Automatic Segmentation of Retinal Vasculature

Segmentation of retinal vessels from retinal fundus images is the key step in the automatic retinal image analysis. In this paper, we propose a new unsupervised automatic method to segment the retinal vessels from retinal fundus images. Contrast enhancement and illumination correction are carried out through a series of image processing steps followed by adaptive histogram equalization and anisotropic diffusion filtering. This image is then converted to a gray scale using weighted scaling. The vessel edges are enhanced by boosting the detail curvelet coefficients. Optic disk pixels are removed before applying fuzzy C-mean classification to avoid the misclassification. Morphological operations and connected component analysis are applied to obtain the segmented retinal vessels. The performance of the proposed method is evaluated using DRIVE database to be able to compare with other state-of-art supervised and unsupervised methods. The overall segmentation accuracy of the proposed method is 95.18% which outperforms the other algorithms.Comment: Published at IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 201

The economics of vision impairment and its leading causes: A systematic review

Vision impairment (VI) can have wide ranging economic impact on individuals, households, and health systems. The aim of this systematic review was to describe and summarise the costs associated with VI and its major causes. We searched MEDLINE (16 November 2019), National Health Service Economic Evaluation Database, the Database of Abstracts of Reviews of Effects and the Health Technology Assessment database (12 December 2019) for partial or full economic evaluation studies, published between 1 January 2000 and the search dates, reporting cost data for participants with VI due to an unspecified cause or one of the seven leading causes globally: cataract, uncorrected refractive error, diabetic retinopathy, glaucoma, age-related macular degeneration, corneal opacity, trachoma. The search was repeated on 20 January 2022 to identify studies published since our initial search. Included studies were quality appraised using the British Medical Journal Checklist for economic submissions adapted for cost of illness studies. Results were synthesized in a structured narrative. Of the 138 included studies, 38 reported cost estimates for VI due to an unspecified cause and 100 reported costs for one of the leading causes. These 138 studies provided 155 regional cost estimates. Fourteen studies reported global data; 103/155 (66%) regional estimates were from high-income countries. Costs were most commonly reported using a societal (n = 48) or healthcare system perspective (n = 25). Most studies included only a limited number of cost components. Large variations in methodology and reporting across studies meant cost estimates varied considerably. The average quality assessment score was 78% (range 35–100%); the most common weaknesses were the lack of sensitivity analysis and insufficient disaggregation of costs. There was substantial variation across studies in average treatment costs per patient for most conditions, including refractive error correction (range $12–$201 ppp), cataract surgery (range $54–$3654 ppp), glaucoma (range $351–$1354 ppp) and AMD (range $2209–$7524 ppp). Future cost estimates of the economic burden of VI and its major causes will be improved by the development and adoption of a reference case for eye health. This could then be used in regular studies, particularly in countries with data gaps, including low- and middle-income countries in Asia, Eastern Europe, Oceania, Latin America and sub-Saharan Africa

Three-year treatment outcomes of Aflibercept versus Ranibizumab for diabetic macular edema:: Data from the Fight Retinal Blindness! Registry

PURPOSE Compare the 3-year outcomes of ranibizumab versus aflibercept in eyes with diabetic macular edema in daily practice. METHODS This was a retrospective analysis of naive diabetic macular edema eyes starting intravitreal injections of ranibizumab (0.5 mg) or aflibercept (2 mg) from January 1, 2013 to December 31, 2017 that were collected in the Fight Retinal Blindness! Registry. RESULTS We identified 534 eyes (ranibizumab-267 and aflibercept-267) of 402 patients. The adjusted mean (95% confidence interval) visual acuity change of +1.3 (-0.1 to 4.2) letters in the ranibizumab group and +2.4 (-0.2 to 5.1) letters (P = 0.001) in the aflibercept group at 3 years was not clinically different. However, the adjusted mean CST change seemed to remain significantly different throughout the 3-year period with higher reductions in favor of aflibercept (-87.8 [-108.3 to -67.4] µm for ranibizumab vs. -114.4 [-134.4 to -94.3] for aflibercept; P < 0.01). When baseline visual impairment was moderate (visual acuity ≤68 Early Treatment Diabetic Retinopathy Study letters), we found a faster improvement in visual acuity in eyes treated with aflibercept up until 18 months of treatment than eyes treated with ranibizumab, which then stayed similar until 36 months of treatment, whereas there was no apparent difference when baseline visual impairment was mild (visual acuity ≥69 Early Treatment Diabetic Retinopathy Study letters). The rate of serious adverse events was low. CONCLUSION Aflibercept and ranibizumab were both effective and safe for diabetic macular edema over 3 years

Artificial Intelligence in Assessing Cardiovascular Diseases and Risk Factors via Retinal Fundus Images: A Review of the Last Decade

Background: Cardiovascular diseases (CVDs) continue to be the leading cause of mortality on a global scale. In recent years, the application of artificial intelligence (AI) techniques, particularly deep learning (DL), has gained considerable popularity for evaluating the various aspects of CVDs. Moreover, using fundus images and optical coherence tomography angiography (OCTA) to diagnose retinal diseases has been extensively studied. To better understand heart function and anticipate changes based on microvascular characteristics and function, researchers are currently exploring the integration of AI with non-invasive retinal scanning. Leveraging AI-assisted early detection and prediction of cardiovascular diseases on a large scale holds excellent potential to mitigate cardiovascular events and alleviate the economic burden on healthcare systems. Method: A comprehensive search was conducted across various databases, including PubMed, Medline, Google Scholar, Scopus, Web of Sciences, IEEE Xplore, and ACM Digital Library, using specific keywords related to cardiovascular diseases and artificial intelligence. Results: A total of 87 English-language publications, selected for relevance were included in the study, and additional references were considered. This study presents an overview of the current advancements and challenges in employing retinal imaging and artificial intelligence to identify cardiovascular disorders and provides insights for further exploration in this field. Conclusion: Researchers aim to develop precise disease prognosis patterns as the aging population and global CVD burden increase. AI and deep learning are transforming healthcare, offering the potential for single retinal image-based diagnosis of various CVDs, albeit with the need for accelerated adoption in healthcare systems.Comment: 40 pages, 5 figures, 2 tables, 91 reference

A core outcome set for the treatment of pregnant women with pregestational diabetes:an international consensus study

Objective: To develop a core outcome set (COS) for randomised controlled trials (RCTs) evaluating the effectiveness of interventions for the treatment of pregnant women with pregestational diabetes mellitus (PGDM). Design: A consensus developmental study. Setting: International. Population: Two hundred and five stakeholders completed the first round. Methods: The study consisted of three components. (1) A systematic review of the literature to produce a list of outcomes reported in RCTs assessing the effectiveness of interventions for the treatment of pregnant women with PGDM. (2) A three-round, online eDelphi survey to prioritise these outcomes by international stakeholders (including healthcare professionals, researchers and women with PGDM). (3) A consensus meeting where stakeholders from each group decided on the final COS. Main outcome measures: All outcomes were extracted from the literature. Results: We extracted 131 unique outcomes from 67 records meeting the full inclusion criteria. Of the 205 stakeholders who completed the first round, 174/205 (85%) and 165/174 (95%) completed rounds 2 and 3, respectively. Participants at the subsequent consensus meeting chose 19 outcomes for inclusion into the COS: trimester-specific haemoglobin A1c, maternal weight gain during pregnancy, severe maternal hypoglycaemia, diabetic ketoacidosis, miscarriage, pregnancy-induced hypertension, pre-eclampsia, maternal death, birthweight, large for gestational age, small for gestational age, gestational age at birth, preterm birth, mode of birth, shoulder dystocia, neonatal hypoglycaemia, congenital malformations, stillbirth and neonatal death. Conclusions: This COS will enable better comparison between RCTs to produce robust evidence synthesis, improve trial reporting and optimise research efficiency in studies assessing treatment of pregnant women with PGDM. Tweetable abstract: 165 key stakeholders have developed #Treatment #CoreOutcomes in pregnant women with #diabetes existing before pregnancy.</p

An Image Quality Selection and Effective Denoising on Retinal Images Using Hybrid Approaches

Retinal image analysis has remained an essential topic of research in the last decades. Several algorithms and techniques have been developed for the analysis of retinal images. Most of these techniques use benchmark retinal image datasets to evaluate performance without first exploring the quality of the retinal image. Hence, the performance metrics evaluated by these approaches are uncertain. In this paper, the quality of the images is selected by utilizing the hybrid naturalness image quality evaluator and the perception-based image quality evaluator (hybrid NIQE-PIQE) approach. Here, the raw input image quality score is evaluated using the Hybrid NIQE-PIQE approach. Based on the quality score value, the deep learning convolutional neural network (DCNN) categorizes the images into low quality, medium quality and high quality images. Then the selected quality images are again pre-processed to remove the noise present in the images. The individual green channel (G-channel) is extracted from the selected quality RGB images for noise filtering. Moreover, hybrid modified histogram equalization and homomorphic filtering (Hybrid G-MHE-HF) are utilized for enhanced noise filtering. The implementation of proposed scheme is implemented on MATLAB 2021a. The performance of the implemented method is compared with the other approaches to the accuracy, sensitivity, specificity, precision and F-score on DRIMDB and DRIVE datasets. The proposed scheme’s accuracy is 0.9774, sensitivity is 0.9562, precision is 0.99, specificity is 0.99, and F-measure is 0.9776 on the DRIMDB dataset, respectively

Effectiveness of interventions to increase uptake and completion of treatment for diabetic retinopathy in low- and middle-income countries: a rapid review protocol.

BACKGROUND: Vision loss due to diabetic retinopathy can largely be prevented or delayed through treatment. Patients with vision-threatening diabetic retinopathy are typically offered laser or intravitreal injections which often require more than one treatment cycle. However, treatment is not always initiated, or it is not completed, resulting in poor visual outcomes. Interventions aimed at improving the uptake or completion of treatment for diabetic retinopathy can potentially help prevent or delay visual loss in people with diabetes. METHODS: We will search MEDLINE, Embase, Global Health and Cochrane Register of Studies for studies reporting interventions to improve the uptake of treatment for diabetic retinopathy (DR) and/or diabetic macular oedema (DMO), compared with usual care, in adults with diabetes. The review will include studies published in the last 20 years in the English language. We will include any study design that measured any of the following outcomes in relation to treatment uptake and completion for DR and/or DMO: (1) proportion of patients initiating treatment for DR and/or DMO among those to whom it is recommended, (2) proportion of patients completing treatment for DR and/or DMO among those to whom it is recommended, (3) proportion of patients completing treatment for DR and/or DMO among those initiating treatment and (4) number and proportion of DR and/or DMO rounds of treatment completed per patient, as dictated by the treatment protocol. For included studies, we will also report any measures of cost-effectiveness when available. Two reviewers will screen search results independently. Risk of bias assessment will be done by two reviewers, and data extraction will be done by one reviewer with verification of 10% of the papers by a second reviewer. The results will be synthesised narratively. DISCUSSION: This rapid review aims to identify and synthesise the peer-reviewed literature on the effectiveness of interventions to increase uptake and completion of treatment for DR and/or DMO in LMICs. The rapid review methodology was chosen in order to rapidly synthesise the available evidence to support programme implementers and policy-makers in designing evidence-based health programmes and public health policy and inform the allocation of resources. SYSTEMATIC REVIEW REGISTRATION: OSF osf.io/h5wgr