimum value. Conclusions: We report the first automated method to quantify retinal and choroidal vascular pulsation on video ICGA. It measures pixels that oscillate over time with the presumed cardiac cycle and works reliably at the spatial scale (grid level) where experts agree. Pulsatile hemodynamics may add a new vascular biomarker for glaucoma, diabetes, hypertension, and pachychoroid diseases.

Objective: To investigate the performance of anterior segment (AS) OCT quantitative imaging of anterior chamber inflammation in uveitis patients with diverse demographics. Design: Prospective cross-sectional study. Participants: 144 adult patients managed at a tertiary care service in the UK Methods: Repeated swept-source ASOCT imaging was performed pre- and post-pupil dilation (i.e. 4 scan sets). Inflammation was quantified using a validated human in the loop automated image analysis pipeline, Minuscule Cell Detection (MCD), which identified and counted putative inflammatory cells on AS-OCT. Main Outcome Measures: Test-retest variability of ASOCT and diagnostic accuracy of various ASOCT derived measurands (minimum, maximum, median counts per cross sectional image, and total counts across volume image sets per eye or MINCC, MAXCC, MEDCC and TOTCC) versus Standardization of Uveitis Nomenclature (SUN) grading system as assessed by a uveitis specialist. Results: A total of 281 eyes were included in the analysis. Median age was 48 years (IQR 36 to 64). Strong test-retest measurand reliability was demonstrated, with a 95% tolerance interval ratio 0.3 to 3.0. The best diagnostic performances for SUN activity were observed with the MINCC threshold of 3 particles (negative predictive value for clinical activity of 89.8%, 95% CI 83.0 to 94.1). Associations between ASOCT measurands and patient age (adjusted coefficient 7.5 additional particles, 95% CI 0.5 to 14.6, p<0.04 for age over 60 years versus under 44), and pigment load (52.8, 11.8 to 92.9, p<0.01 in eyes with AC pigment versus without) were noted. Conclusions: ASOCT assessment of anterior chamber inflammation in uveitis meets current recommendations for quantitative imaging biomarkers, demonstrating strong repeatability, linearity with clinical assessment scores and stability with pupil dilation and patient characteristics of ethnicity and lens status. The absence of variability in diagnostic indices across derived measurands suggests similar performance across different acquisition protocols. Further longitudinal cross-platform studies are needed to determine limitations of use.

Abstract Background. Quantitative optical coherence tomography (OCT) measurements are essential for retinal disease monitoring, yet leading vendors store acquisition data in undocumented proprietary formats or encode measurements exclusively in private DICOM tags inaccessible to open systems. Methods. We present Transducin, an open-source Python library that reverse-engineers the undocumented Optopol Revo FC130 and Revo 60 .OPT binary format and extracts quantitative measurements from Zeiss Cirrus HDOCT private DICOM tags, generating TID 1500 Structured Reports with SNOMEDCT coded findings for both platforms. A novel finding, that OCTPARAMS tag 23 encodes ocular laterality through the arithmetic sign of the foveal horizontal position, enables geometry based laterality inference requiring no operator data entry, validated across 18 files from two device models and four software versions with 100% accuracy. Results. The primary corpus of 452 Optopol .OPT files (73 patients, 7 acquisition types) was parsed with 100% success. Cross-version compatibility was confirmed across SOCT versions 11.5.0 through 21.5.0, spanning approximately eight years of software development. The Zeiss Cirrus pipeline generated TID 1500 SRs for all 41 applicable studies (100%), yielding CMT 203to 630um and RNFL 53 to123 um across a clinically representative range. Conclusions. Transducin provides the first publicly documented specification of the Optopol .OPT format and the first open-source multivendor pipeline generating SNOMEDCT coded DICOM Structured Reports from both Optopol Revo and Zeiss Cirrus devices, closing a gap explicitly confirmed by both manufacturers' own documentation. The code is available at https://github.com/oftalmos-org/transducin (Apache License 2.0).

Glaucoma is a leading cause of irreversible blindness worldwide. Ophthalmologists diagnose glaucoma through a structured reasoning process by sequentially evaluating optic nerve head characteristics before reaching a final diagnosis, whereas existing AI systems typically perform direct image classification without providing clinically meaningful reasoning. We present the first clinically annotated fundus reasoning dataset, comprising 1,077 fundus photographs paired with expert-authored six-step diagnostic reports. Building on this dataset, we develop a reasoning-driven vision-language framework that explicitly models the ophthalmologist's diagnostic workflow by generating structured clinical reasoning prior to diagnosis. The generated reports are clinically validated, achieving the best performance across all evaluated clinical findings, including a cup-to-disc ratio mean absolute error of 0.070, an ISNT Kendall distance of 1.73, and the highest semantic agreement with expert reports (BERTScore-F1 = 0.874). The resulting framework also improves glaucoma diagnosis, achieving a balanced accuracy of $94.7\%$ and precision of $94.8\%$, demonstrating that explicitly modeling expert clinical reasoning simultaneously improves interpretability and diagnostic performance. Code and data are available at \url{https://glaucoma-cot.github.io/}.

Purpose: To evaluate the performance of secure cloud-based large language models (LLMs) in extracting glaucoma diagnosis, type, and severity from free-text clinical notes in the electronic health record (EHR). Design: Retrospective chart review analysis. Participants: 1,250 subjects from the Bascom Palmer Ophthalmic Repository. Methods: Clinical notes of glaucoma-related encounters between 2014 and 2024 were extracted from the Bascom Palmer Ophthalmic Repository. Two fellowship-trained glaucoma specialists annotated clinical notes for glaucoma presence, type, and severity at the eye level. The dataset was split into development (10%), validation (10%), and test (80%) sets. Development and validation sets were used for prompt engineering and refinement, and the held-out test set was used for evaluation. Five LLMs (Claude Opus 4.6, DeepSeek-V3.2, GPT-5.2, Grok 4.1, and Qwen3.6-35B-A3B) were accessed via Azure AI Foundry within HIPAA-compliant containers. Model performance was assessed using standard metrics. Clinician-entered ICD-10 codes were also compared with adjudicated labels. Main Outcome Measures: Gwet AC1, accuracy, sensitivity, specificity, and F1-score. Results: Inter-grader agreement was high for glaucoma detection (Gwet AC1= 0.930 (95% CI: 0.917-0.945), type classification (Gwet AC1= 0.917 (95% CI: 0.904-0.930), and severity staging (Gwet AC1= 0.901 (95% CI: 0.884-0.916). For glaucoma diagnosis, LLMs demonstrated high overall accuracy, with Claude achieving 97.5%, DeepSeek 96.0%, GPT 96.2%, Grok 94.4%, and Qwen 95.5%. F1 scores for glaucoma detection ranged from 95.4% to 98.9% across models. For glaucoma type classification, accuracies were 97.1%, 94.2%, 94.2%, 94.0%, and 94.4% for Claude, DeepSeek, GPT, Grok, and Qwen, respectively. F1 scores for the most prevalent type (POAG) ranged from 96.3% to 98.9%. For severity staging, accuracies were 95.0%, 94.8%, 94.5%, 94.0%, and 95.2%, respectively, with F1 scores ranging from 89.7% to 96.3% across severity categories and models. ICD-10 codes demonstrated substantially lower performance for type and severity staging, with overall accuracies of 89.2% and 58.5%, respectively. Conclusions: Secure cloud-based LLMs accurately extracted glaucoma diagnosis, type, and severity information from free-text ophthalmology notes, achieving performance approaching expert clinician adjudication while substantially outperforming ICD-based phenotyping approaches, particularly for disease severity classification. These findings demonstrate the potential of LLMs to transform unstructured clinical documentation into scalable, research-ready phenotypic data for large-scale glaucoma cohort development and EHR-based ophthalmic research.

Automated glaucoma subtype classification from clinical notes remains clinically unactionable without subspecialty-aligned explanations supporting clinician-facing deployment. We extended our Ci-SSGAN with a GPT-5.2-to-Qwen3-8B teacher-distilled reasoning module, fine-tuning Qwen3-8B on 2,660 de-identified ophthalmology notes using expert-reviewed rationales. On 294 notes, the fine-tuned model achieved ROUGE-L 0.792 and BERTScore F1 0.955, surpassing eight zero-shot comparators including GPT-4o and GPT-4.1, establishing privacy-preserving distillation as a path to interpretable AI.

Clinical relevance: Validated virtual control arms may provide population-level estimates of treatment effect and reduce reliance on untreated control allocations in myopia trials. Background: Untreated single-vision control arms in paediatric myopia efficacy trials are increasingly difficult to justify and retain. Several published models predict untreated childhood axial elongation by region or ethnicity. Here they are implemented unchanged in an open-source tool and validated against an untreated multi-ethnic cohort. Methods: Five published models predicted untreated elongation from baseline age, cycloplegic spherical equivalent, sex, and ethnicity, anchored at baseline axial length (AL) and evaluated at actual follow-up. Predictions were compared with 242 untreated myopic children (Chinese, Vietnamese, Indian) with AL measured at approximately 6 and 12 months, assessing bias, root-mean-square error, and prediction-interval coverage against pre-specified thresholds (bias <0.03 mm; coverage greater than or equal to 0.90). Results: The regional generalised estimating equation (GEE) and meta-regression models reproduced mean East Asian elongation without meaningful bias at 6 months (GEE bias -0.013 mm; equivalence to plus-or-minus 0.03 mm, p = 0.014) and at 12 months (-0.004 mm), although equivalence was not established at 12 months in an underpowered subgroup (n = 71, all Vietnamese; p = 0.068). Older age-only models under-predicted by 0.07 to 0.12 mm. Published individual prediction intervals were too narrow (coverage 0.77): the means were accurate, the individual uncertainty was not. Indian elongation fell between strata and was matched by no existing model. Conclusions: The models reproduce mean untreated East Asian elongation at 6 months, conditional on cohort independence; South Asian children remain unserved by any existing stratum. The tool is a group-level instrument, not an individual predictor, and a transparent unification of published models in open-source code. Its value for estimating treatment effect awaits back-testing against a trial with a known untreated arm, ideally over 24 to 36 months.

Purpose: To develop and validate a temporal deep learning framework for predicting geographic atrophy (GA) progression across multi-year horizons using longitudinal optical coherence tomography (OCT) sequences. Design: Retrospective longitudinal cohort study. Subjects, Participants, and/or Controls: A total of 91 patients with dry age-related macular degeneration (AMD) were identified from Wake Forest University School of Medicine (2013-2023), yielding 455 OCT volumes. Two prediction cohorts were defined: 32 patients with no GA (NGA) at baseline who subsequently developed GA, and 35 patients whose earliest GA manifestation was non-central GA (NCGA). Non-progressing patients served as negative controls. Methods: OCT B-scan volumes were encoded into visit-level feature representations using three pretrained architectures (ResNet-18, ResNet-50, ViT-B/16). Chronologically ordered visit embeddings, optionally augmented with inter-visit time intervals ({Delta}t), were processed through recurrent neural networks (RNN), long short-term memory networks (LSTM), and Transformer encoders to model longitudinal disease trajectories. Models were trained and evaluated independently for prediction horizons of 2, 3, 4, 5, and 6 years using patient-level stratified splits (80/20). Performance was assessed across five random seeds. Main Outcome Measures: Area under the receiver operating characteristic curve (ROC-AUC), F1-score, and accuracy for predicting two clinically critical transitions: NGA to GA onset and NCGA to central GA (CGA) involvement. Results: For NGA to GA prediction, models achieved ROC-AUC of 0.84-0.94 at 2-4 years and 1.00 at 5-6 years. For NCGA to CGA prediction, Transformer-based models achieved peak AUC of 0.95 at 4 years and 0.96 at 5 years. Longer input sequences (8 visits vs. 4 visits) consistently improved NCGA to CGA performance at extended horizons. Temporal interval encoding improved stability in several LSTM configurations.

Objectives: Amblyopia is a pediatric visual disorder traditionally treated by patching the fellow eye, though many patients retain residual amblyopia post-treatment. Increasing evidence suggests that visual plasticity allows treat-ment beyond the classical therapeutic window. AMBER evaluated the efficacy of binocular serious games in virtual reality (VR) in residual amblyopia. Methods and Analysis: The monocentric, prospective, randomized, crossover trial (reported as case series) includ-ed 14 anisometropic, strabismic, or mixed residual amblyopia patients (6-35 years; 5 children, 9 adults). Participants underwent two 2-month intervention phases: optical correction (standard care) and standard care plus VR games (2.5 h/week), each with a 2-month follow-up. Best-corrected visual acuity (BCVA), stereoacuity, and reading speed were assessed (5 timepoints) using the Sloan and Landolt charts, the Titmus, TNO, Lang II, Asteroid, and Mnread tests. Compliance and adverse events (AE) were recorded. Results: VR training improved BCVA in 10 amblyopic eyes (Landolt and Sloan), with more pronounced effects in anisometropic patients. Six patients showed improved stereoacuity (Titmus; 4x mixed, 1x anisometropic, 1x stra-bismic amblyopia), persistent only in children (1x strabismic, 1x mixed amblyopia). Four improvements were ob-served with TNO (1x), Lang II (1x), Asteroid (0x), and MNread (1x). Despite positive trends, when comparing re-sults of individual patients, between both eyes, and with standard treatment, consistency of improvements cannot be conclusively demonstrated. One non-severe AE (dizziness) was reported. Conclusions: Following individual cases, VR training improved BCVA and stereoacuity, particularly in children and patients with high compliance. However, considering the cohort as a whole, consistency of effects has to be confirmed in larger groups. Thus, the methodologically sophisticated AMBER study revealed differences in VR treatment efficacy between amblyopia types, children/adults, endpoints and tests, offering precious data for the design of meaningful future studies. It shows that neurovisual plasticity gauged by VR-games offers safe, engaging treatment options for residual amblyopia.

Abstract Importance: Access to eye care is increasingly constrained by declining ophthalmologist workforce density, particularly in rural areas, while optometrist workforce is projected to exceed demand. Numerous state legislatures have expanded optometrist scope of practice (SOP), but the workforce effects of these policies have not been evaluated.. Objective: To evaluate changes in optometrist and ophthalmologist workforce density following state-level expansion of optometrist scope of practice. Design: This retrospective ecological study analyzed workforce density across 50 states and the District of Columbia. Between 2008 - 2019, nine states expanded SOPs for optometrists. We used interrupted time-series regression to estimate associations between these policy changes and provider density from 2010-2021, adjusting for covariates. Setting: Population-based analysis of all 50 US states and the District of Columbia, 2010 - 2021. Participants: State-level workforce data were derived from Bureau of Labor Statistics and US Census data (optometrists) and the American Medical Association Physician Masterfile (ophthalmologists). Socioeconomic covariates were obtained from the American Community Survey. Exposure: State-level legislative expansion of optometrist SOPs to allow injections beyond anti-anaphylaxis treatment, lesion removal, and/or laser procedures. SOP changes were identified through systematic review of legislative and regulatory records. Main Outcomes and Measures: Annual change in optometrist and ophthalmologist workforce density (providers per 100,000 population) following SOP expansion, adjusted for age, income, rates of vision difficulty, diabetes, poverty, and uninsured status. Results: Nine states met inclusion criteria for SOP expansion between 2008 and 2019. Analysis of national data showed that among all 50 US states and the District of Columbia, SOP expansion was associated with a non-significant change in optometrist density (-0.65 per 100,000; 95% CI, -1.87 to 0.57) and ophthalmologist density (+0.09; 95% CI, -0.10 to 0.28). Results were consistent in the 9-state subgroup (optometrists: -0.60, 95% CI -2.90 to 1.70; ophthalmologists: +0.02, 95% CI -0.13 to 0.18). Conclusions and Relevance: Our population-level data suggest that, in the US, state legislation expanding optometrist scope of practice has not been associated with increased workforce density. As numerous state legislatures continue to consider such policies, these findings can inform efforts to balance access to eye care with patient safety.

Germline pathogenic or likely pathogenic variants (GPVs) in BRCA-1 Associated Protein 1 (BAP1) are associated with a spectrum of tumors, including uveal melanoma (UM). Currently, UM patients with BAP1 GPVs are treated as high-risk class 2 tumors based on mostly empiric data. In the current study, we examined the clinical phenotype of a cohort of 29 UM patients with BAP1 GPVs. We also carried out a systematic review of the literature of UM patients with BAP1 GPVs. We observed that UM patients with BAP1 GPVs have significantly lower median age of diagnosis compared to median age reported in UM patients in the Surveillance, Epidemiology, and End Results Program (SEERS) database. Metastatic risk and overall survival in the UM BAP1 GPVs cohort were statistically significant from those in patients with class 1 tumors, but were comparable to those observed in UM patients with class 2 tumors. In UM BAP1 GPVs treated with radiation (n=12), no secondary cancers were observed in the field of radiation in a median 26.5 months (range, 4-119 months) follow up period. One patient experienced a separate growth of UM at a distinct location within the same eye. These data support managing UM in patients with BAP1 GPVs as aggressive class 2 tumors, following the currently established standard of care for these high-risk tumors.

Purpose: To assess the feasibility of quantitative fundus autofluorescence (FAF) measurement in early age-related macular degeneration (AMD) using the freely available ImageJ software, to characterize signal intensity across FAF patterns, and to evaluate interobserver reproducibility in pattern classification. Methods: Single-center, non-blinded, retrospective, consecutive-case analytical study. FAF images acquired with Spectralis OCT+HRA (Heidelberg Engineering) from patients with early dry AMD seen at a tertiary referral center between January 2010 and September 2016 were analyzed. A standardized 300x300-pixel region of interest (ROI) centered on the fovea was evaluated in ImageJ v2.0.0-rc54/1.51h (Fiji distribution). Mean, minimum, and maximum autofluorescence (AF) pixel intensity were recorded. Each image was independently classified according to the Bindewald classification system by two graders; a third senior grader adjudicated discordances. Cohen's kappa (k) was used to assess interobserver agreement. Results: Of 423 patients with available FAF studies, 107 had dry AMD; 45 met quality and diagnostic criteria for early AMD and were included in the quantitative analysis. Mean age was 73.47 +/- 8.1 years; 62.2% were female. Mean FAF intensity was 120.26 (range 74.76-160.79); mean minimum was 32.07 (range 3-63) and mean maximum was 205.80 (range 125-255). Seven of eight Bindewald patterns were identified; the stippled pattern was absent. The most frequent pattern was minimal changes (31.1%), followed by increased focal (24.4%) and patchy (15.6%). Reticular pattern showed the highest mean AF (143.8), while lace pattern showed the lowest (88.4). Interobserver agreement for Bindewald pattern classification was almost perfect (k = 0.969; 95% CI, 0.908-1.000; p < 0.001). Agreement for lesion extent was moderate (k = 0.531) and for foveal involvement was substantial (k = 0.622). Conclusions: Quantitative FAF evaluation of early AMD using ImageJ is feasible and reproducible. ImageJ represents a cost-free alternative for multimodal retinal image analysis, with potential for automated screening applications in resource-limited settings. Keywords: age-related macular degeneration; fundus autofluorescence; ImageJ; quantitative autofluorescence; image analysis; Bindewald classification; interobserver agreement

Background/Aims: Neovascular glaucoma (NVG) is a severe, secondary glaucoma. This study aimed to identify factors associated with vision, intraocular pressure (IOP), and ocular pain outcomes. Methods: The cohort included all patients diagnosed with NVG during 2008-2024 at Helsinki University Hospital, Finland. Linear mixed-effects models used pre-specified covariates, whereas machine learning was given the full longitudinal data with biomicroscopic findings as an exploratory approach. Results: 626 patients were analysed. Worse baseline vision and a closed angle were associated with worse follow-up vision. Treatments were associated with lower IOP and less pain rather than better vision. Age, sex and comorbidity were largely not associated with the outcomes. Glaucoma drainage devices showed the greatest initial IOP reduction (-10.2 mmHg, 95% confidence interval, CI -11.9 to -8.6 mmHg), followed by transscleral cyclophotocoagulation (TSCPC, -4.7 mmHg, 95% CI -5.8 to -3.7 mmHg) and peripheral retinal cryotherapy (-2.2 mmHg, 95% CI -3.1 to -1.4 mmHg). TSCPC and cryotherapy were also associated with reduced pain (odds ratio 0.51 and 0.46). Pan-retinal photocoagulation and anti-VEGF showed smaller IOP reductions, with a pain reduction for pan-retinal photocoagulation only. Both methods agreed, and machine learning added no novel clinical findings. Conclusions: Vision in this cohort was largely set by the state of the eye at diagnosis. IOP control and pain relief therefore remain realistic goals even when sight cannot be saved. Peripheral retinal cryotherapy stood out, linked to both lower IOP and less pain, seldom reported in NVG. These associations from a large, unselected cohort identify treatments worth comparing prospectively.

Purpose: To determine whether disease-aware adversarial perturbations can reduce demographic recoverability encoded in color fundus photographs (CFPs) while preserving glaucoma-related diagnostic features. Design: Retrospective analysis of a single-institution retinal imaging dataset using adversarial machine-learning experiments. Participants: A total of 4,271 patients contributing 13,959 CFPs from Massachusetts Eye and Ear. Methods: Vision Transformer (ViT) was trained for glaucoma detection and for prediction of race, sex, and ethnicity. Standard and disease-aware (DA) variants of four adversarial attacks--Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), Carlini & Wagner (C&W), and a diffusion-based attack--were applied to suppress demographic prediction; DA attacks augmented the adversarial objective with a disease-preservation term. Cross-architecture transferability was assessed by generating perturbations on ViT and applying them to ResNet50 and EfficientNetB0. Main Outcome Measures: Area under the receiver operating characteristic curve (AUC) and accuracy for glaucoma and demographic classification before and after perturbation, and disease-preservation and attack transferability across architectures. Results: At baseline, CFPs encoded both glaucoma-related and demographic information. Glaucoma detection AUCs were 0.958 (95% CI, 0.949-0.967), 0.960 (95% CI, 0.951-0.967), and 0.963 (95% CI, 0.955-0.971) in the race, sex, and ethnicity analysis cohorts, respectively. Demographic prediction performance was also high, with AUCs of 0.955 (95% CI, 0.945-0.963) for race, 0.983 (95% CI, 0.977-0.988) for sex, and 0.992 (95% CI, 0.987-0.996) for ethnicity. Standard attacks substantially reduced demographic AUC but often degraded glaucoma detection. Disease-aware optimization improved disease preservation while maintaining demographic suppression. Using a prespecified success criterion of at least 90% disease AUC preservation and demographic AUC reduction to 30% or less of baseline, DA-PGD and DA-Diffusion succeeded across race, sex, and ethnicity; DA-C&W succeeded for sex and ethnicity. Cross-architecture transferability experiments demonstrated that disease preservation transferred more robustly than demographic suppression. Conclusions: Disease-aware adversarial perturbations reduced the recoverability of demographic information in CFPs under white-box conditions while preserving glaucoma-relevant features, suggesting these representations are partially separable. Reduced demographic recoverability did not fully transfer across architectures, highlighting the need for architecture-agnostic methods.