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Purpose: To assess and analyze the visual outcomes of patients with retinal vein occlusions in a real-world setting with a long-term follow-up of more than 5 years.Methods: Retrospective analysis of 56 patients having retinal vein occlusions from a tertiary eye center, with a mean follow-up of 7 years was performed. Primary outcome measures were mean change in best-corrected visual acuity (BCVA) from baseline at 6 months, 1 year, 2 years, 3 years, and final visit (≥5 years), proportion of patients having BCVA better than 20/40 and worse than 20/200, and mean number of injections. Secondary outcome measures were change in central macular thickness (CMT), development of subsequent retinal vein occlusion (RVO) in same eye or the other eye, and development of neovascular complications.Results: The mean change in letter score was + 11.84 in branch RVO (BRVO), +7.14 in non-ischemic central RVO (CRVO), and -9.5 in ischemic CRVO at 1 year, which changed to + 8.57, -5 and - 24, respectively, at the end of follow-up. CMT had improved from 506 ± 98.8 μm, 576.44 ± 149 μm, and 618 ± 178.27 μm, respectively, at baseline to 267 ± 94 μm, 345.20 ± 122.61 μm, and 265.50 ± 107.75 μm, respectively, in BRVO, non-ischemic, and ischemic hemi RVO (HRVO)/CRVO groups. The total mean number of injections given in BRVO, non-ischemic CRVO, and ischemic CRVO groups were 4.6, 6.6, and 4.1, respectively. None of the patients with BRVO developed neovascular glaucoma (NVG). Non-ischemic to ischemic HRVO/CRVO conversion was noted in 4/11 eyes at a mean duration of 12.6 months. NVG was noted in 7/9 eyes (77.8%) in initial ischemic CRVO/HRVO group and 3/4 (75%) converted eyes.Conclusion: Patients with BRVO have good visual outcomes with anti-VEGF, while in CRVO results may vary considerably owing to patient compliance and treatment burden on long-term follow-up in a real-world setting. [ABSTRACT FROM AUTHOR]

Purpose: Coats' disease is associated with poor outcomes, and there are limited studies on long-term outcomes of Coats' disease. The purpose of our study is to identify various predictive factors to help in prognosticating the treatment outcomes in advanced Coats' disease in children.Methods: This is a retrospective case series from a single tertiary eye care center of children (<18 years) diagnosed with coat's disease. Sixty-seven patients with Coat's disease were identified from the medical records from 2009 to 2020. Patients' demographic data, clinical presentation, stage, extent of involvement, detailed treatment history, clinical sequelae post-treatment (including complications and anatomical and functional outcomes) were noted. Binary logistic regression was performed to correlate the predictive factors for anatomical and functional improvement.Results: Of the 67 patients, 51 eyes of 51 patients were included in the study. The male to female proportion was 2.2. Mean age at presentation was 4.98 ± 3.55 years (range: 2 months-15 years). Mean duration of follow-up was 31.53 ± 26.38 months. Overall, our globe salvage rate was 92.2%. We found that vitreoretinal fibrosis (P < 0.001), subretinal gliosis (P < 0.001), vitreous hemorrhage (P = 0.02), tractional or combined retinal detachment (P < 0.001), foveal scar (P < 0.006), and cataract (P < 0.001) to be important factors to affect the outcome.Conclusion: Advanced stage of presentation (stage 3B and above), diffuse involvement, cataract, vitreoretinal fibrosis (preretinal and subretinal), vitreous hemorrhage, tractional or combined retinal detachment, and anterior hyaloid proliferation are poor prognostic factors for globe salvage in advanced disease. Subretinal gliotic nodule or scar and lack of visual rehabilitation suggest poor functional outcomes. [ABSTRACT FROM AUTHOR]

Purpose: A deep learning system (DLS) using artificial intelligence (AI) is emerging as a very promising technology in the future of healthcare diagnostics. While the concept of telehealth is emerging in every field of medicine, AI assistance in diagnosis can become a great tool for successful screening in telemedicine and teleophthalmology. The aim of our study was to assess the acceptability of AI-based retina screening.Methods: This was a prospective non-randomized study performed in the outpatient department of a tertiary eye care hospital. Patients older than 18 years who came for a regular eye check-up or a routine retina screening were recruited in the study. Fundus images of the posterior pole were captured on fundus on a phone camera (REMIDIOTM, India) with a built-in AI software (Netra.AI) that can identify normal versus abnormal retina. The patients were then given an 8-point questionnaire to assess their acceptance and willingness toward AI-based screening. We recruited 104 participants.Results: We found that 90.4% were willing for an AI-based fundus screening; 96.2% were satisfied with AI-based screening. Patients with diabetes (P = 0.03) and the male population (P = 0.029) were more satisfied with the AI-based screening. The majority (i.e., 97.1%) felt that AI-based screening gave them a better understanding of their eye condition and 37.5% felt that AI-based retina screening prior to a doctor's visit can help in routine screening.Conclusion: Considering the current COVID-19 pandemic situation across the globe, this study highlights the importance of AI-based telescreening and positive patient approach toward this technology. [ABSTRACT FROM AUTHOR]

Purpose: Intravitreal anti-vascular endothelial growth factor (VEGF) injection therapy has emerged as the mainstay of treatment in the management of diabetic macular edema (DME) today. Various systemic risk factors have to be considered before initiating anti-VEGF therapy. The aim of our study was to form a consensus on various systemic factors to consider before starting anti-VEGF therapy for DME. Methods: A questionnaire was created and sent across to various retina specialists across India. A GoogleTM form with various questions pertaining to what systemic parameters would one consider before giving anti-VEGF therapy for DME was sent to each of them by email/WhatsAppTM/direct telephonic interview. Results: Of the 650 retina specialists contacted, 322 responded to the questionnaire. There was no difference in responses between private and institutional practitioners. The majority would consider RBS (85%), HbA1c (61%), blood pressure (63%), and renal function (57%) as a routine before administering the anti-VEGF injection, while the majority would not consider hemoglobin (63%) or lipid profile (55%) of the patient as a routine practice prior to administering the injection. Conclusion: In our study, most VR specialists prefer to consider RBS, HbA1c, BP, and renal profile (creatinine) routinely prior to anti-VEGF injection. We suggest that it is important to consider blood pressure control, glycemic control, HbA1c, Hb, lipid profile, and renal profile (UACR, eGFR, and creatinine) prior to anti-VEGF therapy in all diabetic patients and to discuss the need for statins in patients with dyslipidemias with the physician. [ABSTRACT FROM AUTHOR]

Purpose: Amblyopia is a significant public health problem. Photoscreeners have been shown to have significant potential for screening; however, most are limited by cost and display low accuracy. The purpose of this study was validate a novel artificial intelligence (AI) and machine learning-based facial photoscreener "Kanna," and to determine its effectiveness in detecting amblyopia risk factors.Methods: A prospective study that included 654 patients aged below 18 years was conducted in our outpatient clinic. Using an android smartphone, three images of each the participants' face were captured by trained optometrists in dark and ambient light conditions and uploaded onto Kanna. Deep learning was used to create an amblyopia risk score based on our previous study. The algorithm generates a risk dashboard consisting of six values: five normalized risk scores for ptosis, strabismus, hyperopia, myopia and media opacities; and one binary value denoting if a child is "at-risk" or "not at-risk." The presence of amblyopia risk factors (ARF) as determined on the ophthalmic examination was compared with the Kanna photoscreener.Results: Correlated patient data for 654 participants were analyzed. The mean age of the study population was 7.87 years. The algorithm had an F-score, 85.9%; accuracy, 90.8%; sensitivity, 83.6%; specificity, 94.5%; positive predictive value, 88.4%; and negative predictive value, 91.9% in identifying amblyopia risk factors. The P value for the amblyopia risk calculation was 8.5 × 10-142 implying strong statistical significance.Conclusion: The Kanna photo-based screener that uses deep learning to analyze photographs is an effective alternative for screening children for amblyopia risk factors. [ABSTRACT FROM AUTHOR]

Purpose: Deep learning is a newer and advanced subfield in artificial intelligence (AI). The aim of our study is to validate a machine-based algorithm developed based on deep convolutional neural networks as a tool for screening to detect referable diabetic retinopathy (DR).Methods: An AI algorithm to detect DR was validated at our hospital using an internal dataset consisting of 1,533 macula-centered fundus images collected retrospectively and an external validation set using Methods to Evaluate Segmentation and Indexing Techniques in the field of Retinal Ophthalmology (MESSIDOR) dataset. Images were graded by two retina specialists as any DR, prompt referral (moderate nonproliferative diabetic retinopathy (NPDR) or above or presence of macular edema) and sight-threatening DR/STDR (severe NPDR or above) and compared with AI results. Sensitivity, specificity, and area under curve (AUC) for both internal and external validation sets for any DR detection, prompt referral, and STDR were calculated. Interobserver agreement using kappa value was calculated for both the sets and two out of three agreements for DR grading was considered as ground truth to compare with AI results.Results: In the internal validation set, the overall sensitivity and specificity was 99.7% and 98.5% for Any DR detection and 98.9% and 94.84%for Prompt referral respectively. The AUC was 0.991 and 0.969 for any DR detection and prompt referral respectively. The agreement between two observers was 99.5% and 99.2% for any DR detection and prompt referral with a kappa value of 0.94 and 0.96, respectively. In the external validation set (MESSIDOR 1), the overall sensitivity and specificity was 90.4% and 91.0% for any DR detection and 94.7% and 97.4% for prompt referral, respectively. The AUC was. 907 and. 960 for any DR detection and prompt referral, respectively. The agreement between two observers was 98.5% and 97.8% for any DR detection and prompt referral with a kappa value of 0.971 and 0.980, respectively.Conclusion: With increasing diabetic population and growing demand supply gap in trained resources, AI is the future for early identification of DR and reducing blindness. This can revolutionize telescreening in ophthalmology, especially where people do not have access to specialized health care. [ABSTRACT FROM AUTHOR]

Objective: Aqueous root extract of Oroxylum indicum was evaluated in rats against dinitrobenzene sulfonic acid (DNBS) induced colitis. Materials and Methods: Rats were pre-treated orally for seven days and continued for four days after the induction of colitis with OIaq (100, 200, and 400 mg/kg) or vehicle. Colitis was induced by intracolonic instillation of 25 mg of DNBS per rat dissolved in 50% alcohol and 4 days later, the colonic mucosal damage was analyzed along with food intake, body weight, colon weight, spleen weight, histological damage, myeloperoxidase (MPO) activity, malondialdehyde (MDA) levels, reduced glutathione (GSH), and nitric oxide levels in colonic tissue homogenate. Results: Significant reduction in gross damage area, weight loss and increase in colonic and spleen weight were evident in test substance-pretreated animals' dose dependently as compared to vehicle treated control. These effects were confirmed biochemically by a reduction in colonic myeloperoxidase activity, malondialdehyde levels, nitric oxide levels, and increase in reduced glutathione (GSH) levels. Furthermore, microscopic examination revealed diminution of inflammatory cell infiltration and submucosal edema in colon segments of rats treated with OIaq. Conclusion: The results demonstrate the protective effect of OIaq in the animal model of acute colitis possibly through an antioxidant, anti-lipoperoxidative or due to reduction in nitric oxide generation. [ABSTRACT FROM AUTHOR]

Keywords: Lens dislocation; suprachoroidal lens; trauma; vitrectomy Posttraumatic lens dislocation into anterior chamber,[[1]] subconjunctival space,[[2]] vitreous cavity, and subretinal space [[3]],[[4]] has been reported. Lens dislocation, suprachoroidal lens, trauma, vitrectomy. [Extracted from the article]