The association between retinal vascular geometry changes and diabetic retinopathy and their role in prediction of progression: an exploratory study

Background: The study describes the relationship of retinal vascular geometry (RVG) to severity of diabetic retinopathy (DR), and its predictive role for subsequent development of proliferative diabetic retinopathy (PDR). Methods. The research project comprises of two stages. Firstly, a comparative study of diabetic patients with different grades of DR. (No DR: Minimal non-proliferative DR: Severe non-proliferative DR: PDR) (10:10: 12: 19). Analysed RVG features including vascular widths and branching angles were compared between patient cohorts. A preliminary statistical model for determination of the retinopathy grade of patients, using these features, is presented. Secondly, in a longitudinal predictive study, RVG features were analysed for diabetic patients with progressive DR over 7 years. RVG at baseline was examined to determine risk for subsequent PDR development. Results: In the comparative study, increased DR severity was associated with gradual vascular dilatation (p = 0.000), and widening of the bifurcating angle (p = 0.000) with increase in smaller-child-vessel branching angle (p = 0.027). Type 2 diabetes and increased diabetes duration were associated with increased vascular width (p = <0.05 In the predictive study, at baseline, reduced small-child vascular width (OR = 0.73 (95 CI 0.58-0.92)), was predictive of future progression to PDR. Conclusions: The study findings suggest that RVG alterations can act as novel markers indicative of progression of DR severity and establishment of PDR. RVG may also have a potential predictive role in determining the risk of future retinopathy progression. © 2014 Habib et al.; licensee BioMed Central Ltd

Uncertainty assessment of vessels width measurement from intensity profile model fitting in fundus images

Arteriolar-to-venular diameter ratio (AVR) is an important clinical measurement that allows to characterize retinal vascular abnormalities. A reliable AVR measurement requires accurate and reproducible width measurement. However, in order to measure the vessel width automatically, an approximation of the intensity profile is required by fitting a model. The aim of the proposed study is to assess the uncertainties introduced in the vessel width measurements when choosing a specific distribution as an intensity profile model. Different models are described and an automatic vessel width measurement procedure is presented. The uncertainty introduced by each model is evaluated by computing the standard deviation of the difference between the automatic and the manual measurements. The results show that the intensity profile model should be chosen according to the relative width of the targeted vessels

Joint segmentation and classification of retinal arteries/veins from fundus images

Objective Automatic artery/vein (A/V) segmentation from fundus images is required to track blood vessel changes occurring with many pathologies including retinopathy and cardiovascular pathologies. One of the clinical measures that quantifies vessel changes is the arterio-venous ratio (AVR) which represents the ratio between artery and vein diameters. This measure significantly depends on the accuracy of vessel segmentation and classification into arteries and veins. This paper proposes a fast, novel method for semantic A/V segmentation combining deep learning and graph propagation. Methods A convolutional neural network (CNN) is proposed to jointly segment and classify vessels into arteries and veins. The initial CNN labeling is propagated through a graph representation of the retinal vasculature, whose nodes are defined as the vessel branches and edges are weighted by the cost of linking pairs of branches. To efficiently propagate the labels, the graph is simplified into its minimum spanning tree. Results The method achieves an accuracy of 94.8% for vessels segmentation. The A/V classification achieves a specificity of 92.9% with a sensitivity of 93.7% on the CT-DRIVE database compared to the state-of-the-art-specificity and sensitivity, both of 91.7%. Conclusion The results show that our method outperforms the leading previous works on a public dataset for A/V classification and is by far the fastest. Significance The proposed global AVR calculated on the whole fundus image using our automatic A/V segmentation method can better track vessel changes associated to diabetic retinopathy than the standard local AVR calculated only around the optic disc.Comment: Preprint accepted in Artificial Intelligence in Medicin

Retinal Imaging in Alzheimer’s Disease

Identifying biomarkers of Alzheimer's disease (AD) will accelerate the understanding of its pathophysiology, facilitate screening and risk stratification, and aid in developing new therapies. Developments in non-invasive retinal imaging technologies, including optical coherence tomography (OCT), OCT angiography and digital retinal photography, have provided a means to study neuronal and vascular structures in the retina in people with AD. Both qualitative and quantitative measurements from these retinal imaging technologies (eg, thinning of peripapillary retinal nerve fibre layer, inner retinal layer, and choroidal layer, reduced capillary density, abnormal vasodilatory response) have been shown to be associated with cognitive function impairment and risk of AD. The development of computer algorithms for respective retinal imaging methods has further enhanced the potential of retinal imaging as a viable tool for rapid, early detection and screening of AD. In this review, we present an update of current retinal imaging techniques and their potential applications in AD research. We also discuss the newer retinal imaging techniques and future directions in this expanding field

Suitability of UK biobank retinal images for automatic analysis of morphometric properties of the vasculature

To assess the suitability of retinal images held in the UK Biobank--the largest retinal data repository in a prospective population-based cohort--for computer assisted vascular morphometry, generating measures that are commonly investigated as candidate biomarkers of systemic disease.Non-mydriatic fundus images from both eyes of 2,690 participants--people with a self-reported history of myocardial infarction (n=1,345) and a matched control group (n=1,345)--were analysed using VAMPIRE software. These images were drawn from those of 68,554 UK Biobank participants who underwent retinal imaging at recruitment. Four operators were trained in the use of the software to measure retinal vascular tortuosity and bifurcation geometry.Total operator time was approximately 360 hours (4 minutes per image). 2,252 (84%) of participants had at least one image of sufficient quality for the software to process, i.e. there was sufficient detection of retinal vessels in the image by the software to attempt the measurement of the target parameters. 1,604 (60%) of participants had an image of at least one eye that was adequately analysed by the software, i.e. the measurement protocol was successfully completed. Increasing age was associated with a reduced proportion of images that could be processed (p=0.0004) and analysed (p<0.0001). Cases exhibited more acute arteriolar branching angles (p=0.02) as well as lower arteriolar and venular tortuosity (p<0.0001).A proportion of the retinal images in UK Biobank are of insufficient quality for automated analysis. However, the large size of the UK Biobank means that tens of thousands of images are available and suitable for computational analysis. Parametric information measured from the retinas of participants with suspected cardiovascular disease was significantly different to that measured from a matched control group

Hypertensive eye disease

Hypertensive eye disease includes a spectrum of pathological changes, the most well known being hypertensive retinopathy. Other commonly involved parts of the eye in hypertension include the choroid and optic nerve, sometimes referred to as hypertensive choroidopathy and hypertensive optic neuropathy. Together, hypertensive eye disease develops in response to acute and/or chronic elevation of blood pressure. Major advances in research over the past three decades have greatly enhanced our understanding of the epidemiology, systemic associations and clinical implications of hypertensive eye disease, particularly hypertensive retinopathy. Traditionally diagnosed via a clinical funduscopic examination, but increasingly documented on digital retinal fundus photographs, hypertensive retinopathy has long been considered a marker of systemic target organ damage (for example, kidney disease) elsewhere in the body. Epidemiological studies indicate that hypertensive retinopathy signs are commonly seen in the general adult population, are associated with subclinical measures of vascular disease and predict risk of incident clinical cardiovascular events. New technologies, including development of non-invasive optical coherence tomography angiography, artificial intelligence and mobile ocular imaging instruments, have allowed further assessment and understanding of the ocular manifestations of hypertension and increase the potential that ocular imaging could be used for hypertension management and cardiovascular risk stratification