Vision (Switzerland)最新文献

This retrospective study evaluated changes in ocular characteristics and retinal pigment epithelium (RPE) and photoreceptor ellipsoid zone (EZ) depletion rates before and after intravitreal pegcetacoplan initiation in clinical practice. A total of 168 eyes from 110 patients with GA secondary to age-related macular degeneration (AMD) who received at least 3 pegcetacoplan injections were included. Data was collected from 5 years before to 9 months after pegcetacoplan initiation. RPE and EZ depletion areas were measured using an automated artificial intelligence (AI) algorithm on optical coherence tomography (OCT) images. At baseline, 76 eyes (45.2%) had concurrent neovascular AMD (nAMD), with mean RPE and EZ depletion areas of 3.3 mm² and 4.9 mm², respectively. By pegcetacoplan initiation, these increased to 8.6 mm² and 11.2 mm², respectively, with 151 eyes (89.9%) having concurrent nAMD and 155 eyes (92.3%) having subfoveal GA. Pre-treatment to post-treatment RPE and EZ square root depletion rates decreased from 0.25 mm/year to 0.096 mm/year, and 0.26 mm/year to 0.049 mm/year, respectively. Mean best-recorded visual acuity (BRVA) worsened by 0.05 logMAR annually before and after treatment. These real-world findings align with data from the pegcetacoplan phase 3 trials, showing reduced RPE and EZ depletion rates without changes in rates of BRVA loss. Additional studies are warranted.

This study evaluates the ability of the QuickSee Free (QSF) portable autorefractor (PlenOptika) to detect and measure refractive error compared to subjective clinical refractometry (SCR) in a Brazilian adult population in a low-resource setting in Amazonas. A total of 100 participants aged 18-65 years underwent visual acuity screening and autorefraction with and without cycloplegia using the QSF, alongside a complete ophthalmic examination including SCR. Refractive error measurements included spherical component (SC), cylindrical component (CC), cylindrical axis (CA), spherical equivalent (SE), and vector powers (MV90 and MV135). Accuracy was assessed for hyperopia ≥ +2.00 D, myopia ≤ -0.75 D, astigmatism ≥ 1.00 DC, and anisometropia ≥ 1.00 D using receiver operating characteristic (ROC) curve analysis. The area under the curve for detecting significant refractive errors ranged from 0.538 to 0.930. The mean difference between QSF without cycloplegia and SCR was -1.08 ± 1.17 D for SC and -1.15 ± 1.15 D for SE (p < 0.0001), and with cycloplegia, it was -0.81 ± 1.07 D and -0.83 ± 1.02 D, respectively. The QSF exhibited a moderate negative bias for both SC and SE with and without cycloplegia, underestimating these values, but it showed good predictability for detecting refractive errors in a low-resource setting.

Deep learning for glaucoma screening often relies on high-resolution clinical images and convolutional neural networks (CNNs). However, these methods face significant performance drops when applied to noisy, low-resolution images from portable devices. To address this, our work investigates ensemble methods using multiple Transformer architectures for automated glaucoma detection in challenging scenarios. We use the Brazil Glaucoma (BrG) and private D-Eye datasets to assess model robustness. These datasets include images typical of smartphone-coupled ophthalmoscopes, which are often noisy and variable in quality. Four Transformer models-Swin-Tiny, ViT-Base, MobileViT-Small, and DeiT-Base-were trained and evaluated both individually and in ensembles. We evaluated the results at both image and patient levels to reflect clinical practice. The results show that, although performance drops on lower-quality images, ensemble combinations and patient-level aggregation significantly improve accuracy and sensitivity. We achieved up to 85% accuracy and an 84.2% F1-score on the D-Eye dataset, with a notable reduction in false negatives. Grad-CAM attention maps confirmed that Transformers identify anatomical regions relevant to diagnosis. These findings reinforce the potential of Transformer ensembles as an accessible solution for early glaucoma detection in populations with limited access to specialized equipment.

Models suggest social anxiety is characterized by negative processing biases. Negative biases also arise from negative mood, i.e., state affect. We examined how social anxiety influences emotional processing and whether state affect, or mood, modified the relationship between social anxiety and perceptual bias. We quantified bias by determining the point of subjective equality, PSE, the face judged equally often as happy and as angry. We found perceptual bias depended on social anxiety and state affect. PSE was greater in individuals high (mean PSE: 8.69) versus low (mean PSE: 3.04) in social anxiety. The higher PSE indicated a stronger negative bias in high social anxiety. State affect modified this relationship, with high social anxiety associated with stronger negative biases, but only for individuals with greater negative affect. State affect and trait anxiety interacted such that social anxiety status alone was insufficient to fully characterize perceptual biases. This raises several issues such as the need to consider what constitutes an appropriate control group and the need to consider state affect in social anxiety. Importantly, our results suggest compensatory effects may counteract the influences of negative mood in individuals low in social anxiety.

We provide an automated characterization of human retinal cells, i.e., RPE's based on the non-invasive AO-TFI retinal imaging and PR's based on the non-invasive AO-FI retinal imaging on a large-scale study involving 171 confirmed healthy eyes from 104 participants of 23 to 80 years old. Comprehensive standard checkups based on SD-OCT and Fondus imaging modalities were carried out by Ophthalmologists from the Luzerner Kantonsspital (LUKS) to confirm the absence of retinal pathologies. AO imaging imaging was performed using the Cellularis^® device and each eye was imaged at various retinal eccentricities. The images were automatically segmented using a dedicated software and RPE and PR cells were identified and morphometric characterizations, such as cell density and area were computed. The results were stratified based on various criteria, such as age, retinal eccentricity, visual acuity, etc. The automatic segmentation was validated independently on a held-out set by five trained medical students not involved in this study. We plotted cell density variations as a function of eccentricity from the fovea along both nasal and temporal directions. For RPE cells, no consistent trend in density was observed between 0° to 9° eccentricity, contrasting with established histological literature demonstrating foveal density peaks. In contrast, PR cell density showed a clear decrease from 2.5° to 9°. RPE cell density declined linearly with age, whereas no age-related pattern was detected for PR cell density. On average, RPE cell density was found to be ≈6313 cells/mm² (±σ=757), while the average PR cell density was calculated as ≈10,207 cells/mm² (±σ=1273).

Background: This paper aims to provide an overview of corneal birefringence (CB), systematize the knowledge and current understanding of CB, and identify difficulties associated with introducing CB into mainstream clinical practice.

Methods: Literature reviews were conducted, seeking articles focused on CB published between the early 19th century and the present time. Secondary-level searches were made examining relevant publications referred to in primary-level publications, ranging back to the early 17th century. The key search words were "corneal birefringence" and "non-invasive measurements".

Results: CB was first recorded by Brewster in 1815. Orthogonally polarized rays travel at different speeds through the cornea, creating a slow axis and a fast axis. The slow axis aligns with the pattern of most corneal stromal collagen fibrils. In vivo, it is oriented along the superior temporal-inferior nasal direction at an angle of about 25° (with an approximate range of -54° to 90°) from the horizontal. CB has been reported to (i) influence the estimation of retinal nerve fiber layer thickness; (ii) be affected by corneal interventions; (iii) be altered in keratoconus; (iv) vary along the depth of the cornea; and (v) be affected by intra-ocular pressure.

Conclusions: Under precisely controlled conditions, capturing the CB pattern is the first step in a non-destructive process used to model the ultra-fine structure of the individual cornea, and changes thereof, in vivo.

Purpose: This study aimed to determine the prevalence and associated risk factors of keratoconus (KC) among high school students in Couva, Trinidad and Tobago. Method: A cross-sectional, school-based approach was used, involving a simple random sampling technique to select schools and students. A structured questionnaire assessed KC risk factors, while clinical assessments, including visual acuity, refraction, slit lamp biomicroscopy, and topography, were performed. Data were analyzed using R. Exact tests were used for KC (n = 2 cases) and robust Poisson regression estimated adjusted prevalence ratios for the 'at-risk' screening endpoint. Results: A total of 432 students aged 12-17 years participated, with a response rate of 97.5%. Most participants were of East Indian descent (48.1%), female (52.1%), and 14 years old (23.1%). Approximately 47.7% (95% CI 43.0-52.5%) were at risk of KC, with 0.5% (2/432; exact 95% CI 0.06-1.67%) diagnosed with the condition. The most common risk factors were eye rubbing (87.4%), over eight hours of sun exposure weekly (71.8%), and atopy (68.4%). KC was observed to be significantly higher among people with a family history (p = 0.018). Conclusions: The study highlights a low prevalence and a high risk of KC among high school students, with a strong link to family history and common risk factors such as eye rubbing and sun exposure. These findings emphasize the urgent need for regular KC screening in schools to ensure early diagnosis and effective management.

Face recognition is an important skill that helps people make social judgments by identifying both who a person is and other characteristics such as their expression, age, and ethnicity. Previous models of face processing, such as those proposed by Bruce and Young and by Haxby and colleagues, suggest that identity and other facial features are processed through partly independent systems. This study aimed to compare the efficiency with which different facial characteristics are processed in a visual search task. Participants viewed arrays of two, four, or six faces and judged whether one face differed from the others. Four tasks were created, focusing separately on identity, expression, ethnicity, and gender. We found that search times were significantly longer when looking for identity and shorter when looking for ethnicity. Significant correlations were found among almost all tests in all outcome variables. Comparison of target-present and target-absent trials suggested that performance in none of the tests seems to follow a serial-search-terminating model. These results suggest that different facial characteristics share early processing but differentiate into independent recognition mechanisms at a later stage.

Glaucoma management relies on lowering intraocular pressure (IOP), but determining the target reduction at presentation is challenging, particularly in normal-tension glaucoma (NTG). We developed and internally validated a neural network regression model using retrospective clinical data from Qiu et al. (2015), including 270 patients (118 with NTG). A single-layer artificial neural network with five nodes was trained in MATLAB R2024b using the Levenberg-Marquardt algorithm. Inputs included demographic, refractive, structural, and functional parameters, with IOP reduction as the output. Data were split into 65% training, 15% validation, and 20% testing, with training repeated 10 times. Model performance was strong and consistent (average RMSE: 1.90 ± 0.29 training, 2.18 ± 0.34 validation, 2.11 ± 0.30 testing; Pearson's r: 0.92 ± 0.02, 0.88 ± 0.02, 0.88 ± 0.04). The best-performing model achieved RMSEs of 1.57, 2.90, and 1.77 with r values of 0.93, 0.91, and 0.93, respectively. Feature ablation revealed significant contributions from IOP, axial length, CCT, diagnosis, VCDR, spherical equivalent, mean deviation, and laterality. This study demonstrates that a simple neural network can reliably predict individualized IOP reduction targets, supporting personalized glaucoma management and improved outcomes.

Background: This study compared the performance of a Head-mounted Virtual Reality Perimeter (HVRP) with the Humphrey Field Analyzer (HFA), the standard in automated perimetry. The HFA is the established standard for automated perimetry but is constrained by lengthy testing, bulky equipment, and limited patient comfort. Comparative data on newer head-mounted virtual reality perimeters are limited, leaving uncertainty about their clinical reliability and potential advantages. Aim: The aim was to evaluate parameters such as visual field outcomes, portability, patient comfort, eye tracking, and usability. Methods: Participants underwent testing with both devices, assessing metrics like mean deviation (MD), pattern standard deviation (PSD), and duration. Results: The HVRP demonstrated small but statistically significant differences in MD and PSD compared to the HFA, while maintaining a consistent trend across participants. MD values were slightly more negative for HFA than HVRP (average difference -0.60 dB, p = 0.0006), while pattern standard deviation was marginally higher with HFA (average difference 0.38 dB, p = 0.00018). Although statistically significant, these differences were small in magnitude and do not undermine the clinical utility or reproducibility of the device. Notably, HVRP showed markedly shorter testing times with HVRP (7.15 vs. 18.11 min, mean difference 10.96 min, p < 0.0001). Its lightweight, portable design allowed for bedside and home testing, enhancing accessibility for pediatric, geriatric, and mobility-impaired patients. Participants reported greater comfort due to the headset design, which eliminated the need for chin rests. The device also offers potential for AI integration and remote data analysis. Conclusions: The HVRP proved to be a reliable, user-friendly alternative to traditional perimetry. Its advantages in comfort, portability, and test efficiency support its use in both clinical settings and remote screening programs for visual field assessment. Its portability and user-friendly design support broader use in clinical practice and expand possibilities for bedside assessment, home monitoring, and remote screening, particularly in populations with limited access to conventional perimetry.