Cause Of Visual Impairment Research Articles

Repeated treatment with VEGF receptor inhibitors induces phenotypic changes in endothelial cells and pericytes in the rat retina

Abnormal ocular angiogenesis is a major cause of visual impairment and vision loss in neovascularization-related diseases. Currently, anti-vascular endothelial growth factor (VEGF) drugs are used to treat ocular neovascularization, but repeated injections are needed to maintain their therapeutic effects. However, repeated injection of anti-VEGF drugs may affect the retinal blood vessel phenotype and diminish therapeutic effects. In this study, we aimed to investigate the phenotypic changes in endothelial cells and pericytes caused by the repeated interruption of the VEGF receptor signaling pathway in neonatal rats. KRN633 (10 mg/kg), a VEGF receptor tyrosine kinase inhibitor, was subcutaneously administered on postnatal day (P)-7 and P8 (first round), P14 and P15 (second round), and P21 and P22 (third round). The rat eyes were collected on P7, P9, P14, P16, P21, P23, P28, and P35. Using retinal flat-mount specimens stained with specific markers for vascular endothelial cells, basement membranes, and pericytes, the arteriolar tortuosity, capillary area density, and distribution of pericytes were evaluated. Significant loss of capillaries was observed the day after the first round of KRN633 treatment, after which aggressive angiogenesis occurred, leading to the formation of tortuous arterioles. Rats that completed second and third rounds of KRN633 treatment showed more severe abnormalities in the retinal vasculature than those that only completed first round treatment. Repeated treatment with KRN633 decreased the anti-angiogenic effects but increased the immunoreactivity of α-smooth muscle actin in the pericytes on veins and capillaries. α-Smooth muscle actin expression was inversely correlated to anti-angiogenic effects. Overall, these results revealed that repeated interruption of VEGF receptor signaling pathway altered the phenotypes of endothelial cells and pericytes and induced anti-VEGF drug resistance. Therefore, careful follow-up is necessary when using anti-VEGF drugs to treat abnormal angiogenesis-associated ocular diseases.

Diabetic Retinopathy Diagnosis based on Convolutional Neural Network in the Russian Population: A Multicenter Prospective Study.

Diabetic retinopathy is the most common complication of diabetes mellitus and is one of the leading causes of vision impairment globally, which is also relevant for the Russian Federation. To evaluate the diagnostic efficiency of a convolutional neural network trained for the detection of diabetic retinopathy and estimation of its severity in fundus images of the Russian population. In this cross-sectional multicenter study, the training data set was obtained from an open source and relabeled by a group of independent retina specialists; the sample size was 60,000 eyes. The test sample was recruited prospectively, 1186 fundus photographs of 593 patients were collected. The reference standard was the result of independent grading of the diabetic retinopathy stage by ophthalmologists. Sensitivity and specificity were 95.0% (95% CI; 90.8-96.4) and 96.8% (95% CI; 95.5- 99.0), respectively; positive predictive value - 98.8% (95% CI; 97.6-99.2); negative predictive value - 87.1% (95% CI, 83.4-96.5); accuracy - 95.9% (95% CI; 93.3-97.1); Kappa score - 0.887 (95% CI; 0.839-0.946); F1score - 0.909 (95% CI; 0.870-0.957); area under the ROC-curve - 95.9% (95% CI; 93.3-97.1). There was no statistically significant difference in diagnostic accuracy between the group with isolated diabetic retinopathy and those with hypertensive retinopathy as a concomitant diagnosis. The method for diagnosing DR presented in this article has shown its high accuracy, which is consistent with the existing world analogues, however, this method should prove its clinical efficiency in large multicenter multinational controlled randomized studies, in which the reference diagnostic method would be unified and less subjective than an ophthalmologist.

Serum lipid and lipoprotein profiles and their association with intraocular pressure in primary open-angle glaucoma: an observational cross-sectional study in the Chinese population

BackgroundGlaucoma is a leading cause of vision impairment and permanent blindness. Primary open-angle glaucoma (POAG) is a prominent type of primary glaucoma; however, its cause is difficult to determine. This study aimed to analyze the serum lipid profile of Chinese POAG patients and assess its correlation with intraocular pressure (IOP).MethodsThe study included 1,139, 1,248, and 356 Chinese individuals with POAG, primary angle closure glaucoma (PACG), and controls, respectively. Peripheral whole blood samples were collected at the time of diagnosis. Enzymatic colorimetry was used to determine serum levels of different lipids: high-density lipoproteins (HDL), low-density lipoproteins (LDL), triglycerides, cholesterol, and very low-density lipoproteins (VLDL). Additionally, immunoturbidimetry was used to quantify serum levels of apolipoproteins A (APOA), B (APOB), E (APOE), and lipoprotein A [Lp(a)], while intraocular pressure (IOP) was measured in all patients with POAG.ResultsAfter adjusting for age and sex, patients with POAG exhibited elevated serum levels of VLDL, APOA, and APOE but mitigated cholesterol levels compared with the control participants. Significantly lower serum triglyceride, VLDL, and Lp(a) levels were found in patients with PACG than in control participants. Serum cholesterol (P = 0.019; β = -0.75, 95% confidence interval [CI]: -1.38 – -0.12) and HDL levels (P < 0.001; β = -2.91, 95% CI: -4.58 – -1.25) were inversely linked to IOP in patients with POAG, after adjusting for age, sex, and ocular metrics. In addition, serum Lp(a) levels were correlated with the average IOP (P = 0.023; β = -0.0039, 95% CI: -0.0073 – -0.006) and night peak (P = 0.027; β = -0.0061, 95% CI: -0.0113 – -0.0008) in patients with POAG.ConclusionsSignificantly different serum lipid and lipoprotein profiles were observed in POAG and PACG patients. This study highlighted the differences in serum lipid and lipoprotein levels among Chinese POAG patients and their relationship with IOP and IOP fluctuation. Serum lipid and lipoprotein profiles should be considered while evaluating glaucoma risk.

Advanced Filtering and Enhancement Techniques for Diabetic Retinopathy Image Analysis

Diabetic retinopathy is a leading cause of visual impairment and blindness in diabetes sufferers. Early detection is crucial to prevent severe outcomes. This study presents an image processing method for retinal images to aid early detection. The method involves four steps: image enlargement, preprocessing, enhancement, and convolution. First, an algorithm enlarges the retinal image to increase resolution and reveal finer details. Preprocessing uses a min-max filtering algorithm to reduce noise and improve image quality. Next, specific pixel range enhancement techniques further refine the image and highlight relevant features. Finally, convolution with customized kernels detects and emphasizes areas indicating diabetic retinopathy, such as aneurysms and hemorrhages. Experimental results show improvement in image clarity and detail, enabling more accurate detection of diabetic retinopathy features. The correlation results are as follows: Filtering (0.35275, 0.20157, 0.4345), Enhancement (0.3214, 0.15823 0.34674), and Convolution (0.33542, 0.15758, 0.36826). The proposed algorithm enhances early detection and diagnosis by improving retinal image quality. Future work can optimize the algorithm and validate results with larger datasets, aiming to refine the determination of areas or pixel values relevant to diabetic retinopathy.

Global certification of visual impairment registries: A scoping review.

Visual impairment is a global problem which is predicted to rise in the coming years. Some of the biggest causes of visual impairment globally include uncorrected refractive error, cataract and age-related macular degeneration. People with a visual impairment often require support and so many countries hold registers of visual impairment. These registers can sit at a national, regional or local level. This scoping review aims to identify which countries hold visual impairment registries and have published data from them. Medline All, Embase and EBSCOHost were searched using several search terms after consulting an information specialist. All papers after the year 2000 were included in the scoping review. All results are shown using a PRISMA diagram and presented narratively. The total number of articles and papers identified was 1266; after screening and review, 57 articles were included in the review from 2000 to 2024. These articles came from 19 different countries and encompassed national, regional and local visual impairment databases. Many countries cited age-related macular degeneration as the major cause of blindness with diabetic retinopathy and glaucoma following. In less economically developed countries, refractive error was the main cause of sight loss. There were papers which focused on specific eye conditions such as glaucoma and diabetic retinopathy or on specific cohorts including working-age population and children. The leading causes of blindness in children appeared to be inherited retinal diseases, albinism and cerebral visual impairment. Certification of visual impairment is held differently across the world. There is commonality among different countries regarding the major causes of visual impairment in both adults and children. The importance of holding visual impairment registers to support people with a visual impairment and to plan services is essential.

Macular Pigment Optical Density as a Measurable Modifiable Clinical Biomarker.

Carotenoids are present throughout retina and body its dense deposition leads to an identifiable yellow spot in the macula. Macular pigment optical density (MPOD) measured in the macula is vital to macular well-being and high-resolution visual acuity. MPOD has also been associated with various health and disease states. We sought to review the literature on this topic and summarize MPODs role as a measurable modifiable clinical biomarker, particularly as a measure of the eye's antioxidant capacity in the context of oxidative damage and retinal ischemia. A literature review collated the articles relevant to MPOD, carotenoid intake or supplementation, and their influence on various health and disease states. Literature reveals that MPOD can serve as a reliable biomarker for assessing the retinal defense mechanisms against oxidative stress and the deleterious effects of excessive light exposure. Elevated MPOD levels offer robust protection against the onset and progression of age-related macular degeneration (AMD), a prevalent cause of vision impairment among the elderly population. MPOD's implications in diverse ocular conditions, including diabetic retinopathy and glaucoma, have been explored, underscoring the real need for clinical measurement of MPOD. The integration of MPOD measurement into routine eye examinations presents an unparalleled opportunity for early disease detection, precise treatment planning, and longitudinal disease monitoring. Longitudinal investigations underscore the significance of MPOD in the context of age-related ocular diseases. These studies show promise and elucidate the dynamic nuances of MPOD's status and importance as a measurable, modifiable clinical biomarker.

A Competition for the Diagnosis of Myopic Maculopathy by Artificial Intelligence Algorithms

Myopic maculopathy (MM) is a major cause of vision impairment globally. Artificial intelligence (AI) and deep learning (DL) algorithms for detecting MM from fundus images could potentially improve diagnosis and assist screening in a variety of health care settings. To evaluate DL algorithms for MM classification and segmentation and compare their performance with that of ophthalmologists. The Myopic Maculopathy Analysis Challenge (MMAC) was an international competition to develop automated solutions for 3 tasks: (1) MM classification, (2) segmentation of MM plus lesions, and (3) spherical equivalent (SE) prediction. Participants were provided 3 subdatasets containing 2306, 294, and 2003 fundus images, respectively, with which to build algorithms. A group of 5 ophthalmologists evaluated the same test sets for tasks 1 and 2 to ascertain performance. Results from model ensembles, which combined outcomes from multiple algorithms submitted by MMAC participants, were compared with each individual submitted algorithm. This study was conducted from March 1, 2023, to March 30, 2024, and data were analyzed from January 15, 2024, to March 30, 2024. DL algorithms submitted as part of the MMAC competition or ophthalmologist interpretation. MM classification was evaluated by quadratic-weighted κ (QWK), F1 score, sensitivity, and specificity. MM plus lesions segmentation was evaluated by dice similarity coefficient (DSC), and SE prediction was evaluated by R2 and mean absolute error (MAE). The 3 tasks were completed by 7, 4, and 4 teams, respectively. MM classification algorithms achieved a QWK range of 0.866 to 0.901, an F1 score range of 0.675 to 0.781, a sensitivity range of 0.667 to 0.778, and a specificity range of 0.931 to 0.945. MM plus lesions segmentation algorithms achieved a DSC range of 0.664 to 0.687 for lacquer cracks (LC), 0.579 to 0.673 for choroidal neovascularization, and 0.768 to 0.841 for Fuchs spot (FS). SE prediction algorithms achieved an R2 range of 0.791 to 0.874 and an MAE range of 0.708 to 0.943. Model ensemble results achieved the best performance compared to each submitted algorithms, and the model ensemble outperformed ophthalmologists at MM classification in sensitivity (0.801; 95% CI, 0.764-0.840 vs 0.727; 95% CI, 0.684-0.768; P = .006) and specificity (0.946; 95% CI, 0.939-0.954 vs 0.933; 95% CI, 0.925-0.941; P = .009), LC segmentation (DSC, 0.698; 95% CI, 0.649-0.745 vs DSC, 0.570; 95% CI, 0.515-0.625; P < .001), and FS segmentation (DSC, 0.863; 95% CI, 0.831-0.888 vs DSC, 0.790; 95% CI, 0.742-0.830; P < .001). In this diagnostic study, 15 AI models for MM classification and segmentation on a public dataset made available for the MMAC competition were validated and evaluated, with some models achieving better diagnostic performance than ophthalmologists.

Artificial Intelligence in Detecting the Severity of Diabetic Retinopathy

BACKGROUND Diabetic retinopathy (DR) is the leading cause of visual impairment and blindness among individuals with diabetes mellitus. Early detection and timely intervention are crucial for preventing irreversible vision loss. However, traditional methods of DR screening are labor-intensive and reliant on the availability of skilled personnel, posing challenges in resource-constrained settings. Objective This study aims to evaluate the effectiveness of artificial intelligence (AI) in detecting the severity of DR compared to conventional ophthalmological assessments. METHODS A hospital-based observational study was conducted at the ophthalmology outpatient department of Tertiary care hospital, India over a six-month period. A total of 300 diabetic patients were included, and fundus photographs were obtained using a fundus camera. The images were then analyzed using an AI model trained on a diabetic retinopathy dataset. The severity of DR was graded according to established criteria, and the accuracy of the AI model was compared to that of ophthalmologist grading. RESULTS The AI model demonstrated an accuracy rate of 95.25% in grading the severity of DR. Comparison between AI and ophthalmologist grading showed close sensitivity and specificity rates across different DR grades, with the AI model slightly outperforming in certain categories. CONCLUSIONS Artificial intelligence shows promise as an effective and efficient tool for the screening and diagnosis of diabetic retinopathy. Its integration into healthcare systems could enhance early detection and treatment of DR, particularly in underserved regions with limited access to ophthalmological services. Further research and validation are warranted to optimize the use of AI in diabetic eye care and ensure its equitable distribution and ethical use.

Revolutionizing diabetic eye disease detection: retinal image analysis with cutting-edge deep learning techniques

Globally, glaucoma is a leading cause of visual impairment and vision loss, emphasizing the critical need for early diagnosis and intervention. This research explores the application of deep learning for automated glaucoma diagnosis using retinal fundus photographs. We introduce a novel cross-sectional optic nerve head (ONH) feature derived from optical coherence tomography (OCT) images to enhance existing diagnostic procedures. Our approach leverages deep learning to automatically detect key optic disc characteristics, eliminating the need for manual feature engineering. The deep learning classifier then categorizes images as normal or abnormal, streamlining the diagnostic process. Deep learning techniques have proven effective in classifying and segmenting retinal fundus images, enabling the analysis of a growing number of images. This study introduces a novel mixed loss function that combines the strengths of focal loss and correntropy loss to handle complex biomedical data with class imbalance and outliers, particularly in OCT images. We further refine a multi-task deep learning model that capitalizes on similarities across major eye-fundus activities and metrics for glaucoma detection. The model is rigorously evaluated on a real-world ophthalmic dataset, achieving impressive accuracy, specificity, and sensitivity of 100%, 99.8%, and 99.2%, respectively, surpassing state-of-the-art methods. These promising results underscore the potential of our deep learning algorithm for automated glaucoma diagnosis, with significant implications for clinical applications. By simultaneously addressing segmentation and classification challenges, our approach demonstrates its effectiveness in accurately identifying ocular diseases, paving the way for improved glaucoma diagnosis and early intervention.

Prevalence, causes and factors associated with vision impairment in Limpopo province

Background: Epidemiological data on the prevalence, causes and risk factors associated with vision impairment (VI) is necessary to evaluate the effectiveness of eye care services.Aim: To determine the prevalence, causes and factors associated with VI among patients presenting to public hospitals of Limpopo province, South Africa.Setting: This study was conducted in public hospitals.Methods: A retrospective chart review of patients seen from April 2019 to March 2022 in 29 public hospitals was conducted. The following information was extracted: demographic information, medical history, visual acuity (VA), refractive and ophthalmoscopy findings.Results: Of the 1140 participants, 56.1% were women. Participants’ ages ranged from 5 to 94 years. The prevalence of VI was 61.5%. Most had moderate-severe VI (57.3%), followed by blindness (22.7%) and mild VI (20.0%). The leading causes of VI were uncorrected refractive error (URE) (28.1%), cataract (26.0%) and glaucoma (25.0%). The main causes of blindness were glaucoma (42.8%) and cataract (32.1%); while URE (7.5%), retinal anomalies (7.5%) and corneal anomalies (6.9%) accounted for almost equal proportions of blindness. Participants aged 50–64 years (odds ratio [OR]: 1.7; 95% confidence interval [CI]: 1.2–2.6); 65 years and older (OR: 6.6; 95% CI: 4.3–10.2); and those diagnosed with systemic hypertension (OR: 2.5; 95% CI: 1.9–3.2) and diabetes mellitus (OR: 2.9; 95% CI: 1.9–4.4) had increased risk of VI.Conclusion: The prevalence of VI in this population is relatively high. The main causes of VI are correctable, suggesting the need for improved measures to prevent avoidable VI.Contribution: The study addresses the gap in the province’s current prevalence, causes and factors related to VI.

Intravitreal Dexamethasone Implant in Anti-Vascular Endothelial Growth Factor Pretreated Diabetic Macular Edema-A Swiss Cohort Study.

Diabetic macular edema (DME) is a significant cause of visual impairment, often treated with anti-vascular endothelial growth factor (anti-VEGF) agents. However, some patients do not respond adequately to this treatment. This study aims to evaluate the contribution of the intravitreal dexamethasone (DEX) implant as a second-line treatment in DME patients with insufficient response to anti-VEGF therapy or with high treatment burden. This retrospective multicenter cohort study was conducted across seven clinical sites in Switzerland. The study included eyes with active DME that had been pretreated with anti-VEGF for at least six months before receiving DEX therapy. Data were extracted from electronic patient records, focusing on best-corrected visual acuity (BCVA), central subfield thickness (CST), and injection frequency. A total of 95 eyes from 89 patients (38.8% females, mean age 65.6 ± 9.1 years, follow-up time 80.6 ± 38.5 [13.5-166.7] months) were analyzed. Prior to the first DEX implant, eyes had undergone an average of 16.0 ± 13.3 anti-VEGF injections over 32.5 ± 22.4 months. Post-DEX treatment, 22.1% of eyes received DEX monotherapy, 44.2% received a combination of DEX and anti-VEGF, 25.3% continued with anti-VEGF monotherapy, and 8.4% received no further treatment. The number of anti-VEGF injections decreased significantly from 6.4 ± 3.1 in the year before DEX to 1.6 ± 2.4 in the year after DEX (p < 0.001). BCVA remained stable (0.4 ± 0.3 logMAR at baseline, 0.4 ± 0.5 logMAR at 24 months, p = 0.2), while CST improved from 477.7 ± 141.0 to 320.4 ± 125.5 μm (p < 0.001), and the presence of retinal fluid decreased from 98.0% to 61.1% (p = 0.021). During follow-up, 26.3% of eyes required glaucoma medication, 4.2% underwent glaucoma surgery, and 1.1% needed cataract surgery. In real-world clinical settings, the addition of DEX to anti-VEGF therapy in DME patients significantly reduces treatment burden and retinal fluid while maintaining visual function. Treatment decisions should balance anatomical and functional outcomes, considering individual patient needs.

Red Blood Cell Transfusion for Incidence of Retinopathy of Prematurity: Prospective Multicenter Cohort Study.

Retinopathy of prematurity (ROP) is a leading cause of visual impairment and blindness in preterm infants. This study sought to investigate the association between red blood cell (RBC) transfusion and ROP in very preterm infants (VPIs) to inform clinical strategies for ROP prevention and treatment. We designed a prospective multicenter cohort study that included VPIs and follow-up data from January 2017 to December 2022 at 3 neonatal clinical medicine centers. They were categorized into a transfusion group (infants who received an RBC transfusion within 4 wk) and a nontransfusion group. The relationship between RBC transfusion and ROP incidence was assessed using binary logistic regression, with subgroup analyses based on gestational age, birth weight, sex, and sepsis status. Inverse probability of treatment weighting and propensity score matching were applied to account for all potential confounding factors that could affect ROP development, followed by sensitivity analysis. The study included 832 VPIs, including 327 in the nontransfusion group and 505 in the transfusion group. The transfusion group had a lower average birth weight and gestational age and a greater incidence of ROP, ≥stage 2 ROP, and severe ROP. Logistic regression analysis revealed that the transfusion group had a significantly greater risk of ROP (adjusted odds ratio [aOR] 1.70, 95% CI 1.14-2.53, P=.009) and ≥stage 2 ROP (aOR 1.68, 95% CI 1.02-2.78, P=.04) but not severe ROP (aOR 1.75, 95% CI 0.61-5.02, P=.30). The trend analysis also revealed an increased risk of ROP with an increasing number of transfusions and a larger volume of blood transfused (P for trend<.001). Subgroup analyses confirmed a consistent trend, with the transfusion group at a higher risk for ROP across all subgroups. Inverse probability of treatment weighting and propensity score matching analyses supported the initial findings. For VPIs, RBC transfusion significantly increases the risk of ROP, and the risk increases with an increasing number of transfusions and volume of blood transfused.

FK962 protects retinal ganglion cell under hypoxia/reoxygenation: Possible involvement of glial cell line-derived neurotrophic factor signaling pathway

Loss of retinal ganglion cells (RGCs) is the cause of visual impairment and blindness in glaucoma. Previously, our studies showed that FK962 (N-[1-acetylpiperidin-4-yl]-4-fluorobenzamide) promoted neurite elongation in rat RGCs and trigeminal ganglion (TG) cells. In TG cells, glial cell line-derived neurotrophic factor (GDNF) is known to be involved in the mechanism. The purpose of the present study is to investigate whether, 1) FK962 shows an RGC-protective effect under hypoxia/reoxygenation (H/R) and 2) GDNF is involved in the neuroprotective mechanism of FK962. Rat primary retinal cells were cultured under 24-h hypoxia/24-h reoxygenation conditions, with or without FK962, recombinant GDNF, GDNF antibody and RET receptor tyrosine kinase inhibitor, GSK3179106. Cells were co-immunostained with RBPMS and Neurofilament 200 as a RGC marker, and the number of survived RGCs was counted. Results showed H/R treatment decreased the number of survived RGCs. FK962 promoted RGC survival under H/R by a bell-shaped dose response, with the highest RGC-protective effect of 10−8 M. The protective effect was the same level with 10−12 M exogenous GDNF. Addition of GDNF antibody or GSK3179106 counteracted the neuroprotective effect of FK962. From these results, it is suggested that FK962 ameliorates RGC death under H/R, possibly via a GDNF signaling pathway.

Integrated Assessment of OCT, Multimodal Imaging, and Cytokine Markers for Predicting Treatment Responses in Retinal Vein Occlusion Associated Macular Edema: A Comparative Review of Anti-VEGF and Steroid Therapies.

Retinal vein occlusion (RVO) is a significant cause of vision loss, characterized by the occlusion of retinal veins, leading to conditions such as central retinal vein occlusion (CRVO) and branch retinal vein occlusion (BRVO). Macular edema (ME), a prevalent consequence of RVO, is the primary cause of vision impairment in affected patients. Anti-VEGF agents have become the standard treatment, showing efficacy in improving visual acuity (VA) and reducing ME. However, a subset of patients exhibit a suboptimal response to anti-VEGF therapy, necessitating alternative treatments. Corticosteroids, which address inflammatory pathways implicated in ME, have shown promise, particularly in cases resistant to anti-VEGF. This review aims to identify biomarkers that predict treatment response to corticosteroids in RVO-associated ME, utilizing multimodal imaging and cytokine assessments. Baseline imaging, including SD-OCT and OCT-A, is essential for evaluating biomarkers like hyperreflective foci (HRF), serous retinal detachment (SRF), and central retinal thickness (CRT). Elevated cytokine levels, such as IL-6 and MCP-1, correlate with ME severity and poor anti-VEGF response. Early identification of these biomarkers can guide timely transitions to corticosteroid therapy, potentially enhancing treatment outcomes. The practical conclusion of this review is that integrating biomarker assessment into clinical practice enables personalized treatment decisions, allowing for earlier and more effective management of RVO-associated ME by transitioning patients to corticosteroid therapy when anti-VEGF agents are insufficient. Advanced diagnostics and machine learning may further refine personalized treatment strategies, improving the management of RVO-associated ME.

Keeping Patients Safe Online: A Study of Refractive Surgery Advertisements on Social Media.

Introduction Refractive error is the leading cause of visual impairment and blindness globally. Increasingly, patients are exposed to information about refractive surgery through social media advertisements. While national guidelines specify how refractive surgery should be advertised in traditional media, it is unclear to what extent these standards are adhered to in the emerging commercial arena of social media. The adherence of refractive surgery advertisements on social mediato professional standards is poorly studied. Method We retrospectively analyzed the content of refractive surgery advertisements on the social media platform "TikTok," shown in the United Kingdom (UK) from October 2022 to October 2023, and compared them to the guidelines set out by The Royal College of Ophthalmologists (RCOphth) and the Advertising Standards Authority (ASA). Results We found that 39/51 (76%) of advertisements did not state the specific pathology to be corrected, and 41/51 (80%) did not specify a surgical procedure. Additionally, 33/51 (65%) of advertisements included at least onefinancial inducement, 44/51 (86%) contained misleading claims. None of the analyzed advertisements provided specific prices, offered refractive surgery as a competition prize, or featured celebrity endorsements. No medical jargon was found in any of the advertisements. The most viewed advertisement was seen by over 1.2 million unique users, with the median number of views for all advertisements being 34,000. Conclusion In conclusion, our analysis revealed that none of the refractive surgery advertisements on a popular social media platform met the standards set by RCOphth or ASA. This study presents the first qualitative analysis of social media refractive surgery advertisements, offering insights into what users can expect and providing recommendations for patients, doctors, social media platforms, and regulators to enhance refractive surgery advertising in the future.

CA-ViT: Contour-Guided and Augmented Vision Transformers to Enhance Glaucoma Classification Using Fundus Images.

Glaucoma, a predominant cause of visual impairment on a global scale, poses notable challenges in diagnosis owing to its initially asymptomatic presentation. Early identification is vital to prevent irreversible vision impairment. Cutting-edge deep learning techniques, such as vision transformers (ViTs), have been employed to tackle the challenge of early glaucoma detection. Nevertheless, limited approaches have been suggested to improve glaucoma classification due to issues like inadequate training data, variations in feature distribution, and the overall quality of samples. Furthermore, fundus images display significant similarities and slight discrepancies in lesion sizes, complicating glaucoma classification when utilizing ViTs. To address these obstacles, we introduce the contour-guided and augmented vision transformer (CA-ViT) for enhanced glaucoma classification using fundus images. We employ a Conditional Variational Generative Adversarial Network (CVGAN) to enhance and diversify the training dataset by incorporating conditional sample generation and reconstruction. Subsequently, a contour-guided approach is integrated to offer crucial insights into the disease, particularly concerning the optic disc and optic cup regions. Both the original images and extracted contours are given to the ViT backbone; then, feature alignment is performed with a weighted cross-entropy loss. Finally, in the inference phase, the ViT backbone, trained on the original fundus images and augmented data, is used for multi-class glaucoma categorization. By utilizing the Standardized Multi-Channel Dataset for Glaucoma (SMDG), which encompasses various datasets (e.g., EYEPACS, DRISHTI-GS, RIM-ONE, REFUGE), we conducted thorough testing. The results indicate that the proposed CA-ViT model significantly outperforms current methods, achieving a precision of 93.0%, a recall of 93.08%, an F1 score of 92.9%, and an accuracy of 93.0%. Therefore, the integration of augmentation with the CVGAN and contour guidance can effectively enhance glaucoma classification tasks.

Long-term follow-up results and visual outcomes of childhood glaucoma in the black sea region of turkey.

To investigate long-term visual outcomes and factors associated with low vision in patients with childhood glaucoma. A retrospective review was conducted on the medical records of pediatric glaucoma patients at the Ondokuz Mayıs University Ophthalmology Clinic from 2005 to 2023. The patients were categorized into three groups: primary congenital glaucoma (PCG), secondary childhood glaucoma, and glaucoma following cataract surgery (GFCS). Groups were analyzed regarding visual acuity (VA), ocular conditions and comorbidities, and the cause of visual impairment. The study also investigated the potential risk factors associated with visual impairment. A total of 105 eyes of 60 patients with a mean age of 9.7 ± 5.5years were included in the study. The mean VA in logMAR was 0.59 ± 0.52. At the final follow-up, 34.1% had good VA (≥ 20/50), 29.5% had moderate VA (20/50-20/200), and 36.4% had poor VA (< 20/200). The final mean intraocular pressure (IOP) was 16.2 ± 6.2mmHg. Amblyopia was the leading cause of vision loss (38.2%), followed by glaucomatous damage (36.4%). Patients with GFCS had a higher rate of visual impairment (42.4%) and refractive error. The results of the regression analysis showed that low vision was associated with undergoing more than two surgeries, high IOP at baseline, high initial and final cup-to-disc (C/D) ratio, and high initial central corneal thickness (CCT) (CI 95%, p = 0.018, p= 0.017, p = 0.013, p = 0.003, p = 0.001, respectively). Good VA can be achieved in 34.1% of childhood glaucoma cases. However, the VA prognosis may be worse in patients with GFCS. Achieving good visual outcomes in childhood glaucoma requires timely and effective treatment, consideration of risk factors, and management of amblyopia and ocular comorbidities.

Artificial intelligence in myopia in children: current trends and future directions.

Myopia is one of the major causes of visual impairment globally, with myopia and its complications thus placing a heavy healthcare and economic burden. With most cases of myopia developing during childhood, interventions to slow myopia progression are most effective when implemented early. To address this public health challenge, artificial intelligence has emerged as a potential solution in childhood myopia management. The bulk of artificial intelligence research in childhood myopia was previously focused on traditional machine learning models for the identification of children at high risk for myopia progression. Recently, there has been a surge of literature with larger datasets, more computational power, and more complex computation models, leveraging artificial intelligence for novel approaches including large-scale myopia screening using big data, multimodal data, and advancing imaging technology for myopia progression, and deep learning models for precision treatment. Artificial intelligence holds significant promise in transforming the field of childhood myopia management. Novel artificial intelligence modalities including automated machine learning, large language models, and federated learning could play an important role in the future by delivering precision medicine, improving health literacy, and allowing the preservation of data privacy. However, along with these advancements in technology come practical challenges including regulation and clinical integration.

Prevalence, Characteristics, and Management of Pediatric Ocular Trauma in Riyadh, Saudi Arabia: A Retrospective Analysis.

Ocular trauma is a major cause of visual impairment; however, little is known about its burden in Saudi Arabia. Therefore, this study aimed to determine the epidemiological characteristics of ocular trauma in pediatric patients in Riyadh, Saudi Arabia. Medical records of pediatric patients diagnosed with eye injuries between January 2016 and December 2020 were retrospectively reviewed. Demographic and injury characteristics were collected, and ocular trauma injuries were classified according to the Birmingham Eye Trauma Terminology. A total of 855 injured patients were included in the study, of whom 525 (61.4%) were boys. Patient age ranged from one month to 18 years. Most ocular injuries occurred in children aged 5-9 years. The injuries were more prevalent in boys than in girls. Closed globe injuries accounted for 70% of cases, open globe injuries for 21%, and other injuries for 9%. Most ocular injuries occurred at home (n = 87, 42%), followed by school (n = 61, 30%). These results may inform the implementation and targeting of interventions to reduce or prevent eye injuries in children. Further, they highlight the importance of well-planned prevention programs to prevent eye injuries from occurring in children's daily lives.

Pain perception enhancement in consecutive second-eye phacoemulsification cataract surgeries under topical anesthesia.

Cataract is the main cause of visual impairment and blindness worldwide while the only effective cure for cataract is still surgery. Consecutive phacoemulsification under topical anesthesia has been the routine procedure for cataract surgery. However, patients often grumbled that they felt more painful during the second-eye surgery compared to the first-eye surgery. The intraoperative pain experience has negative influence on satisfaction and willingness for second-eye cataract surgery of patients with bilateral cataracts. Intraoperative ocular pain is a complicated process induced by the nociceptors activation in the peripheral nervous system. Immunological, neuropsychological, and pharmacological factors work together in the enhancement of intraoperative pain. Accumulating published literatures have focused on the pain enhancement during the second-eye phacoemulsification surgeries. In this review, we searched PubMed database for articles associated with pain perception differences between consecutive cataract surgeries published up to Feb. 1, 2024. We summarized the recent research progress in mechanisms and interventions for pain perception enhancement in consecutive second-eye phacoemulsification cataract surgeries. This review aimed to provide novel insights into strategies for improving patients' intraoperative experience in second-eye cataract surgeries.