The evidence for automated grading in diabetic retinopathy screening

Peer reviewedPostprin

Automatic Screening and Classification of Diabetic Retinopathy Eye Fundus Image

Diabetic Retinopathy (DR) is a disorder of the retinal vasculature. It develops to some degree in nearly all patients with long-standing diabetes mellitus and can result in blindness. Screening of DR is essential for both early detection and early treatment. This thesis aims to investigate automatic methods for diabetic retinopathy detection and subsequently develop an effective system for the detection and screening of diabetic retinopathy. The presented diabetic retinopathy research involves three development stages. Firstly, the thesis presents the development of a preliminary classification and screening system for diabetic retinopathy using eye fundus images. The research will then focus on the detection of the earliest signs of diabetic retinopathy, which are the microaneurysms. The detection of microaneurysms at an early stage is vital and is the first step in preventing diabetic retinopathy. Finally, the thesis will present decision support systems for the detection of diabetic retinopathy and maculopathy in eye fundus images. The detection of maculopathy, which are yellow lesions near the macula, is essential as it will eventually cause the loss of vision if the affected macula is not treated in time. An accurate retinal screening, therefore, is required to assist the retinal screeners to classify the retinal images effectively. Highly efficient and accurate image processing techniques must thus be used in order to produce an effective screening of diabetic retinopathy. In addition to the proposed diabetic retinopathy detection systems, this thesis will present a new dataset, and will highlight the dataset collection, the expert diagnosis process and the advantages of the new dataset, compared to other public eye fundus images datasets available. The new dataset will be useful to researchers and practitioners working in the retinal imaging area and would widely encourage comparative studies in the field of diabetic retinopathy research. It is envisaged that the proposed decision support system for clinical screening would greatly contribute to and assist the management and the detection of diabetic retinopathy. It is also hoped that the developed automatic detection techniques will assist clinicians to diagnose diabetic retinopathy at an early stage

Diagnostic accuracy of diabetic retinopathy grading by an artificial intelligence-enabled algorithm compared with a human standard for wide-field true-colour confocal scanning and standard digital retinal images.

Background: Photographic diabetic retinopathy screening requires labour-intensive grading of retinal images by humans. Automated retinal image analysis software (ARIAS) could provide an alternative to human grading. We compare the performance of an ARIAS using true-colour, wide-field confocal scanning images and standard fundus images in the English National Diabetic Eye Screening Programme (NDESP) against human grading. Methods: Cross-sectional study with consecutive recruitment of patients attending annual diabetic eye screening. Imaging with mydriasis was performed (two-field protocol) with the EIDON platform (CenterVue, Padua, Italy) and standard NDESP cameras. Human grading was carried out according to NDESP protocol. Images were processed by EyeArt V.2.1.0 (Eyenuk Inc, Woodland Hills, California). The reference standard for analysis was the human grade of standard NDESP images. Results: We included 1257 patients. Sensitivity estimates for retinopathy grades were: EIDON images; 92.27% (95% CI: 88.43% to 94.69%) for any retinopathy, 99% (95% CI: 95.35% to 100%) for vision-threatening retinopathy and 100% (95% CI: 61% to 100%) for proliferative retinopathy. For NDESP images: 92.26% (95% CI: 88.37% to 94.69%) for any retinopathy, 100% (95% CI: 99.53% to 100%) for vision-threatening retinopathy and 100% (95% CI: 61% to 100%) for proliferative retinopathy. One case of vision-threatening retinopathy (R1M1) was missed by the EyeArt when analysing the EIDON images, but identified by the human graders. The EyeArt identified all cases of vision-threatening retinopathy in the standard images. Conclusion: EyeArt identified diabetic retinopathy in EIDON images with similar sensitivity to standard images in a large-scale screening programme, exceeding the sensitivity threshold recommended for a screening test. Further work to optimise the identification of ‘no retinopathy’ and to understand the differential lesion detection in the two imaging systems would enhance the use of these two innovative technologies in a diabetic retinopathy screening setting

Diabetic Macular Edema Grading Based on Deep Neural Networks

Diabetic Macular Edema (DME) is a major cause of vision loss in diabetes. Its early detection and treatment is therefore a vital task in management of diabetic retinopathy. In this paper, we propose a new featurelearning approach for grading the severity of DME using color retinal fundus images. An automated DME diagnosis system based on the proposed featurelearning approach is developed to help early diagnosis of the disease and thus averts (or delays) its progression. It utilizes the convolutional neural networks (CNNs) to identify and extract features of DME automatically without any kind of user intervention. The developed prototype was trained and assessed by using an existing MESSIDOR dataset of 1200 images. The obtained preliminary results showed accuracy of (88.8 %), sensitivity (74.7%) and specificity (96.5 %). These results compare favorably to state-of-the-art findings with the added benefit of an automatic feature-learning approach rather than a time-consuming handcrafted approach

Diabetic retinopathy screening: global and local perspective

Diabetes mellitus has become a global epidemic. It causes significant macrovascular complications such as coronary artery disease, peripheral artery disease, and stroke; as well as microvascular complications such as retinopathy, nephropathy, and neuropathy. Diabetic retinopathy is known to be the leading cause of blindness in the working-age population and may be asymptomatic until vision loss occurs. Screening for diabetic retinopathy has been shown to reduce blindness by timely detection and effective laser treatment. Diabetic retinopathy screening is being done worldwide either as a national screening programme or hospital-based project or as a community-based screening programme. In this article, we review different methods of screening including grading used to detect the severity of sight-threatening retinopathy and the newer screening methods. This review also includes the method of systematic screening being carried out in Hong Kong, a system that has helped to identify diabetic retinopathy among all attendees in public primary care clinics using a Hong Kong–wide public patients’ database.published_or_final_versio

In Vivo Multimodal Imaging of Drusenoid Lesions in Rhesus Macaques.

Nonhuman primates are the only mammals to possess a true macula similar to humans, and spontaneously develop drusenoid lesions which are hallmarks of age-related macular degeneration (AMD). Prior studies demonstrated similarities between human and nonhuman primate drusen based on clinical appearance and histopathology. Here, we employed fundus photography, spectral domain optical coherence tomography (SD-OCT), fundus autofluorescence (FAF), and infrared reflectance (IR) to characterize drusenoid lesions in aged rhesus macaques. Of 65 animals evaluated, we identified lesions in 20 animals (30.7%). Using the Age-Related Eye Disease Study 2 (AREDS2) grading system and multimodal imaging, we identified two distinct drusen phenotypes - 1) soft drusen that are larger and appear as hyperreflective deposits between the retinal pigment epithelium (RPE) and Bruchs membrane on SD-OCT, and 2) hard, punctate lesions that are smaller and undetectable on SD-OCT. Both exhibit variable FAF intensities and are poorly visualized on IR. Eyes with drusen exhibited a slightly thicker RPE compared with control eyes (+3.4 μm, P=0.012). Genetic polymorphisms associated with drusenoid lesions in rhesus monkeys in ARMS2 and HTRA1 were similar in frequency between the two phenotypes. These results refine our understanding of drusen development, and provide insight into the absence of advanced AMD in nonhuman primates

Automated retinal analysis

Diabetes is a chronic disease affecting over 2% of the population in the UK [1]. Long-term complications of diabetes can affect many different systems of the body including the retina of the eye. In the retina, diabetes can lead to a disease called diabetic retinopathy, one of the leading causes of blindness in the working population of industrialised countries. The risk of visual loss from diabetic retinopathy can be reduced if treatment is given at the onset of sight-threatening retinopathy. To detect early indicators of the disease, the UK National Screening Committee have recommended that diabetic patients should receive annual screening by digital colour fundal photography [2]. Manually grading retinal images is a subjective and costly process requiring highly skilled staff. This thesis describes an automated diagnostic system based oil image processing and neural network techniques, which analyses digital fundus images so that early signs of sight threatening retinopathy can be identified. Within retinal analysis this research has concentrated on the development of four algorithms: optic nerve head segmentation, lesion segmentation, image quality assessment and vessel width measurements. This research amalgamated these four algorithms with two existing techniques to form an integrated diagnostic system. The diagnostic system when used as a 'pre-filtering' tool successfully reduced the number of images requiring human grading by 74.3%: this was achieved by identifying and excluding images without sight threatening maculopathy from manual screening

SDOCT Imaging to Identify Macular Pathology in Patients Diagnosed with Diabetic Maculopathy by a Digital Photographic Retinal Screening Programme

INTRODUCTION: Diabetic macular edema (DME) is an important cause of vision loss. England has a national systematic photographic retinal screening programme to identify patients with diabetic eye disease. Grading retinal photographs according to this national protocol identifies surrogate markers for DME. We audited a care pathway using a spectral-domain optical coherence tomography (SDOCT) clinic to identify macular pathology in this subset of patients. METHODS: A prospective audit was performed of patients referred from screening with mild to moderate non-proliferative diabetic retinopathy (R1) and surrogate markers for diabetic macular edema (M1) attending an SDOCT clinic. The SDOCT images were graded by an ophthalmologist as SDOCT positive, borderline or negative. SDOCT positive patients were referred to the medical retina clinic. SDOCT negative and borderline patients were further reviewed in the SDOCT clinic in 6 months. RESULTS: From a registered screening population of 17 551 patients with diabetes mellitus, 311 patients met the inclusion criteria between (March 2008 and September 2009). We analyzed images from 311 patients' SDOCT clinic episodes. There were 131 SDOCT negative and 12 borderline patients booked for revisit in the OCT clinic. Twenty-four were referred back to photographic screening for a variety of reasons. A total of 144 were referred to ophthalmology with OCT evidence of definite macular pathology requiring review by an ophthalmologist. DISCUSSION: This analysis shows that patients with diabetes, mild to moderate non-proliferative diabetic retinopathy (R1) and evidence of diabetic maculopathy on non-stereoscopic retinal photographs (M1) have a 42.1% chance of having no macular edema on SDOCT imaging as defined by standard OCT definitions of DME when graded by a retinal specialist. SDOCT imaging is a useful adjunct to colour fundus photography in screening for referable diabetic maculopathy in our screening population

Prospective evaluation of an artificial intelligence-enabled algorithm for automated diabetic retinopathy screening of 30 000 patients.

BACKGROUND/AIMS: Human grading of digital images from diabetic retinopathy (DR) screening programmes represents a significant challenge, due to the increasing prevalence of diabetes. We evaluate the performance of an automated artificial intelligence (AI) algorithm to triage retinal images from the English Diabetic Eye Screening Programme (DESP) into test-positive/technical failure versus test-negative, using human grading following a standard national protocol as the reference standard. METHODS: Retinal images from 30 405 consecutive screening episodes from three English DESPs were manually graded following a standard national protocol and by an automated process with machine learning enabled software, EyeArt v2.1. Screening performance (sensitivity, specificity) and diagnostic accuracy (95% CIs) were determined using human grades as the reference standard. RESULTS: Sensitivity (95% CIs) of EyeArt was 95.7% (94.8% to 96.5%) for referable retinopathy (human graded ungradable, referable maculopathy, moderate-to-severe non-proliferative or proliferative). This comprises sensitivities of 98.3% (97.3% to 98.9%) for mild-to-moderate non-proliferative retinopathy with referable maculopathy, 100% (98.7%,100%) for moderate-to-severe non-proliferative retinopathy and 100% (97.9%,100%) for proliferative disease. EyeArt agreed with the human grade of no retinopathy (specificity) in 68% (67% to 69%), with a specificity of 54.0% (53.4% to 54.5%) when combined with non-referable retinopathy. CONCLUSION: The algorithm demonstrated safe levels of sensitivity for high-risk retinopathy in a real-world screening service, with specificity that could halve the workload for human graders. AI machine learning and deep learning algorithms such as this can provide clinically equivalent, rapid detection of retinopathy, particularly in settings where a trained workforce is unavailable or where large-scale and rapid results are needed