Chronic kidney disease is an emerging health problem worldwide. The eye shares striking structural, developmental, and genetic pathways with the kidney, suggesting that kidney disease and ocular ...disease may be closely linked. A growing number of studies have found associations of chronic kidney disease with age-related macular degeneration, diabetic retinopathy, glaucoma, and cataract. In addition, retinal microvascular parameters have been shown to be predictive of chronic kidney disease. Chronic kidney disease shares common vascular risk factors including diabetes, hypertension, smoking, and obesity, and pathogenetic mechanisms including inflammation, oxidative stress, endothelial dysfunction, and microvascular dysfunction, with ocular diseases supporting the ‘Common Soil Hypothesis.’ In this review, we present major epidemiological evidence for these associations and explore underlying pathogenic mechanisms and common risk factors for kidney and ocular disease. Understanding the link between kidney and ocular disease can lead to the development of new treatment and screening strategies for both diseases.
Lutein is a carotenoid with reported anti-inflammatory properties. A large body of evidence shows that lutein has several beneficial effects, especially on eye health. In particular, lutein is known ...to improve or even prevent age-related macular disease which is the leading cause of blindness and vision impairment. Furthermore, many studies have reported that lutein may also have positive effects in different clinical conditions, thus ameliorating cognitive function, decreasing the risk of cancer, and improving measures of cardiovascular health. At present, the available data have been obtained from both observational studies investigating lutein intake with food, and a few intervention trials assessing the efficacy of lutein supplementation. In general, sustained lutein consumption, either through diet or supplementation, may contribute to reducing the burden of several chronic diseases. However, there are also conflicting data concerning lutein efficacy in inducing favorable effects on human health and there are no univocal data concerning the most appropriate dosage for daily lutein supplementation. Therefore, based on the most recent findings, this review will focus on lutein properties, dietary sources, usual intake, efficacy in human health, and toxicity.
Digital solutions for dry eye disease Ribot, Francesc March
Acta ophthalmologica (Oxford, England),
December 2022, 2022-12-00, 20221201, Letnik:
100, Številka:
S275
Journal Article
Recenzirano
Purpose: Present a novel solution to screen‐induced dry eye symptoms that is cheap, easily deployable and scalable.
Methods: Device presentation. Research presentation. A previously validated ...questionnaire for screen‐induced dry eye symptoms was applied to participants after a 30‐minute read on a laptop while the blink rate was recorded on camera. This happens twice for each participant; once with Digital Blinking Stimuli (DiBS) on and once with DiBS off. Each participant is randomized to start with either DiBS on or off without her/his knowledge. DiBS is an innovative computer program that produces screen distortion during 100 ms every 4 s to induce participants to blink at a normal rate while looking at screens. Results from both groups were compared with a Student T‐test.
Results: The results showed a significant reduction in the eye soreness, eye strain and tearing subscores with DiBS on. Additionally, with DiBS on, a significant increase in the blink rate was observed.
Conclusions: DiBS is a promising new technology that may aid in decreasing screen‐induced dry eye symptoms while at the same time being cheap and easy to deploy.
Visual hallucinations are common in older people and are especially associated with ophthalmological and neurological disorders, including dementia and Parkinson's disease. Uncertainties remain ...whether there is a single underlying mechanism for visual hallucinations or they have different disease-dependent causes. However, irrespective of mechanism, visual hallucinations are difficult to treat. The National Institute for Health Research (NIHR) funded a research programme to investigate visual hallucinations in the key and high burden areas of eye disease, dementia and Parkinson's disease, culminating in a workshop to develop a unified framework for their clinical management. Here we summarise the evidence base, current practice and consensus guidelines that emerged from the workshop.Irrespective of clinical condition, case ascertainment strategies are required to overcome reporting stigma. Once hallucinations are identified, physical, cognitive and ophthalmological health should be reviewed, with education and self-help techniques provided. Not all hallucinations require intervention but for those that are clinically significant, current evidence supports pharmacological modification of cholinergic, GABAergic, serotonergic or dopaminergic systems, or reduction of cortical excitability. A broad treatment perspective is needed, including carer support. Despite their frequency and clinical significance, there is a paucity of randomised, placebo-controlled clinical trial evidence where the primary outcome is an improvement in visual hallucinations. Key areas for future research include the development of valid and reliable assessment tools for use in mechanistic studies and clinical trials, transdiagnostic studies of shared and distinct mechanisms and when and how to treat visual hallucinations.
A deep learning system (DLS) is a machine learning technology with potential for screening diabetic retinopathy and related eye diseases.
To evaluate the performance of a DLS in detecting referable ...diabetic retinopathy, vision-threatening diabetic retinopathy, possible glaucoma, and age-related macular degeneration (AMD) in community and clinic-based multiethnic populations with diabetes.
Diagnostic performance of a DLS for diabetic retinopathy and related eye diseases was evaluated using 494 661 retinal images. A DLS was trained for detecting diabetic retinopathy (using 76 370 images), possible glaucoma (125 189 images), and AMD (72 610 images), and performance of DLS was evaluated for detecting diabetic retinopathy (using 112 648 images), possible glaucoma (71 896 images), and AMD (35 948 images). Training of the DLS was completed in May 2016, and validation of the DLS was completed in May 2017 for detection of referable diabetic retinopathy (moderate nonproliferative diabetic retinopathy or worse) and vision-threatening diabetic retinopathy (severe nonproliferative diabetic retinopathy or worse) using a primary validation data set in the Singapore National Diabetic Retinopathy Screening Program and 10 multiethnic cohorts with diabetes.
Use of a deep learning system.
Area under the receiver operating characteristic curve (AUC) and sensitivity and specificity of the DLS with professional graders (retinal specialists, general ophthalmologists, trained graders, or optometrists) as the reference standard.
In the primary validation dataset (n = 14 880 patients; 71 896 images; mean SD age, 60.2 2.2 years; 54.6% men), the prevalence of referable diabetic retinopathy was 3.0%; vision-threatening diabetic retinopathy, 0.6%; possible glaucoma, 0.1%; and AMD, 2.5%. The AUC of the DLS for referable diabetic retinopathy was 0.936 (95% CI, 0.925-0.943), sensitivity was 90.5% (95% CI, 87.3%-93.0%), and specificity was 91.6% (95% CI, 91.0%-92.2%). For vision-threatening diabetic retinopathy, AUC was 0.958 (95% CI, 0.956-0.961), sensitivity was 100% (95% CI, 94.1%-100.0%), and specificity was 91.1% (95% CI, 90.7%-91.4%). For possible glaucoma, AUC was 0.942 (95% CI, 0.929-0.954), sensitivity was 96.4% (95% CI, 81.7%-99.9%), and specificity was 87.2% (95% CI, 86.8%-87.5%). For AMD, AUC was 0.931 (95% CI, 0.928-0.935), sensitivity was 93.2% (95% CI, 91.1%-99.8%), and specificity was 88.7% (95% CI, 88.3%-89.0%). For referable diabetic retinopathy in the 10 additional datasets, AUC range was 0.889 to 0.983 (n = 40 752 images).
In this evaluation of retinal images from multiethnic cohorts of patients with diabetes, the DLS had high sensitivity and specificity for identifying diabetic retinopathy and related eye diseases. Further research is necessary to evaluate the applicability of the DLS in health care settings and the utility of the DLS to improve vision outcomes.
Medical artificial intelligence (AI) offers great potential for recognizing signs of health conditions in retinal images and expediting the diagnosis of eye diseases and systemic disorders
. However, ...the development of AI models requires substantial annotation and models are usually task-specific with limited generalizability to different clinical applications
. Here, we present RETFound, a foundation model for retinal images that learns generalizable representations from unlabelled retinal images and provides a basis for label-efficient model adaptation in several applications. Specifically, RETFound is trained on 1.6 million unlabelled retinal images by means of self-supervised learning and then adapted to disease detection tasks with explicit labels. We show that adapted RETFound consistently outperforms several comparison models in the diagnosis and prognosis of sight-threatening eye diseases, as well as incident prediction of complex systemic disorders such as heart failure and myocardial infarction with fewer labelled data. RETFound provides a generalizable solution to improve model performance and alleviate the annotation workload of experts to enable broad clinical AI applications from retinal imaging.
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