Ophthalmology Science

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

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

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

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

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

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

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

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.

This study evaluated the feasibility of collecting passive and active digital phenotyping data using the OverSight iOS application in individuals with inherited retinal diseases (IRDs), and explored associations between digital behavioural markers, visual function, and mental health. Participants with IRDs were recruited from Moorfields Eye Hospital (UK) and followed for 12 months. OverSight passively captures mobility data through HealthKit and typing-derived metrics through SensorKit. Participants completed patient-reported outcome measures (EQ-5D, NEI-VFQ-25, HADS, and MRDQ) within the app. Passive data included step count, walking speed, typing speed, total words typed, autocorrections, and sentiment word categories (anxiety, down, and health-related terms). Feasibility indices included enrolment, retention, and completeness of passive datastreams. Twenty-five participants were enrolled and 92% were retained at 12 months. Seventeen participants met the validity threshold for HealthKit data and 16 also met SensorKit thresholds. Median daily step count was 6,087, walking speed 1.18 m/s, and typing speed 2.19 characters/s. Age was negatively correlated with typing speed and anxiety-related word use, and photopic peripheral visual difficulty was negatively correlated with anxiety- and down-related word use. Digital phenotyping using OverSight was feasible over 12 months. Exploratory analysis suggest mobility, typing behaviour and sentiment markers may represent useful adjunctive indicators of functional vision and psychological outcomes in patients with IRDs.

PurposeMitochondrial dysfunction contributes to major blinding diseases, including age-related macular degeneration and glaucoma. Although mitochondrial transplantation has shown therapeutic potential in multiple organ systems, translation to the eye remains limited, partly due to uncertainty regarding optimal delivery. We summarize the biologic rationale and preclinical evidence supporting ocular mitochondrial transplantation and present feasibility data evaluating clinically relevant delivery routes. MethodsWe conducted a focused narrative review of ocular mitochondrial transplantation. For feasibility experiments, mitochondria with an endogenous fluorescent dye were isolated from liver donor mice. Postnatal day 7 pups received subretinal injections, and adult CD1 mice received intravitreal injections, including optic nerve head directed delivery. Eyes were analyzed using fluorescence microscopy and immunohistochemistry. Mitochondrial uptake was assessed in cultured retinal pigmental epithelial (RPE) cells using co-incubation assays. Suprachoroidal delivery feasibility was evaluated in cadaveric human near-real surgical specimens using a novel dedicated suprachoroidal injector. ResultsThe literature on ocular mitochondrial transplantation remains limited and consists primarily of small preclinical studies using intravitreal delivery and imaging-based detection. In our experiments, intravitreal delivery produced donor signals predominantly within inner retinal layers, with enrichment along retinal nerve fiber bundles when directed toward the optic nerve head. Cultured RPE cells demonstrated dose-dependent uptake of exogenous mitochondria. Subretinal delivery localized donors signal to the RPE and adjacent outer retina. Suprachoroidal injections demonstrated procedural feasibility with reliable access to the suprachoroidal space and visible injectate distribution. ConclusionsOcular mitochondrial transplantation is in an early stage of investigation. Our feasibility data indicate that established posterior-segment delivery routes expose distinct retinal compartments and that route selection strongly influences anatomic distribution. Further studies are needed to verify intracellular uptake, define dosing and durability, and evaluate safety in disease-relevant models.

Diabetic retinopathy (DR) is a leading cause of vision impairment, requiring accurate and scalable diagnostic tools. Foundation models are increasingly applied to clinical imaging, but concerns remain about their calibration. We evaluated DINOv3, RETFound, and VisionFM for DR classification using different transfer learning strategies in BRSET (n = 16,266) and mBRSET (n = 5,164). Models achieved high discrimination in binary classification (normal vs retinopathy) in BRSET (AUROC 0.90-0.98), with DINOv3 achieving the best under full fine-tuning (AUROC 0.98 [95% CI: 0.97-0.99]). External validation on mBRSET showed decreased performance for all models regardless of the fine-tuning strategy (AUROC 0.70-0.85), though fine-tuning improved performance. Foundation models achieved strong discrimination but poor calibration, generally overestimating DR risk. While the generalist model, DINOv3, benefited from deeper fine-tuning, miscalibration remained evident. These findings underscore the need to improve calibration and the comprehensive evaluation of foundation models, which are essential in clinical settings. Author summaryArtificial intelligence is increasingly being used to detect eye diseases such as diabetic retinopathy from retinal images. Recent advances have introduced "foundation models," which are trained on large datasets and can be adapted to new tasks. We aimed to evaluate how well these models perform in a clinical prediction context, with a focus not only on accuracy but also on how reliably they estimate disease risk. In this study, we compared different types of foundation models using two independent datasets from Brazil. We found that while these models were generally good at distinguishing between healthy and diseased eyes, their predicted risks were often poorly calibrated. In other words, the estimated probabilities did not consistently reflect the true likelihood of disease. We also examined whether adapting the models to the target population could improve performance. Although this approach led to improvements, calibration issues remained. However, post-training correction improved the agreement between predicted risks and observed outcomes. Our findings highlight an important gap between model performance and clinical usefulness. We suggest that improving the reliability of risk estimates is essential before such systems can be safely used in healthcare.

ObjectiveThe objective of this study is to assess the quality, empathy, and readability of large language model (LLM) responses regarding otologic questions from patients as they compare to verified physician responses in other patient-driven forums. This study aims to predict the potential utility of LLMs in patient-centered communication. Study DesignComparative study SettingsInternet MethodsA sample of 49 otology-related questions posted on Reddit r/AskDocs1 between January 2020 and June 2025 were selected using search terms including "hearing loss," "ear infection," "tinnitus," "ear pain," and "vertigo." Posts were retrieved using Reddits "Top" filter. Each question was answered by a verified doctor on Reddit and three AI LLMs (ChatGPT-4o, ClaudeAI, Google Gemini). Responses were scored by five evaluators. ResultsCommon otologic concerns posed in patient questions were otalgia (38.7%), vertigo (28.6%), tinnitus (24.5%), hearing loss (22.4%), and aural fullness (20.4%). LLM responses were longer than physician responses (mean 145 vs 67 words; p < .05) and rated higher in quality (10.95 vs 9.58), empathy (7.26 vs 5.18), and readability (4.00 vs 3.73); (all p < .05). Evaluators correctly identified AI versus physician responses in 89.4% of cases with higher sensitivity for detecting physician responses (93.5%). By Flesch-Kincaid grade level, ChatGPT produced the most readable content (mean 7.25), while ClaudeAI responses were more complex (11.86; p < .05). ConclusionLLM responses received higher ratings in quality, empathy, and readability than those of physicians in response to a variety of otologic concerns. When appropriately implemented, such systems may enhance access to understandable otologic information and complement clinician-delivered care.

Purpose: To determine whether spatial decomposition of longitudinal retinal nerve fiber layer (RNFL) change maps reveals distinct modes of glaucomatous progression masked by conventional averaging, and to validate these modes through structure function mapping and genetic association analysis. Methods: Pixel wise RNFL rates of change were computed from longitudinal optic disc OCT scans of 15,242 eyes (8,419 adults with primary open angle glaucoma [POAG]; Massachusetts Eye and Ear, 1998 to 2023). A loss only constraint zeroed all thickening values, reflecting the biological prior that adult RNFL does not regenerate. Nonnegative matrix factorization decomposed these maps into spatial progression components (80% training set). Components were evaluated in a heldout set (20%) for retinotopic structure function concordance, visual field (VF) progressor classification against global and quadrant RNFL rates, and enrichment of genetic association signals at established POAG loci. Results: Six anatomically distinct progression patterns emerged, including diffuse circumferential loss, focal peripapillary defects, and arcuate bundle degeneration. Pattern based models significantly outperformed global RNFL rate for classifying VF progressors (area under the curve, 0.750 [95% CI, 0.709 to 0.790] vs. 0.702; P = .0096) and explained additional variance in functional decline (Nagelkerke pseudoR2, 0.301 vs. 0.198; P = .0011). Structure function mapping confirmed retinotopic coherence. Spatial phenotypes recovered stronger genetic signals than global rates at 85.3% of established POAG loci, suggesting they capture more biologically homogeneous endophenotypes of progression. Conclusions: Glaucomatous structural progression occurs through spatially distinct modes with independent structure function and genetic signatures that conventional RNFL averaging obscures.

Background: Despite strong evidence from controlled trials, uncertainty remains about the real-world use of 0.05% atropine in patients with lighter irises due to tolerability concerns, and predictors of treatment response are poorly understood. Here, we evaluated the effectiveness, tolerability, and early biometric response to 0.05% atropine in clinical practice among patients with predominantly light irises. Methods: This prospective cohort study included 33 patients treated with 0.05% atropine (82% with light irises). Cycloplegic spherical equivalent refraction (SER) was measured at baseline and 3-month intervals. Axial length (AL), photopic pupil diameter, accommodation amplitude, and subjective side effects were monitored more frequently initially. Results: Median age at treatment initiation was 11.97 years, SER -5.38 D, and AL 25.42 mm. Over 12 months, SER changed by -0.078 {+/-} 0.349 D (mean {+/-} SD), and AL increased by 0.052 {+/-} 0.115 mm. Eighty-eight percent of participants had a SER change of <0.5 D, and 91% had axial elongation of <0.2 mm, indicating clinically limited myopia progression. Photopic pupil diameter was larger, and accommodation amplitude was reduced throughout follow-up. Early in treatment, side effects, including photophobia and near-work difficulties, were common but minimally disruptive. Their incidence decreased rapidly and rarely required treatment modification. In exploratory analyses, early AL changes predicted 12-month AL outcomes, with associations detectable as early as 1 week and strengthening over time. Conclusions: 0.05% atropine was well tolerated and effective in this population with light irises. Early AL changes may predict 12-month treatment response. These findings support the implementation of 0.05% atropine in routine clinical practice in populations with light irises and highlight the potential for early AL monitoring to guide timely treatment adjustments.

Multimodal learning has the potential to improve clinical prediction by integrating complementary data sources, but the incremental value of imaging beyond structured electronic health record (EHR) data remains unclear in real-world settings. We developed a multimodal survival modeling framework integrating optical coherence tomography (OCT) and EHR data to predict time to visual improvement in patients with diabetic macular edema (DME), and evaluated how different ophthalmic foundation model representations contribute to prognostic performance. In a retrospective cohort of 973 patients (1,450 eyes) receiving anti-vascular endothelial growth factor therapy, we compared multimodal models combining 22,227 EHR variables with 196,402 OCT images, with OCT embeddings derived from three ophthalmic foundation models (RETFound, EyeCLIP, and VisionFM). The EHR-only model showed minimal prognostic discrimination (C-index 0.50 [95% CI, 0.45-0.55]). Incorporating OCT improved performance, with the magnitude of improvement depending on the representation. EHR+RETFound achieved the strongest performance (C-index 0.59 [0.54-0.65]), followed by EHR+EyeCLIP (0.57 [0.52-0.62]) and EHR+VisionFM (0.56 [0.51-0.61]). Multimodal models, particularly EHR+RETFound, demonstrated improved risk stratification with clearer separation of Kaplan-Meier curves. Partial information decomposition revealed that prognostic information was dominated by modality-specific contributions, with OCT and EHR providing largely distinct signals and minimal shared information. The magnitude of OCT-specific contribution varied across foundation models and aligned with observed performance differences. These findings indicate that OCT provides complementary prognostic value beyond structured clinical data, but gains are modest and depend strongly on representation choice. Our results highlight both the promise of multimodal modeling for personalized prognosis and the need for rigorous, context-specific evaluation of foundation models in real-world clinical settings.

Rhodopsin (RHO) P23H is one of the most common mutations causing autosomal dominant retinitis pigmentosa (adRP), yet the relationship between retinal electrophysiology, structure and visually guided behaviour in rodent models remains unclear. We characterised changes in heterozygous P23H (Sakami line) mice and P23H line 3 (P23H-3) rats using full-field electroretinography (ERG), optomotor response (OMR) assays and, in rats, optical coherence tomography (OCT). ERG assessed rod- and cone-mediated responses relative to wild-type controls, whereas OMR under scotopic and photopic conditions quantified contrast sensitivity and visual acuity. In P23H mice, scotopic ERG responses were significantly reduced from postnatal day 16 and declined further from 4 months. Scotopic OMR contrast sensitivity remained largely preserved until 2 months, and photopic acuity was comparable to wild-type up to 6 months. In 13-week-old P23H-3 rats, ERG amplitudes were significantly reduced, and OCT revealed retinal thinning. OMR showed a decline in contrast sensitivity at 7 and 15 weeks, whereas photopic acuity was maintained. Thus, in both models, electrophysiological and structural abnormalities precede detectable OMR deficits, with implications for the selection of outcome measures in preclinical studies. Summary StatementThis study compares electrical and behavioural measures of vision in rodent models of inherited blindness, revealing that retinal dysfunction appears well before measurable vision loss.

ObjectiveOnly preliminary investigations on the use of the 445 nanometer wavelength blue light laser (BLL) for various laryngeal pathologies have been described. Currently, no standard exists for reporting treatment technique and tissue effect with this modality. Here, we aim to establish and validate a classification system to describe laser-induced tissue effects. Study DesignRetrospective video-based study for classification development and reliability validation. MethodsVideo recordings from procedures performed with the BLL by multiple academic laryngologists were retrospectively reviewed. A preliminary 6-point classification (BLL 1-6) was developed based on expert consensus. Thirteen additional procedural clips were independently rated utilizing the classification schema to assess perceived tissue effect, and measure inter- and intra-rate reliability. ResultsThe final 5-point classification system (BLL 1-5) included angiolysis, blanching, tissue vaporization, ablation with mechanical tissue removal, and cutting. The consensus of the combined reviewers in rating all cases was 89% (58 of 65). Complete consensus was not achieved in 11% (7/65) of cases. Of those incorrect, 57% (4/7) were of clips illustrating the BLL-2 classification. Intra-rater reliability amongst the reviewers was 100%. ConclusionTissue effect of the 445 nm blue light laser can reliably be standardized with this proposed classification system. This rating system can be used to facilitate future systematic study of outcomes and effective communication between laryngologists and trainees.

Background and Objectives: We report the first intraoperative deployment of a real-time machine vision system in neurosurgery, derived from our previous anatomical detection work, automatically identifying structures during endoscopic endonasal surgery. Existing systems demonstrate promising performance in offline anatomical recognition, yet so far none have been implemented during live operations. Methods: A real-time anatomy detection model was trained using the YOLOv8 architecture (Ultralytics). Following training completion in the PyTorch environment, the model was exported to ONNX format and further optimized using the NVIDIA TensorRT engine. Deployment was carried out using the NVIDIA Holoscan SDK, the system ran on an NVIDIA Clara AGX developer kit. We used the model for real-time recognition of intraoperative anatomical structures and compared it with the same video labelled manually as reference. Model performance was reported using the average precision at an intersection-over-union threshold of 0.5 (AP50). Furthermore, end-to-end delay from frame acquisition to the display of the annotated output was measured. Results: A mean AP50 of 0.56 was achieved. The model demonstrated reliable detection of the most relevant landmarks in the transsphenoidal corridor. The mean end-to-end latency of the model was 47.81 ms (median 46.57 ms). Conclusion: For the first time, we demonstrate that clinical-grade, real-time machine-vision assistance during neurosurgery is feasible and can provide continuous, automated anatomical guidance from the surgical field. This approach may enhance intraoperative orientation, reduce cognitive load, and offer a powerful tool for surgical training. These findings represent an initial step toward integrating real-time AI support into routine neurosurgical workflows.

Background/Aims: To evaluate the impact of socioeconomic deprivation on clinical presentation and outcomes of bacterial keratitis (BK) in the United Kingdom. Methods: A retrospective multicentre cohort study of 320 patients with BK presenting to two UK tertiary ophthalmic centres. Demographic, clinical and microbiological data were extracted from electronic health records. Socioeconomic status was assigned using residential postcodes mapped to the 2019 English Index of Multiple Deprivation (IMD) and grouped into quintiles (Q1 most deprived; Q5 least deprived). Presenting severity and outcomes were compared across IMD quintiles. Results: The mean age was 54.0{+/-}20.9 years; 50.6% were male and 83.4% were White. Mean presenting CDVA was 1.10{+/-}1.01 logMAR and time to presentation was a median of 3 days (IQR 1-6). Most cases had a small infiltrate (<3 mm; 68.4%), small epithelial defect (<3 mm; 63.4%) and no hypopyon (72.5%). Hospitalisation was required in 50.0%, and 17.5% underwent surgery. Culture positivity was 36.3%. There were no significant differences in presenting CDVA, time to presentation, clinical severity, admission, microbiological profile, surgical intervention or final CDVA across IMD quintiles (all p>0.05). Final CDVA improved to 0.75{+/-}0.96 logMAR (p<0.001). On multivariable analysis, poorer final CDVA was associated with worse presenting CDVA, increasing age and Gram-positive organisms, but not IMD. Conclusion: Socioeconomic deprivation did not influence the clinical presentation or outcomes in BK. Clinical severity at presentation and microbiological profile were the principal determinants of outcome. In this acute, painful sight-threatening condition, deprivation-related disparities may be attenuated by prompt presentation and universal access to emergency ophthalmic care.

Background: Atopic dermatitis (AD) is a prevalent chronic inflammatory skin disease associated with clinical, psychosocial, and economic burden. Accurate severity assessment is essential for guiding treatment escalation and monitoring disease activity, yet clinician-based scoring systems such as the Eczema Area and Severity Index (EASI) are limited by subjectivity and considerable inter- and intra-rater variability. Erythema, a key driver of AD severity grading, is particularly prone to inconsistent evaluation due to differences in ambient lighting, device quality, skin tone, and rater experience, underscoring the need for objective, reproducible assessment tools. Objective: To develop and validate an artificial intelligence (AI) pipeline for grading erythema, excoriation, and lichenification severity in AD from clinical photographs. The study evaluated the level of agreement between AI severity ratings in each category against dermatologists, non-specialists, and a consensus reference standard, with erythema as the primary outcome of interest. Methods: A two-stage AI pipeline was developed using EfficientNet B7 convolutional neural networks (CNNs). The first CNN was trained as a binary AD classifier on 451 AD and 601 non-AD images for lesion detection and segmentation. The second CNN was trained on 173 dermatologist-annotated AD images which were scored on a 0-3 ordinal scale for erythema, excoriation, and lichenification. This CNN had a downstream feature extraction algorithms such red channel contrast for erythema, Law's E5L5 for excoriation, and S5L5 texture maps for lichenification. In a cross-sectional validation study, 41 independent test images were scored by two blinded dermatologists and two blinded physicians. AI predictions were compared to individual rater groups and mode-derived consensus scores using weighted Cohen's kappa, classification accuracy, confusion matrices, and error direction analyses. Results: On internal validation, the severity CNN achieved 84% overall accuracy (averaged across all three attributes), 86% sensitivity, 87% specificity, and a macro-averaged area under the receiver operating characteristic curve (AUC) of 0.90. In the external comparison with blinded human raters, erythema agreement between the AI and dermatologist consensus was substantial (accuracy 80.7%; kappa = 0.68), with no large (>2-point) misclassifications. Physician consensus agreement was lower (accuracy 54.8%; kappa = 0.34), reflecting greater variability among primary care physicians (non-specialists). For excoriation, AI-dermatologist agreement was moderate (accuracy 72.4%; kappa = 0.62); for lichenification, agreement was similar (accuracy 71.4%; kappa = 0.59). Across all features, disagreements were predominantly between adjacent severity categories. The AI was able to generate erythema severity grades for images of darker skin tones that dermatologists typically would not rate and were marked as "unable to assess". Limitations: The validation set was small (41 images), severe cases (score 3) were underrepresented, one rater participated in both training annotation and validation scoring, and sample size was insufficient for robust stratification by skin tone or body site. Conclusion: The AI pipeline demonstrated dermatologist-level accuracy for erythema scoring, consistent moderate agreement for excoriation and lichenification, and a potential advantage in assessing erythema on darker skin tones. These findings support its potential as a standardized, objective tool for AD severity assessment. Prospective validation in larger, more diverse cohorts is warranted.