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Climate change has important implications on human health, affecting almost every system in the body. Multiple studies have raised the possibility of climate change impacting eye health. In this review, we aimed to summarize current literature on the impact of air pollution and climate change on eye health. We performed a search in four different databases, including Medline, Scopus, Cochrane, and Web of Sciences databases. The search strategy combined terms regarding eye health and environmental/climate changes. The outcome of interest included all eye conditions. The search yielded 2,051 unique articles. After applying inclusion and exclusion criteria, 61 articles were included in this systematic review with data covering 2,620,030 participants. Most studies originated from China, India, South Korea, and USA. Climate change adversely affected different eye conditions, with ocular surface diseases (e.g., conjunctivitis and dry eye) being most affected. Moreover, higher particulate matter (PM) was the most widely assessed pollutant and was adversely associated with the majority of eye conditions, increasing the burden on patients and healthcare providers. We found a low frequency of publications related to the delivery of eye care and its impact on climate change in countries with high air pollution and climate change burden. [ABSTRACT FROM AUTHOR]

Background Evidence on the practical application of artificial intelligence (AI)-based diabetic retinopathy (DR) screening is needed. Methods Consented participants were screened for DR using retinal imaging with AI interpretation from March 2021 to June 2021 at four diabetes clinics in Rwanda. Additionally, images were graded by a UK National Health System-certified retinal image grader. DR grades based on the International Classification of Diabetic Retinopathy with a grade of 2.0 or higher were considered referable. The AI system was designed to detect optic nerve and macular anomalies outside of DR. A vertical cup to disc ratio of 0.7 and higher and/or macular anomalies recognised at a cut-off of 60% and higher were also considered referable by AI. Results Among 827 participants (59.6% women (n=493)) screened by AI, 33.2% (n=275) were referred for follow-up. Satisfaction with AI screening was high (99.5%, n=823), and 63.7% of participants (n=527) preferred AI over human grading. Compared with human grading, the sensitivity of the AI for referable DR was 92% (95% CI 0.863%, 0.968%), with a specificity of 85% (95% CI 0.751%, 0.882%). Of the participants referred by AI: 88 (32.0%) were for DR only, 109 (39.6%) for DR and an anomaly, 65 (23.6%) for an anomaly only and 13 (4.73%) for other reasons. Adherence to referrals was highest for those referred for DR at 53.4%. Conclusion DR screening using AI led to accurate referrals from diabetes clinics in Rwanda and high rates of participant satisfaction, suggesting AI screening for DR is practical and acceptable. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI) appears capable of detecting diabetic retinopathy (DR) with a high degree of accuracy in adults; however, there are few studies in children and young adults. Methods: Children and young adults (3-26 years) with type 1 diabetes mellitus (T1DM) or type 2 diabetes mellitus (T2DM) were screened at the Dhaka BIRDEM-2 hospital, Bangladesh. All gradable fundus images were uploaded to Cybersight AI for interpretation. Two main outcomes were considered at a patient level: 1) Any DR, defined as mild non-proliferative diabetic retinopathy (NPDR or more severe; and 2) Referable DR, defined as moderate NPDR or more severe. Diagnostic test performance comparing Orbis International's Cybersight AI with the reference standard, a fully qualified optometrist certified in DR grading, was assessed using the Matthews correlation coefficient (MCC), area under the receiver operating characteristic curve (AUC-ROC), area under the precision-recall curve (AUC-PR), sensitivity, specificity, positive and negative predictive values. Results: Among 1274 participants (53.1% female, mean age 16.7 years), 19.4% (n = 247) had any DR according to AI. For referable DR, 2.35% (n = 30) were detected by AI. The sensitivity and specificity of AI for any DR were 75.5% (CI 69.7-81.3%) and 91.8% (CI 90.2-93.5%) respectively, and for referable DR, these values were 84.2% (CI 67.8-100%) and 98.9% (CI 98.3%-99.5%). The MCC, AUC-ROC and the AUC-PR for referable DR were 63.4, 91.2 and 76.2% respectively. AI was most successful in accurately classifying younger children with shorter duration of diabetes. Conclusions: Cybersight AI accurately detected any DR and referable DR among children and young adults, despite its algorithms having been trained on adults. The observed high specificity is particularly important to avoid over-referral in low-resource settings. AI may be an effective tool to reduce demands on scarce physician resources for the care of children with diabetes in low-resource settings. [ABSTRACT FROM AUTHOR]

To describe outcomes of patients with hepatitis C virus (HCV) seropositivity undergoing cataract surgery, as well as investigate risk factors for surgical complications. This is a retrospective cohort study of all consecutive patients who underwent cataract surgery at a tertiary care hospital in the United States between 2014 and 2019. The exposure of interest was HCV seropositivity and outcomes included surgical complications and associated risk factors, visual acuity, and post-operative complications. A total of 11,276 eyes of 6,858 patients were included in the study, of which 122 patients (1.78%) and 210 eyes (1.86%) were HCV positive. Average age at surgery was 63.4 (8.4) years for HCV positive patients and 69.1 (10.6) years for HCV negative patients. Patients with HCV were more likely to suffer a complication during cataract surgery, 2.9% versus 1.2% (OR 2.27, 95% CI 1.03 to 5.01, p =.0415). Postoperative best corrected visual acuity was excellent: median and range 0.00 (−0.13, 3.00) logMAR for HCV positive eyes versus 0.00 (−0.30, 3.00) logMAR for HCV negative eyes. Among HCV positive patients, elevated alanine transaminase (>52 U/L) was associated with a higher intraoperative complication rate (10.0% vs 1.8%, OR 5.53, 95% CI 1.05 to 29.2, p =.044). While patients with HCV are more likely to have complications during cataract surgery, final best corrected visual acuity was excellent regardless of HCV status. Patients with HCV are more likely to undergo cataract surgery at a younger age, and those with elevated alanine transaminase are at highest risk for complications. [ABSTRACT FROM AUTHOR]

Patients were grouped into three categories: normal BMI (BMI less than 25), overweight BMI (BMI between 25 and 30), and obese BMI (BMI greater than 30). Elevated body mass index (BMI) and increased abdominal girth are associated with vitreous pressure during cataract surgery. Surgeons at our institution do not use the standing phaco-emulsification technique.[3] Patients with a BMI classification of obese undergo cataract surgery at a younger age and are more likely to have diabetic retinopathy. [Extracted from the article]