Ophthalmology Science

Objective: To define CSC genetic architecture and identify implicated ocular tissues, cell types, genes, and circulating proteins. Data Sources: Genome-wide data were assembled from FinnGen, All of Us, Mass General Brigham Biobank, Million Veteran Program, and a Dutch chronic CSC cohort. Serum protein quantitative trait loci, human single-cell ocular atlases, and UK Biobank macular optical coherence tomography (OCT) imaging were used for downstream analyses. Study Selection: Five European-ancestry cohorts with genome-wide data and cohort-specific CSC case-control definitions were included, comprising 2,584 cases and 1,044,455 controls. Variants present in at least 2 cohorts were meta-analyzed. Data Extraction and Synthesis: Cohort-level GWASs were adjusted for age, age squared, sex, genotyping array or batch, and 10 genetic principal components, then combined using fixed-effects inverse-variance meta-analysis. Post-GWAS analyses included gene prioritization, colocalization, Mendelian randomization, single-cell disease-relevance scoring, and testing of a CSC genetic risk score in UK Biobank OCT images. Main Outcome(s) and Measure(s): Genome-wide significant CSC loci, effector genes and proteins, tissue and cell-type enrichment, and CSC-relevant OCT abnormalities. Results: Across 11,068,938 variants, 10 loci reached genome-wide significance (P < 5e-8), including 3 novel loci near TGFB1, LINC00551, and LOC105375630 and 7 replicated loci near CFH, CD46, NOTCH4, PREX1, PTPRB, GATA5, and TNFRSF10A. Integrative analyses prioritized 10 candidate effector genes. Colocalization and Mendelian randomization implicated circulating TNFRSF10A, TGFB1, and CASP10 levels. Single-cell analyses localized genetic risk to sclera (P = 2.0e-4) and vascular endothelial cells (P = 4.0e-4), with fibroblast enrichment. In UK Biobank, OCT abnormalities were more frequent in the top vs bottom 1% of CSC genetic risk (18 of 109 [16.5%] vs 8 of 134 [6.0%]; odds ratio, 4.05; 95% CI, 1.65-10.87; P = .002). Conclusions and Relevance: In this GWAS meta-analysis, CSC susceptibility localized predominantly to scleral and vascular biology rather than primary retinal pigment epithelial dysfunction. These findings support CSC as a sclerovascular disorder and nominate complement regulation, endothelial signaling, and extracellular matrix pathways for future study.

Purpose To investigate the relative efficacy of nine distinct visual field (VF) denoising artificial intelligence (AI) methods and a pathology-aware AI strategy to discourage over-correction of glaucomatous defects. Design Retrospective study. Participants 87,940 paired visual field (VF) and optical coherence tomography (OCT) samples from a tertiary academic center. Methods Denoising models were trained on a separate VF-only dataset and evaluated on an independent structure-function dataset of paired VF-OCT samples. We implemented and evaluated nine distinct VF denoising strategies representing three broad categories: baseline measurements, self-supervised and image restoration models (including Noise2Noise, Noise2Void, and NAFNet), and latent variable compression-based models (autoencoders and variational autoencoders). All models were designed to reconstruct VF sensitivity maps. We then predicted retinal nerve fiber layer thickness (RNFLT) maps from the denoised VFs using a fixed, independently trained VF-to-RNFLT prediction model. Main Outcome Measures Predicted VF and RNFLT maps and resultant evaluation metrics. Results The raw VF baseline achieved a global R2 of 0.5468 and MAE of 16.83 um. Restoration-based models maintained or slightly improved concordance, with the pathology-aware NAFNet achieving the highest global R2 of 0.5485 and a comparable MAE of 16.82 um. In contrast, compression-based models degraded concordance, with CNN-VAE showing a significant reduction (R2 approximately 0.50). In severe glaucoma, concordance decreased across all methods; however, compression architectures exhibited disproportionately greater degradation compared with restoration-based approaches. Conclusions We present a comparative benchmark of AI-based VF denoising strategies paired with structure-function evaluation. While restoration-based models can reduce variability without loss of biological signal, latent compression risks attenuating clinically meaningful defects. Visually smoother fields are not necessarily more biologically accurate.

Purpose: To develop and evaluate a deep learning model that predicts optical coherence tomography (OCT)-equivalent retinal nerve fiber layer thickness (RNFLT) maps directly from color fundus photographs and to assess their diagnostic value for glaucoma detection. Design: Retrospective model development and evaluation study. Participants: 15,031 paired fundus photographs and spectral-domain OCT scans collected at Massachusetts Eye and Ear between 2011 and 2022. Methods: Paired fundus and OCT images were used to train a U-Net-based model to predict pixel-wise RNFLT maps with artifact-corrected supervision. Diagnostic performance was evaluated across single-modality models (fundus photos only, real RNFLT maps, predicted RNFLT maps) and multimodal fusion models (fundus + predicted RNFLT maps). Stratified analyses examined model performance across glaucoma severity and demographic subgroups. Glaucoma was defined based on standard criteria applied to Humphrey 24-2 visual field testing. Main Outcome Measures: Mean absolute error (MAE) and structural similarity index (SSIM) for RNFLT map prediction. Area under the ROC curve (AUC) and accuracy for glaucoma detection. Results: RNFLT map prediction achieved a MAE = 15.4 m and a SSIM = 0.65, measured against artifact-corrected RNFLT maps derived from OCT. For glaucoma detection, the predicted RNFLT-only classifier outperformed the fundus-only classifier (AUC 0.889 vs 0.883, p < 0.005; Accuracy 82.0% vs 78.0%), but performed worse than the real-RNFLT-only classifier (AUC 0.889 vs 0.903, p < 0.005). Multimodal fusion of fundus images with predicted RNFLT maps improved performance, achieving an AUC of 0.909, outperforming all single-modality inputs (p < 0.005 vs fundus-only, predicted-RNFLT-only, and real-RNFLT-only). Performance gains between the fundus-only and the multimodal classifier were greater in early-stage glaucoma compared to severe cases: accuracy increased from 55.3% to 64.0% in mild cases, from 71.5% to 80.4% in moderate cases, and from 90.0% to 94.6% in severe cases. Conclusions: Predicted RNFLT maps derived from fundus photographs provide quantitative, OCT-like structural information and improve glaucoma detection. Unlike prior work that predicted only summary RNFLT values, our model generates full RNFLT maps that better support glaucoma classification than fundus images alone. This approach offers a scalable pathway for early glaucoma screening and expands diagnostic access in resource-limited settings.

Manufacturer-defined signal-strength indices are frequently employed as quality benchmarks for automated optical coherence tomography analysis, yet their empirical relationship with deep learning segmentation accuracy remains unclear. Because these metrics were originally developed for conventional image-processing pipelines, their ability to predict modern model-based segmentation accuracy has not been empirically validated. To address this gap, we evaluated the Heidelberg Spectralis Q-score against U-Net segmentation performance across 5,047 B-scans from 103 eyes for three anatomical boundaries of the posterior segment of the eye: the Ellipsoid Zone (EZ), Bruch's Membrane (BM), and Choroid Outer Boundary (COB). Alongside standard boundary agreement metrics (MAE, MSE, Dice Similarity Coefficient), we adapted the Earth Mover's Distance (EMD) from optimal transport theory as a boundary evaluation metric. Unlike column-wise averages, EMD quantifies boundary agreement as a 2-D geometric displacement, directly measuring residual spatial displacement between the model segmented boundary and the ground-truth boundary. Our results demonstrate that the Q-score - originally designed to gate image-processing-based automated analysis - is a poor predictor of deep learning boundary segmentation accuracy, with explained variance (R2) failing to exceed 1.4% across all three boundaries. We further observed a monotonically increasing error hierarchy with anatomical depth (EZ < BM < COB), consistent across metrics, which is unexplained by the signal strength. At the COB, correlations were paradoxically positive, explained by a B-scan-level mediation chain: higher Q-scores correspond to greater choroidal thickness (r=0.113, {rho}=0.158), which in turn predicts higher COB segmentation error (r=0.165, {rho}=0.191) - a localization difficulty that global signal strength cannot capture. Collectively, these findings challenge the implicit assumption that signal-strength-based quality thresholds are a reliable proxy for deep learning model performance, and motivate a shift toward task-specific acquisition quality criteria calibrated to model performance rather than signal interpretability.

Purpose: Psychological distress is highly prevalent in glaucoma and is associated with worse adherence, reduced quality of life, and faster disease progression. However, distress is rarely assessed in ophthalmology settings due to time, workflow, and staffing constraints. We evaluated two artificial intelligence (AI)-based screening strategies, designed to efficiently identify distressed primary open angle glaucoma (POAG) patients during routine care, aiming to achieve effective, resource conscious, low burden clinical screening. Design: Hybrid retrospective cohort and prospective cross-sectional study. Participants: The retrospective cohort included >3,000 POAG patients from the Duke Ophthalmic Registry. Prospective validation was conducted in a separate 300 POAG patient cohort who completed patient-reported distress screening. Methods: Using retrospective data, a neural network model was trained to predict an electronic health record (EHR)-derived computable phenotype of distress ("silver standard"). Prospective validation used the 8-item Patient Health Questionnaire (PHQ-8) as the "gold standard." Three screening strategies were compared against PHQ-8: (1) universal PHQ-2 screening (two-item screener administered to all patients), (2) AI-only screening (fully automated EHR-based screener), and (3) sequential screening, (only patients flagged as high risk by AI screener completed the PHQ-2). Performance metrics included sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and screening burden. Main Outcome Measures: Sensitivity; specificity; PPV; NPV; accuracy; proportion of patients requiring secondary screening (screening burden). Results: Distress prevalence was 17% (PHQ-8 > 6). Universal PHQ-2 screening (> 0) achieved high sensitivity (0.96) but lower specificity (0.73) and PPV (0.41), while requiring screening of all patients. The AI-assisted sequential approach substantially reduced screening burden while maintaining strong diagnostic performance. By administering PHQ-2 to ~25% of patients, sequential screening achieved sensitivity 0.64, specificity 0.93, PPV 0.64, NPV 0.93, and accuracy 0.88, representing a ~50% increase in PPV compared to PHQ-2 alone. AI-only screening reduced burden further but did not achieve comparable sensitivity or predictive performance. Conclusions: AI-assisted sequential screening enables scalable, resource efficient identification of psychological distress in glaucoma care, substantially reducing screening burden while preserving clinically meaningful performance. This framework offers a practical pathway for integrating distress screening into routine ophthalmology workflows and improving the identification and referral of at-risk patients.

Purpose: To predict retinal nerve fiber layer thickness (RNFLT) norms from fundus images. Methods: We selected 18,000 OCT scans and visual fields (VF) from the Massachusetts Eye and Ear Glaucoma Service. A U-Net-based deep learning model was developed to predict RNFLT norms from OCT en face fundus images. A total of 10,000 OCT scans with normal VFs (mean deviation [MD] [&ge;] -1 dB, glaucoma hemifield test within normal limits, and pattern standard deviation probability > 5%) tested within 30 days were used for training, while the remaining 8,000 OCT scans (mean VF MD: 3.3 +/- 4.9 dB), including 2,419 scans with normal VFs, were used for evaluation. Structure-function correlations between RNFLT maps and VFs were assessed using linear regression and VGG-16 across original RNFLT maps, deviation maps, and their combination. Performance was evaluated using correlation coefficients, mean absolute error (MAE), and R-squared. Results: Predicted RNFLT norm maps showed agreement with baseline RNFLT maps in eyes with normal VFs (R-squared = 0.81 +/- 0.13). RNFLT deviation maps correlated more strongly with VF MD than original RNFLT maps (R = 0.42 vs. 0.19, p < 0.01). In deep learning-based VF prediction, combining original and deviation maps achieved the best performance (MAE = 3.31 dB, R-squared = 0.39), outperforming the model (p < 0.05) using original RNFLT maps alone (MAE = 3.36 dB, R-squared = 0.35). Conclusions: Deep learning can estimate individualized RNFLT norms and improve structure-function assessment in glaucoma. Translational Relevance: Personalized RNFLT norm prediction may improve detection of glaucomatous damage.

Purpose: To evaluate the safety and exploratory outcomes of a single intravitreal injection of OGX110, a peptide agonist of CXCR3, in eyes with persistent fluid secondary to neovascular age-related macular degeneration (nAMD) despite ongoing anti-vascular endothelial growth factor (anti-VEGF) therapy. Methods: This prospective, open-label, sequential dose-escalation phase I study (NCT05904691) enrolled subjects receiving standard-of-care intravitreal anti-VEGF therapy. Subjects received a single intravitreal injection of OGX110 at 0.5 mg, 1.0 mg, or 2.0 mg (n=3 per cohort), 7 to 14 days after the anti-VEGF injection. Results: All nine enrolled subjects completed follow-up through day 56. Two subjects (22%) experienced at least 1 adverse event (AE); all were mild and unrelated to study treatment. Exploratory analyses showed a BCVA change of +1.4 letters following anti-VEGF injection and +4.4 letters from OGX110 baseline to 4 weeks (P < 0.05). Six of 9 subjects gained at least 3 ETDRS letters after OGX110. Anatomic responses were heterogeneous. Four eyes showed a reduction in CRT after anti-VEGF injection that was maintained after OGX110 administration. One additional eye demonstrated a substantial reduction in CRT after OGX110 despite minimal response to anti-VEGF treatment. Conclusions: A single intravitreal injection of OGX110 was well tolerated. Exploratory functional and anatomic findings suggest biologic activity; interpretation is limited by small sample size, open-label design, absence of a concurrent control group, and inter-subject heterogeneity. These results support further study in a controlled trial. Translational Relevance: OGX110 represents a mechanistically distinct investigational approach for nAMD that may warrant further evaluation in eyes with persistent.

The retinal nerve fiber layer, composed of axon bundles converging toward the optic nerve, is a key biomarker for diagnosing and monitoring glaucoma and other neurodegenerative diseases. High-resolution en face imaging of individual nerve fiber bundles offers morphological information beyond what conventional optical coherence tomography provides, yet clinical integration remains limited by the lack of automated analysis tools and normative data. Here, we imaged 14 healthy volunteers using time-domain full-field optical coherence tomography and adaptive optics scanning laser ophthalmoscopy, and developed automated pipelines to quantify bundle width, trajectory, tortuosity, and orientation. Bundles were on average 25% wider at shallower retinal depths, width measurements were consistent across imaging modalities, and estimated axon count per bundle decreased significantly with age. Global trajectory analysis revealed systematic deviations of high resolution data from existing mathematical models, particularly in the temporal sector, leading us to propose two refined trajectory models. These normative results provide a foundation for high resolution biomarkers for use in investigations of retinal neurodegeneration.

Purpose: To characterize peripheral immune alterations in treated birdshot uveitis (BU) patients using high-dimensional mass cytometry and multiplex serology. Design: Cohort study. Subjects: 36 BU patients on immunomodulatory treatment (IMT) and 31 healthy controls (HCs). Methods: Detailed ophthalmologic examinations were performed, and peripheral blood and serum samples were collected for immune profiling using mass cytometry and multiplex cytokine analysis. Main Outcome Measures: Imaging-based indicators of ocular inflammation; peripheral immune cell frequencies; serum cytokine levels. Results: Compared to HCs, BU patients showed increased frequencies of Th17, CD146+ T cells, intermediate effector/central memory T cells co-expressing CXCR3 and CCR4, CD56dim NK cells and elevated IL-18 levels. Patients were clinically stratified by an expert ophthalmologist into three disease activity groups: Inactive, Active (comprising combinations of surface retina, deep retina and choroid activity) and Burned-out. Inactive patients harbored more quiescent effector T cells, e.g. Tim-3+ Tc17-Tc22 intermediates and more CD8+ TSCM, potentially representing a resting pool of autoimmune T cells. Active patients exhibited increased in vivo activation of relevant T cells, with stronger HLA-DR, CD38 or PD-1 expression, and highest levels of CD56dim NK cells. Immune profiles were also linked to treatment subgroups: csDMARDs (conventional synthetic disease-modifying antirheumatic drugs) were associated with higher CD56bright NK frequencies, and absence of therapy showed elevated PD-1/SLAMF7 Tc17+1 and PD-1CD57 CD8 TEMRA cells. IL-6R blockade (tocilizumab) resulted in loss of IL-6R T-cells accompanied by increased SLAMF7 T cells, due to epitope masking. Conclusions: Peripheral CyTOF profiling anchored to thorough clinical stratification revealed disease activity-associated immune signatures and therapy-associated imprints in BU.

Age-related macular degeneration (AMD) shows substantial clinical heterogeneity that remains unexplained despite extensive genetic and clinical characterization. We evaluated whether proteomic stratification could provide insight beyond clinical phenotype and genetic risk. We performed 384-plex plasma proteomics in a cohort of 215 individuals, including patients with early and late neovascular AMD, other complement-associated retinal diseases, and age-matched controls. Proteome-based reclassification identified four disease-overarching clusters. Neovascular AMD cases were partitioned almost exclusively between two clusters (30/36). Early AMD cases were predominantly assigned to one of these clusters (10/18), whereas only two localized to the other (2/18). Both AMD-associated clusters shared elevated levels of a protein module enriched for lipoprotein-related functions compared to the other clusters. However, the cluster containing both early and neovascular AMD cases showed higher levels of additional protein modules enriched for complement pathways and cellular stress-response pathways compared with the other AMD-associated cluster. Importantly, this molecular divergence in neovascular AMD could not be explained by genetic predisposition (i.e., 52-variant AMD genetic risk score), signatures of biological ageing, nor by other clinical features. Together, these findings support two proteomic endotypes of neovascular AMD with distinct involvement of cellular stress pathways.

Purpose: To quantify the binocular integrated visual field (IVF) loss patterns with archetypal (AT) analysis and their associations with patients' Quality of Life (QoL). Design: Retrospective study. Participants: Over 125,000 patients from three datasets from Massachusetts Eye and Ear and Glaucoma Research Network Consortium. Methods: We used: (1) the Glaucoma Research Network excluding the Massachusetts Eye and Ear subset for the binocular archetypal model training (77, 270 IVFs from 77 270 patients), (2) Massachusetts Eye and Ear dataset for demographic correlation analysis (47,965 IVFs from 47,965 patients), and (3) the MEE Quality of Life Survey dataset for QoL correlation analysis (75 IVFs from 75 patients). The whole study was restricted to the most recent VF measurements from each subject and binocular VFs were constructed by the integrated visual field method, which was taking the higher sensitivity at each test location. We first applied archetypal analysis to cluster 24-2 binocular VFs into archetypal patterns. The total number of patterns was determined by the Bayes factor. Pearson's correlations analyzed the associations between patients demographic information, binocular VF patterns and QoL scores, and the coefficients were set to 0 if p-values corrected by multiple comparisons < 0.05. Main Outcome Measures: A binocular VF archetypal patterns and its relationships with demographic divergences and QoL. Results: We identified 17 binocular VF loss patterns. Patterns with major vision impairment (AT10, AT12, AT13, AT14, and AT17) were more common in older patients, while Black or African Americans exhibited a broader spectrum of visual loss, notably AT5 and AT12, compared to Asian and White counterparts. 81 MEE patients with QoL survey data was analyzed to investigate the impact of demographic and vision-related variables on QoL. Older age and female gender were significantly associated with lower QoL. Binocular central vision loss (AT 5) and total vision loss (AT 12) had a significantly greater impact on QoL than binocular peripheral vision loss (AT 2, AT 5, AT 16). Conclusions: Individuals with central or total vision loss, as well as certain demographic groups, experience a significantly greater impact on quality of life. The quantifications of binocular VF loss patterns by archetypal analysis may help better understand glaucoma's impact on patients' quality of life.

Purpose: To evaluate the incidence and baseline predictors of intraocular pressure (IOP)-lowering treatment following detection of referable glaucoma by teleretinal screening. Design: Retrospective cohort study. Methods: Participants were derived from a safety-net teleretinal diabetic retinopathy screening program (2013-2024). Participants included individuals who screened positive for referable glaucoma (cup-to-disc ratio [CDR] [&ge;]0.6 or CDR asymmetry [&ge;]0.2) and completed in-office diagnostic evaluation. The primary outcome was initiation of IOP-lowering treatment (medication, laser, or surgery) and the secondary outcome was intervention with surgery. Cumulative incidence functions were estimated, accounting for loss to follow-up. Fine-Gray models were used to identify baseline screening predictors to risk stratify each outcome. Glaucoma diagnosis was approximated using diagnostic codes and chart review. Results: 2,367 participants were included. The cumulative incidence of treatment was 19.6% (95% CI: 18.0-21.2) at Year 1 and 45.1% (42.1-48.1) at Year 8. Early treatment occurred primarily in glaucoma cases, whereas treatment accumulated longitudinally in glaucoma suspects, reaching 36.5% (31.6-41.5) by Year 8. Surgery was less common (8-year incidence: 5.3%). Baseline screening data predicted treatment and surgery, enabling risk stratification. At Year 8, cumulative incidence differed substantially between high- and low-risk groups (treatment: 59.9% vs. 31.2%; surgery: 9.7% vs. 1.0%). Older age (sub-distribution hazard ratio [SHR] 1.03 per year, p<0.001), Black race (SHR 1.50, p<0.001), and personal history of glaucoma (SHR 1.90, p<0.001) were associated with treatment; Asian race was protective (0.71, p=0.03). Older age (SHR 1.06, p<0.001), worse visual acuity (SHR 5.11 per logMAR unit, p<0.001), and screening at a hospital-based site (SHR 2.46, p=0.003) were associated with surgical treatment. Conclusion: Nearly half of safety-net diabetic patients screening positive for referable glaucoma initiated IOP-lowering treatment over 8 years, while few received surgery. Baseline screening characteristics enabled risk stratification of treatment and surgery. These findings address an evidence gap about longitudinal consequences of screening and suggest that its impact extends beyond detection of prevalent glaucoma to include identification of high-risk glaucoma suspects who warrant ongoing surveillance.

Purpose: To evaluate the impact of ''not commonly considered risk factors '' on glaucoma surgical outcomes. Methods: This study included 339 eyes that underwent glaucoma surgery. Surgical procedures included microhook ab-interno trabeculotomy (TLO), Preserflo ab-externo microshunt implantation, trabeculectomy (Trab), and Ahmed Glaucoma Valve (AGV) implantation. In addition to conventional background factors, we examined a set of ''not commonly considered risk factors, '' including very elderly age ([&ge;]85 years), avitreous status, aphakia, use of antithrombotic agents, difficulty attending frequent postoperative visits, small palpebral fissure, corneal endothelial dysfunction, poor vision in the fellow eye, dementia, hearing loss, mental illness, atopic dermatitis, pseudophacodonesis, glaucoma eye drop allergy, and conditions contraindicating {beta}-blocker use. Surgical success was defined as intraocular pressure (IOP) [&le;]21 mmHg, [&ge;]20% reduction from baseline, and no additional glaucoma surgery at 1 year. Logistic regression was performed to identify potential risk factors; significant factors were further evaluated using propensity score matching. Results: Of the 339 cases, surgical success rates were 65% for TLO, 82% for Preserflo, 91% for Trab, and 82% for AGV. Multivariate logistic regression identified two independent predictors of surgical failure: small palpebral fissure (odds ratio 2.52, p < 0.01) and hearing loss (odds ratio 3.94, p = 0.04). Propensity score matching of patients with small versus large palpebral fissures (111 per group) confirmed significantly worse postoperative outcomes in the small-palpebral-fissure group despite balanced baseline characteristics. Conclusion: Small palpebral fissure is an independent and previously unnoticed risk factor for glaucoma surgical failure, affecting both minimally invasive and filtration procedures.

Background/Aims: Neovascular glaucoma (NVG) is an aggressive secondary glaucoma with limited longitudinal data. This study reports the aetiologies, treatments, and longitudinal outcomes in NVG. Methods: Patients with NVG were identified through electronic medical record review. Inclusion required documented rubeosis of the iris and/or anterior chamber angle, intraocular pressure (IOP) [&ge;]25 mmHg, diagnosis during 2008-2024, and follow-up at Helsinki University Hospital. Baseline data and all follow-up visits were included. Results: Of 919 patients identified, 626 met inclusion criteria, with a median follow-up of 24 months. The estimated NVG incidence was 2.2/100,000/year. The most common aetiology was central retinal vein occlusion (CRVO; 45%), followed by diabetic retinopathy (DR; 14%), central retinal artery occlusion (CRAO; 11%), and ocular ischaemic syndrome (8%). Half of patients had hand motion vision or worse at baseline, with 18% at no light perception (NLP). At 5 years, 13% of patients had Snellen 6/60 vision or better. Visual outcomes differed by aetiology, with median time to NLP ranging from 1.6 (CRAO) to 9.1 (DR) years (log-rank p=0.002). Median baseline IOP was 40 mmHg, decreasing to 21 mmHg by 1 year. Ocular pain fell from 43% at baseline to 11% at last follow-up. Structural eye loss (e.g., enucleation or phthisis) occurred in 3% by 5 years. Conclusion: The estimated incidence was lower than previously reported elsewhere. Unlike other cohorts where DR predominates, CRVO was the most common aetiology, and visual prognosis was strongly aetiology-dependent. Glaucoma drainage device surgery reached 7.6% at 3 years, despite the severity and refractory nature of NVG.

The choroid is critical for maintaining vision and implicated in several ocular diseases, being the sole source of nutrients and waste removal for the outer retina. Genetic discovery can help elucidate the pathways through which choroidal features influence disease risk. Our meta-analysis of genome-wide association studies (n= 78,682 participants) identified 30 genomic regions, including 20 novel loci, associated with choroidal thickness. Findings suggest inflammatory and vascular processes drive choroidal thickness, with overlapping mechanisms shared with refractive error. Genome-wide independently significant SNPs accounted for 18.7% of the genetic variance in choroidal thickness. Mendelian randomisation analyses showed a causal effect of age-related macular degeneration on choroidal thickness, and suggest a bidirectional causal effect between choroidal thickness and primary angle-closure glaucoma. These findings provide insight into the shared genetic architecture and biological pathways linking choroidal thickness and related diseases.

Inherited retinal diseases (IRDs) are a heterogeneous group of genetic disorders that cause progressive vision loss. A subset of IRDs is associated with ubiquitously expressed genes involved in fundamental cellular processes, often resulting in multisystem disease. Among these is Zellweger spectrum disorder (ZSD), caused by pathogenic variants in PEX genes required for peroxisome biogenesis and function. There are no proven targeted disease-modifying treatments for ZSD, and it is unclear whether localized restoration of peroxisome function is sufficient to mitigate retinal degeneration. We previously demonstrated that HsPEX1 retinal gene augmentation therapy in a mouse model of mild ZSD homozygous for the murine equivalent (PEX1-p.[Gly844Asp]) of the most common deleterious allele in patients (PEX1-c.[2528G>A], PEX1-p.[Gly843Asp]), improved retinal electrophysiological response. Here, we present a comprehensive, dose-range evaluation of a re-designed, clinically relevant AAV8-delivered HsPEX1 subretinal gene therapy, employing expanded outcome measures. We observed a marked improvement in functional vision, retinal response, photoreceptor structure, retinal pigment epithelium integrity, subretinal inflammation, and peroxisomal metabolites, durable to the endpoint of 6 months post single subretinal injection. These studies provide preclinical proof-of-concept that localized retinal gene replacement can mitigate vision loss in peroxisome-mediated IRD.

Background: Inherited Retinal Diseases (IRDs) are a group of genetically heterogeneous blinding conditions. Major global genomic reference databases are disproportionately enriched for individuals of European ancestry. This underrepresentation creates a significant bias that impedes the accuracy of genetic diagnosis in the Chinese population. This study aims to address this limitation by constructing a comprehensive genetic landscape of IRDs using large-scale deep-sequencing data from a large Chinese cohort. Methods: The study leveraged variant data primarily from 10,588 individuals in the China Metabolic Analytics Project (ChinaMAP) and cross-referenced findings against multiple national and international databases. We systematically curated variants within a targeted panel of 291 IRD-associated genes. Variant pathogenicity was assessed using a comprehensive pipeline integrating InterVar-automated classification based on 2015 American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) guidelines, ClinVar evidence (review status [&ge;] 1 star), and manual literature curation. We delineated the mutational spectrum, identified population-enriched pathogenic/likely pathogenic (P/LP) variants, and analyzed the distribution characteristics of IRD-associated highly-mutated genes. Furthermore, we calculated the carrier frequencies (CF) and genetic prevalence (GP) of autosomal recessive(AR)-IRD genes in the Chinese population. Results: The study revealed a highly concentrated genetic landscape for AR-IRDs in the Chinese population, with ABCA4 and USH2A emerging as the primary drivers of the genetic burden. This finding aligns with previous Chinese cohorts but contrasts with global databases, where genes such as the X-linked RPGR are more prevalent. In contrast, autosomal dominant (AD)-IRDs exhibited high locus heterogeneity, with pathogenic variants dispersed across numerous genes (e.g., COL2A1 and MFN2). We identified a series of P/LP variants that were either high-frequency or significantly enriched in the Chinese population, such as CNGB1 (p.P530R) and specific recurrent alleles in ABCA4 and CYP4V2. The estimated cumulative CF for AR-IRDs was 1 in 5.60, and the theoretical total GP was 1 in 2,624.67, based on the ChinaMAP data. Conclusion: By integrating the ChinaMAP dataset with diverse genomic resources, this study provides a genetic landscape of IRDs in the Chinese population. Our analysis shows a concentrated mutational spectrum in AR-IRDs, contrasting with the pronounced heterogeneity in AD-IRDs. These findings, including population-specific pathogenic variants and refined prevalence estimates, provide a resource for precision diagnostics, genetic counseling, expanded carrier screening (ECS), and public health policy development in China.

Accurate risk stratification of pigmented skin lesions is critical for early melanoma detection and for reducing unnecessary excisions. Artificial intelligence (AI) is increasingly applied to dermoscopic image analysis, but its diagnostic performance relative to standard dermoscopy in real-world clinical settings remains uncertain. To address this gap, we conducted a systematic review and meta-analysis of prospective clinical studies directly comparing AI alone, dermoscopy, and AI-assisted clinicians for malignancy risk assessment of pigmented skin lesions. We systematically searched PubMed, Embase, Web of Science, and Cochrane Library from inception to January 2026. Ten studies with 17 diagnostic arms (10 dermoscopy arms, 6 AI-alone arms, and 1 AI-assisted clinician arm) were included. Pooled sensitivity and specificity were 0.773 (95% CI, 0.648-0.863) and 0.793 (95% CI, 0.673-0.877) for dermoscopy, and 0.757 (95% CI, 0.428-0.928) and 0.859 (95% CI, 0.619-0.958) for standalone AI. Summary ROC curves showed overlapping performance, indicating that autonomous AI is broadly comparable to dermoscopy but does not demonstrate a consistent advantage. Heterogeneity in AI performance was driven almost entirely by threshold effects rather than by differences in inherent model capacity. AI-assisted clinicians showed promising results (sensitivity 1.000, specificity 0.837) in a single study, but more evidence is needed. Our findings suggest that, at present, AI should be viewed as a complementary decision-support tool rather than a replacement for dermoscopic evaluation. The study provides valuable evidence for clinicians, guideline developers, and researchers working on AI integration into melanoma diagnostic pathways.

Background: Store-and-forward teledermatology commonly relies on several patient-submitted photographs of the same concern, but most dermatology artificial intelligence models classify single images independently. Objective: To develop and internally validate a case-level diagnostic-support model that aggregates multiple patient-submitted photographs for common dermatologic conditions. Methods: We conducted a retrospective diagnostic-modeling study using the Skin Condition Image Network, a public dataset of deidentified self-taken dermatology images from US adults. We curated 2,336 cases comprising 5,041 images across 10 common inflammatory, allergic, and infectious conditions. Cases were split at the submission level into training, validation, and held-out test sets. Frozen general-purpose and dermatology-specific encoders were compared with image-level classifiers and a gated-attention multiple instance learning model that generated one case-level output from 1-3 images. Results: The strongest image-level baseline, dermatology-specific embeddings with random forest classification, achieved macro/micro ROC-AUCs of 0.797/0.854. Case-level aggregation improved discrimination, with dermatology-specific embeddings plus multiple instance learning achieving mean macro/micro ROC-AUCs of 0.819/0.863 across repeated stratified experiments. The locked final model achieved macro/micro ROC-AUCs of 0.800/0.849 on the held-out test set. Balanced-threshold sensitivity/specificity examples were 0.702/0.688 for eczema and 0.818/0.826 for urticaria. Limitations: Internal validation used a 10-condition subset from a US volunteer dataset; external validation, calibration, subgroup performance analysis, and prospective workflow studies are required. Conclusion: Modeling the teledermatology submission as a multi-image case better reflects asynchronous dermatology workflow than single-image classification. The model is preliminary clinician-facing support for structured review and triage, not autonomous diagnosis.

Teledermatology expands access to dermatologic expertise in rural settings, yet diagnostic uncertainty persists in low-resource primary care. This retrospective study evaluated MedGemma-4B-IT, a compact multimodal vision-language model, as adjunctive clinical decision support for challenging diagnostic cases. We analyzed 77 zero-concordance cases (360 clinical photographs) from a Dermatology Extension for Community Healthcare Outcomes (ECHO) tele-mentoring program (2016-2021). Zero-concordance cases showed no overlap between primary clinician provisional diagnosis and dermatologist-confirmed diagnosis. The model was prompted using dermatologist-style format to generate ranked differential diagnoses. Performance was assessed using strict case-level top-k exact-match accuracy and relaxed matching criteria based on fuzzy string similarity. MedGemma achieved 0.0% strict top-1 accuracy, 1.3% top-3 accuracy, 3.9% top-5 accuracy, and 3.9% top-10 accuracy. Relaxed concept-level matching achieved 28.6% top-1, 63.6% top-5, and 67.5% top-10 accuracy. Image-level accuracy was 44.2% (159/360, 95% CI 39.0-49.5%). The model surfaced the correct diagnosis within differential lists in 45.5% of cases despite no exact top-1 matches, suggesting utility for differential expansion rather than definitive diagnosis. Performance varied across diagnostic categories, with highest accuracy in Other categories (54.5%) and lowest in neoplastic conditions (0.0%). Common errors included confusion between inflammatory and other diagnostic groupings. These findings characterize MedGemma performance on real-world teledermatology cases and inform safe, clinician-in-the-loop integration into teledermatology workflows where specialist oversight remains essential.