Translational Vision Science & Technology

PurposeAspheric planning in laser refractive surgery remains difficult: surgeons often rely on empirical nomo-grams or simple linear regression for defocus and astigmatism, while console Q-factor modulation yields a variably predictable effect on asphericity and an inconsistent cross-effect on defocus. This translational methods proof-of-concept frames planning as supervised prediction of console-programmable inputs (de-focus, astigmatism, Q-factor) and evaluates competing models; it is not a clinical effectiveness study. MethodsWe analyzed an anonymized, retrospective, single-platform dataset of 2,448 complete-case treatments. Multi-output regressors (linear and nonlinear) were trained and compared using prespecified metrics (R2, MAE/MSE) and residual-distribution visualization/calibration. Actuator-response checks related programmed inputs to changes in defocus (Z20) and primary spherical aberration (Z40). External validation used a temporally later, device-shift cohort (n=147). ResultsLinear regression predicted defocus and astigmatism well (e.g., defocus R2=0.98) but degraded for Q-factor (R2=0.47), whereas nonlinear models improved Q-factor error and calibration. Actuator-response analyses showed strong coupling for defocus input (R2=0.97), moderate coupling of Q-factor to {Delta}Z40 (R2=0.51), and a weak Q[->]defocus cross-effect (R2=0.12). On external validation, the best model generalized: Defocus MAE 0.22 D (R2=0.98) and Q-factor MAE 0.21 (R2=0.81). ConclusionsSupervised nonlinear multi-output models achieve lower error and better calibration for Q-factor than linear baselines, supporting a metric-driven pathway toward more reliable control of low-order refractive targets and primary asphericity. Potential clinical implications include tissue sparing, improved contrast, and near-vision gains. Prospective, human-in-the-loop evaluation with safety and patient-reported endpoints is warranted.

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

Conventional assays (questionnaires, acuity, visual fields) may be insufficient to assess the real-world impact of new glaucoma treatments. Here, we report a novel virtual reality shopping task, and assess its sensitivity to differences in mild visual field loss. Eight glaucoma patients were asked to freely navigate a virtual grocery store and place items from a shopping list into a trolley. Across a range of metrics (time, head/body movements), performance was associated with variations in binocular visual field loss [r238=0.35, P<.001]. This indicates promise for using virtual reality tasks to evaluate the benefits of new treatments. Improvements and use-cases are discussed. ONLINE ONLY SUPPLEMENTARY MATERIALThe article contains supplementary material which will be provided as a single stand-alone PDF document.

AimTo assess the relationship between pointwise visual field (VF) sensitivity fluctuation and localised glaucoma progression. MethodsRetrospective observational analysis of prospective cohort data from 399 participants (641 eyes) in the African Descent and Glaucoma Evaluation Study (ADAGES). Glaucoma, glaucoma suspect, and control participants underwent annual examinations including VF testing. VF fluctuation was evaluated using the pointwise standard deviation (SD) of total deviation (TD) residuals during the early 30-month period. Pointwise progression was defined independently at each location as a confirmed sensitivity loss >7 dB. The primary outcome was the association between early fluctuation and subsequent pointwise progression. We additionally evaluated whether the early pointwise rate of change (slope) strengthened this association. ResultsOf 33,332 VF points, 5.8% showed progression over 12.2 {+/-} 3.1 years. Progression occurred more frequently in glaucoma (15.6%) than in suspects (1.6%) or controls (0.4%) (p<0.0001). In glaucomatous eyes, progressive points demonstrated greater early fluctuation (median 1.75 dB; IQR 1.52-2.00) than non-progressive points (1.14 dB; IQR 0.97-1.34; p<0.0001) and faster early slopes (-0.65 vs 0.08 dB/year; p<0.0001). In multivariable mixed-effects models, higher early fluctuation ({beta} = 0.40 {+/-} 0.02; p<0.0001) and faster early slopes ({beta} = - 0.40 {+/-} 0.02; p<0.0001), but not baseline TD (p=0.92), were associated with progression. Conclusions: Greater early pointwise VF fluctuation independently predicted future localised progression. The slope analysis mirrored these findings, indicating that early functional variability reflects underlying local instability. These results support early pointwise fluctuation as a predictor of glaucoma progression and a potential endpoint for clinical trials.

This prospective, multicenter, real-world evidence study evaluates the 12-month safety and effectiveness of standalone cyclodialysis with AlloFlo cleft reinforcement for intraocular pressure (IOP) reduction in open-angle glaucoma (OAG). AlloFlo represents the worlds first acellular, allogenic scleral tissue implant, and data from this CREST Study cohort (NCT05506423) contribute critical long-term safety and effectiveness knowledge to the field of extracellular matrix biomaterials research, in addition to describing a novel procedure for surgical management of OAG. Eyes with investigator-confirmed inadequately controlled OAG were treated with standalone cyclodialysis using a microsurgical cannula (CycloPen), followed by uveoscleral cleft reinforcement with AlloFlo. Eyes were followed prospectively for 12 months. Key outcomes included changes in medicated IOP, number of glaucoma medications, adverse events, and progression to subsequent glaucoma procedures. Forty-one eyes of 38 patients were included. Most eyes (66%) were considered treatment-refractory, defined as having any of: failed [&ge;] 1 incisional surgery or cilioablative procedure; condition in which incisional surgery would be more likely to fail than in eyes with uncomplicated OAG. At 12 months, mean IOP decreased 31% to 14.7 {+/-} 6.9 mmHg (within the normal IOP range of 10-20 mmHg, p < 0.001); mean number of glaucoma medications decreased 32% to 1.9 {+/-} 1.6 (p < 0.001). Seventy-one percent of eyes achieved [&ge;] 20% IOP reduction (a clinically meaningful benchmark set by the FDA). More than half of eyes (53%) achieved [&ge;] 20% IOP reduction without increasing medication. Three eyes (7.2%) progressed to incisional glaucoma surgery. Postoperative IOP elevations [&ge;] 10 mmHg occurred in 17% of eyes, most of which resolved within 30 days of the procedure. No persistent inflammation, implant rejection, clinically significant hyphema, or scaffold migration occurred. These findings suggest that uveoscleral outflow enhancement with AlloFlo provides a safe, conjunctiva-sparing option for IOP reduction in OAG, including eyes with prior surgical interventions.

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

PurposeMeasuring near vision provides clinicians with valuable insight into visual function. There is limited information on the accuracy of available reading charts frequently used in community practice. This study aimed to measure internationally available reading charts to determine how they compare to international standards, and develop a free chart, the UC/UWA Reading Chart, that conforms to these standards. MethodsCommercially and device manufacturer-provided reading charts were scanned at 600 dots per inch. Gaussian adaptive threshold was used to facilitate repeatable measurements. X-heights of letters were measured independently by three researchers. Other variables such as contrast levels and line spacing were also measured. Results for each chart were compared with ISO Standards. Intraclass correlation coefficient was used to assess intergrader agreement. ResultsOf the 19 reading charts that were measured, only one chart (5.26%) had text sizes that were all within tolerance. There was high variability in size observed between charts. Twelve charts (63.2%) used serif fonts and seven used sans-serif (36.8%). Text on serif charts tended to be smaller than required ({micro}=-9.65%) compared to sans-serif ({micro}=+4.96%). All charts met the line spacing requirements and minimum required contrast level; however, some charts were printed on laminated or satin plastic which does not meet the standard of using a matte surface. There was high interrater agreement (ICC(2,1) = 1.00), indicating a highly repeatable measurement technique. ConclusionThis study found that the tested reading charts displayed significant variability in text size. Although some charts had more lines of text within size tolerances than others, none met all the requirements of the International Standard. Clinicians and researchers should take care when interpreting changes in near reading acuity when multiple charts have been used, especially as part of shared care models or when monitoring progressive vision changes. Key pointsO_LINone of the measured reading charts met the requirements of the ISO 7921:2024 standard. C_LIO_LIThere is high variability in text size between reading charts. C_LIO_LIA new chart, the UC/UWA Reading Chart, has been developed to conform to the ISO standards. C_LI

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

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

PurposeTo evaluate the diagnostic performance of anterior segment optical coherence tomography (ASOCT) compared to slit lamp examination for identification of corneal perforation in microbial keratitis, and to assess ASOCT grading reproducibility. MethodsWe conducted a diagnostic concordance study of 150 eyes with microbial keratitis at a tertiary eye hospital in India. Two masked graders independently evaluated ASOCT scans for perforation, with disagreements resolved by consensus. We calculated Cohens kappa for inter-grader concordance and intra-grader repeatability. Sensitivity and specificity of slit lamp examination were calculated using ASOCT as the reference standard. Logistic regression identified factors associated with disagreement between modalities. ResultsInter-grader agreement for ASOCT was near-perfect ({kappa}=0.98; 95% CI, 0.92-1.00). ASOCT identified perforation in 24 eyes (16.0%) compared to 12 eyes (8.0%) by slit lamp examination. Using ASOCT as reference, slit lamp examination demonstrated 33.3% sensitivity (95% CI, 14.9-52.2%) and 96.8% specificity (95% CI, 93.4-99.2%). Odds of disagreement were significantly higher for eyes with stromal thinning (OR=8.19; 95% CI, 2.27-29.54), mid-stromal involvement (OR=4.44; 95% CI, 1.08-18.30), and infection within 2mm of the limbus (OR=8.81; 95% CI, 1.77-43.80). ConclusionsASOCT enables highly reproducible perforation grading and detects substantially more perforations than slit lamp examination, particularly in severe disease. These findings support ASOCT as an objective tool for clinical assessment and outcome ascertainment in microbial keratitis.

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

PurposeTo characterize microscopic alteration of photoreceptors and RPE surrounding cuticular drusen in age-related macular degeneration (AMD) using multimodal imaging, including high resolution adaptive optics scanning laser ophthalmoscopy (AOSLO). MethodsEyes with early to intermediate AMD and predominantly cuticular drusen underwent color fundus photography, infrared reflectance, fundus autofluorescence, optical coherence tomography (OCT), and AOSLO. Cuticular drusen were identified using multimodal imaging and classified into three OCT-defined phenotypes. Cone photoreceptor reflectivity was assessed on AOSLO. A subset of eyes underwent longitudinal AOSLO and OCT imaging. ResultNineteen eyes from 12 subjects aged 70.3 {+/-} 5.8 years were studied. Six eyes had longitudinal follow-up imaging. A total of 3177 cuticular drusen were evaluated and classified into 3 types based on cross sectional OCT imaging. AOSLO revealed corresponding phenotype-dependent cone reflectivity alterations associated with the 3 types of cuticular drusen. Type 1: Maintained cone reflectivity overlying the drusen on a hyporeflective background. Type 2: Cone reflectivity loss overlying the cuticular drusen. Type 3: Cones are predominantly not visible over the cuticular drusen. Lesion diameters were 52.62 {+/-} 9.38 {micro}m (Type 1), 71.88 {+/-} 12.39 {micro}m (Type 2), and 124.72 {+/-} 20.94 {micro}m (Type 3). All lesions were accompanied by hypertransmission in the choroid on OCT. Longitudinal imaging showed that localized outer retinal reflectivity reduction on AOSLO preceded the detection of new cuticular drusen on OCT. ConclusionsCellular-resolution multimodal imaging demonstrates progressive, phenotype-specific disruption of the photoreceptor-RPE complex associated with cuticular drusen in AMD. Early AOSLO-detected reflectivity changes preceding OCT-visible lesions highlight the sensitivity of adaptive optics imaging for identifying early outer retinal alterations and for advancing understanding of the biogenesis of cuticular drusen.

ImportanceEarly intraoperative warning signs of zonular instability during cataract surgery, such as anterior capsular radial folds, are subtle and easily missed but are clinically important for preventing surgical complications. Whether current artificial intelligence (AI) systems can reliably detect such subtle warning signs in real-world surgical video remains unknown. Recently, automated AI model generators have become available, enabling the automatic construction of task-specific AI models for individual clinical tasks. ObjectiveTo evaluate the diagnostic performance of general-purpose and automated task-specific artificial intelligence systems for detecting anterior capsular radial folds during cataract surgery and to compare their performance with human clinicians. Design, Setting, and ParticipantsThis retrospective diagnostic study used 537 continuous curvilinear capsulorhexis (CCC) video clips collected from Beijing Tongren Hospital (China), National University Hospital (Singapore), and the OphNet-APTOS public dataset. ExposurePresence or absence of anterior capsular radial folds during CCC, annotated at both clip and frame levels by senior glaucoma surgeons based on expert consensus. Main Outcomes and MeasuresDiscrimination between fold-positive and fold-negative cases was assessed using macro-averaged precision, recall, and F1 score at the frame and clip levels. Performance was compared among general-purpose AI systems, task-specific models generated by an automated AI model generator, and human graders with different levels of clinical experience. ResultsAmong 537 video clips (mean 7.32 seconds), 156 (29.1%) were fold-positive. General-purpose AI systems showed limited and inconsistent performance; the best-performing model achieved a mean F1 score of 0.519, and fine-tuned models remained inferior to human graders (maximum F1 score, 0.606). In contrast, task-specific models generated by an automated AI model generator achieved substantially higher performance (F1 score, 0.869; area under the receiver operating characteristic curve, 0.958). In head-to-head comparison with clinicians, the top automated task-specific model (F1 score, 0.835) matched the performance of junior specialists (mean F1 score, 0.829) but remained below that of senior specialists. Conclusions and RelevanceGeneral-purpose artificial intelligence systems do not reliably detect subtle intraoperative warning signs during cataract surgery and consistently underperform human clinicians. In contrast, recently available automated AI model generators enable the creation of task-specific models with near-junior specialist performance. These findings suggest that clinically reliable surgical AI is more likely to be achieved through automated generation of task-specific models rather than through general-purpose AI systems. Although evaluated in cataract surgery, these findings highlight a broader challenge for artificial intelligence in detecting brief, low-contrast intraoperative warning signs in surgical video. Key PointsO_ST_ABSQuestionC_ST_ABSHow reliably can general-purpose artificial intelligence (AI) systems and task-specific AI models generated by an automated AI model generator detect subtle intraoperative warning signs during cataract surgery compared with human clinicians? FindingsIn this multicenter diagnostic study of 537 cataract surgery video clips, general-purpose AI systems were unreliable and consistently underperformed human clinicians in detecting anterior capsular radial folds. In contrast, task-specific AI models generated by an automated AI model generator--a technology that has only recently become available--achieved substantially higher diagnostic performance and matched the performance of junior specialists. MeaningGeneral-purpose AI systems show limited reliability for detecting subtle intraoperative warning signs during cataract surgery. The recent availability of automated AI model generators enables a new paradigm of task-specific model development and represents a more clinically viable path for surgical decision support.

PurposeGlaucoma, a leading cause of irreversible vision loss, often remains undiagnosed due to its asymptomatic progression and the limitations of existing screening methods. This study aimed to validate an artificial intelligence machine learning algorithm for the camera-agnostic detection of glaucomatous optic neuropathy using macula-centered fundus images. MethodsData were collected from EyePACS, a teleretinal screening system, comprising 25,000 macula-centered fundus images from 12,500 patients at U.S. primary care centers. A secondary dataset from the Philadelphia Telemedicine Glaucoma Follow-up Study was used for independent validation. A convolutional neural network was developed to detect glaucomatous optic neuropathy. Expert-graded fundus images served as the ground truth. Images underwent quality filtering to ensure the visibility of the optic nerve. Bilateral images were analyzed to produce patient-level diagnoses. Validation involved a secondary dataset of fundus images. ResultsThe sensitivity and specificity of the algorithm in detecting glaucomatous optic neuropathy is calculated in comparison to expert grading. From the EyePACS dataset, 21,792 images (10,986 subjects) met quality standards. The algorithm demonstrated a sensitivity of 90.6% and specificity of 90.5%. Validation on the secondary dataset (200 fundus images from 100 subjects) resulted in a sensitivity of 96.4% and specificity of 85.3%. ConclusionsThe algorithm achieved high sensitivity and specificity in detecting glaucomatous optic neuropathy using macula-centered fundus images, demonstrating its potential for integration into diverse clinical settings. Its camera-agnostic design and robust performance offer a scalable solution for improving glaucoma screening pathways, making them more accessible and efficient.

PurposeTo investigate the effects of morning and evening narrowband blue light exposure on axial length, and to examine the short-term effect of morning blue light combined with myopic defocus on axial length. MethodsFor objective 1, 18 individuals underwent 60 minutes of narrowband blue light exposure (460nm) in the morning (9:00-11:00AM) and evening (5:00-7:00PM) of the same day. The axial length values were normalized to the average of the morning and evening axial length values. For objective 2, 27 young adults were exposed to 60 minutes of narrowband blue light and broadband white light while wearing a +3.00 D lens over the right eye. Axial length was measured using Lenstar LS900. ResultsA significant reduction in axial length was observed after exposure to morning blue light compared to evening blue light (-10.0{+/-}3.96{micro}m vs.-0.67{+/-}3.30{micro}m; p=0.02), whereas no such effect was observed with broadband white light exposure (0.0{+/-}3.53 {micro}m vs. -2.50{+/-}4.23{micro}m, p=0.70). While the broadband white light exposure did not alter the normal diurnal variation in axial length (+2.35{+/-}1.82{micro}m vs.-6.25{+/-}2.21{micro}m, p=0.04), blue light diminished such a pattern (-4.12{+/-}1.72{micro}m vs. - 2.00{+/-}2.00{micro}m, p=0.48). The myopic defocus did not influence axial length under either narrowband blue or broadband white light conditions. ConclusionThe short-term narrowband blue light exposure led to a significant decrease in axial length in the morning than evening exposure, with a likely influence on the diurnal rhythm of axial length. Morning blue light exposure with lens-induced myopic defocus did not provide additional short-term modulation of axial length.

PurposeMuller cell (MC) morphology and markers were investigated using histology and immunohistochemistry in an eye with clinically documented multifocal geographic atrophy (GA) and correlated with clinical images. MethodsThe donor was followed clinically for five years and last examined six years before death. The superior posterior pole retina was dissected and immunolabeled with antibodies against glial fibrillary acidic protein (GFAP; activated MCs and astrocytes) and glutamine synthetase (GS, MC) and Ulex Europaeus Agglutinin-1 lectin (blood vessels) before embedding for JB-4 cross section analysis. The inferior macula was cryopreserved. Cryosections were immunolabeled with MC homeostatic and activation markers. Transmission electron microscopy (TEM) of the fellow eye was used to study ultrastructure changes. ResultsGross examination demonstrated mottled retinal pigment epithelium (RPE) over presumably calcified drusen. In the submacular retina, MC processes surrounding both drusen and outer retinal pigmented lesions created a large subretinal membrane. Cryosection analysis demonstrated persistence of aquaporin 4 and GS in MCs with both proteins prominently expressed in the subretinal membrane. Increased MC S100B and GFAP expression were also observed in the atrophic area as well as the OJZ. Cryosection labeling and TEM confirmed the MC encasing calcified drusen and RPE debris as well as invading basal laminar deposits. ConclusionsThis multifocal GA case demonstrates how MC activation and structural changes surrounding individual drusen could coalesce, contributing to photoreceptor loss. MCs penetrating basal laminar deposits and encasing calcified drusen suggests that they are attempting to clear these and/or protect the retina from harmful contents.

Purpose: To determine the test-retest reliability of visual function parameters in patients with genetically confirmed Pseudoxanthoma elasticum (PXE), as a necessary step toward evaluating their suitability as outcome measures in future therapeutic trials. Methods: In this prospective natural history study (PROPXE, ClinicalTrials.gov ID: NCT05662085), patients with PXE underwent comprehensive visual function evaluation in one study eye at baseline and at a month 2 retest visit. Functional testing included light- and dark-adapted steady state microperimetry and dark-adaptometry at 8{degrees}, 15{degrees} 30{degrees}, and 46{degrees} eccentricity. Test-retest reliability was evaluated using Bland-Altman statistics. Results: Twenty-six patients (14 female, 12 male; median [IQR] age 55 years [43; 59]) with genetically confirmed PXE were included in the study. Overall, the steady-state microperimetry limits of agreement (LoA) were {+/-}2 dB for mean sensitivity and {+/-}6.8 dB for pointwise sensitivity in both scotopic (cyan and red) and mesopic conditions. The LoAs of rod intercept time as a measure of dark adaptometry were {+/-} 12 min at the inner measurement points (8{degrees} and 15{degrees}) and {+/-} 18 min at the outer measurement points (30{degrees} and 46{degrees}). Conclusions: Scotopic and mesopic microperimetry LoAs are similar to earlier published test-retest analyses in other retinal diseases. Dark-adaptometry curve parameters were markedly more variable compared to previous data in healthy volunteers. This is likely attributable to the severe dark adaptation abnormalities in PXE, leading to long test durations. Translational Relevance: The evaluation of functional biomarkers is critical for designing future clinical trials aimed at slowing PXE progression.

PurposeThe interpupillary distance is a measure of the width of the orbit and is important for spectacle design and proper head-mounted displays in virtual reality. Extreme interpupillary distances may predispose to horizontal strabismus. The interpupillary distance is thought to differ between ethnicities, but global data on this parameter have not been systematically explored, mapped and compared. MethodologyWe performed a systematic review that compiled 242 studies reporting the interpupillary distance and mapped the data geographically. We then compared the regional distribution of the mean interpupillary distance with the distribution of horizontal strabismus patterns. The strabismus data, obtained from our previous systematic review of the literature, were used to map the relative frequency of esotropia and exotropia according to 301 population-based studies. The mean interpupillary distance and esotropia/exotropia ratio of major ethnicities were then analyzed by meta-regression analyses to determine an association between the two parameters. ResultsInterpupillary distances are larger in Western Africa, South India, East Asia, in Latinos/Hispanics, in Native and African Americans, and they are smaller in Europe, North Africa, the Middle East, Northwestern India, and in Inuit populations. Regression analyses revealed an association between the interpupillary distance and the esotropia/exotropia ratio with R2 values of 0.320 (major ethnicities) and 0.410 (populations at higher resolution). ConclusionThe mean interpupillary distance varies significantly between ethnicities. Orbital anatomical parameters contribute to diverse horizontal strabismus patterns. Our findings may aid in the design of appropriate spectacles and the optimal size of head-mounted displays, and help to better understand the pathogenesis of horizontal strabismus. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=106 SRC="FIGDIR/small/25343217v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@14cf7d6org.highwire.dtl.DTLVardef@232e5aorg.highwire.dtl.DTLVardef@1b82f66org.highwire.dtl.DTLVardef@12ffd81_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG

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

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