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
Abraham Olvera‐BarriosTjebo HeerenKonstantinos BalaskasRyan ChambersLouis BolterCatherine EganAdnan TufailJohn Anderson
38
Citation
29
Reference
10
Related Paper
Citation Trend
Abstract:
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.Keywords:
Grading (engineering)
Fundus (uterus)
Cotton wool spots
Cotton-wool spots are a hallmark of human immunodeficiency virus (HIV) retinopathy in the acquired immunodeficiency syndrome (AIDS). We analysed the half-life of cotton-wool spots in AIDS in a prospective study, and found the average time to disappearance to be 6.9 weeks. HIV retinopathy differs from diabetic retinopathy in having a smaller size cotton-wool spot and a much shorter half-life, suggesting a patchy involvement of the retinal capillaries in AIDS and a widespread capillary disease in preproliferative and proliferative diabetic retinopathy.
Cotton wool spots
Cite
Citations (25)
Abstract: Diabetic retinopathy is the leading cause of blindness in diabetic patients. Screening of diabetic retinopathy using fundus image is the most effective way. As the time increases this DR leads to permanent loss of vision. At present, Diabetic retinopathy is still being treated by hand by an ophthalmologist which is a time-consuming process. Computer aided and fully automatic diagnosis of DR plays an important role in now a day. Data-set containing a collection of fundus images of different severity scale is used to analyze the fundus image of DR patients. Here the deep neural network model is trained by using this fundus image and five-degree classification task is performed. We were able to produce an sensitivity of 90%. Keywords: Confusion matrix, Deep convolutional Neural Network, Diabetic Retinopathy, Fundus image, OCT
Fundus (uterus)
Confusion matrix
Confusion
Cite
Citations (1)
Diabetic retinopathy is the leading cause of blindness among people of ages 20 to 44 and is the second leading cause of blindness between the ages of 45 to 74. Most patients are unaware of the changes within their eyes. Primary medical providers caring for diabetic patients should receive greater training in fundus examination techniques and the recognition of significant retinopathy. Primary medical providers should be encouraged to perform fundus examinations at 6-month or annual routine examinations of all diabetics, and be made aware of the value of detection and treatment of high risk eyes.
Fundus (uterus)
Eye examination
Cite
Citations (2)
Diabetic retinopathy, an asymptomatic problem of diabetes, is one of the leading sources of blindness worldwide. The primary detection and diagnosis can decrease the incidence of severe vision loss due to diabetes. Therefore, the present study was conducted to design an experiment in order to diagnose symptomless clinical stages of diabetic retinopathy, i.e., progressive diabetic retinopathy and nonproliferative diabetic retinopathy subjectively and objectively. The diagnostic confirmation of diabetic retinopathy depends on the reliable detection and classification of bright lesions, such as exudates and cotton wool spots, and dark lesions, such as: microan-eurysms and hemorrhages, present in retinal fundus images. However, variations in the retinal fundus images make it difficult to discriminate dark and bright lesions in the existence of landmarks, like blood vessels and optic disk. Thus, it is essential to remove any spurious and false areas caused by anatomical structures before the segmentation of retinal lesions. In addition, to design an efficient computer-aided diagnostic method, a benchmark composite database, having variable characteristics, such as position, dimensions, shapes, and color is required. Keeping all these facts in mind, a composite experimental methodology is designed in this study for an effective analysis of the computer-aided solution for the diagnosis of diabetic retinopathy.
Cotton wool spots
Fundus (uterus)
Cite
Citations (0)
Fundus (uterus)
Fundus camera
Optic disc
Cite
Citations (37)
Diabetic retinopathy is a condition of the eye of diabetic patients where the retina is damaged because of long-term diabetes. The condition deteriorates towards irreversible blindness in extreme cases of diabetic retinopathy. Hence, early detection of diabetic retinopathy is important to prevent blindness. Regular screening of fundus images of diabetic patients could be helpful in preventing blindness caused by diabetic retinopathy. In this paper, we propose techniques for staging of diabetic retinopathy in fundus images using several shape and texture features computed from detected microaneurysms, exudates, and hemorrhages. The classification accuracy is reported in terms of the area (Az) under the receiver operating characteristic curve using 200 fundus images from the MESSIDOR database. The value of Az for classifying normal images versus mild, moderate, and severe nonproliferative diabetic retinopathy (NPDR) is 0:9106. The value of Az for classification of mild NPDR versus moderate and severe NPDR is 0:8372. The Az value for classification of moderate NPDR and severe NPDR is 0:9750.
Fundus (uterus)
Cite
Citations (3)
Background: Diabetic retinopathy is a disease caused due by complications of diabetes mellitus which can lead to blindness. About 33% of the US population with diabetes also show symptoms for diabetes retinopathy. If not treated, diabetic retinopathy worsens over time by progressing through two main pathological stages of non-proliferative and proliferative and four clinical stages. While the diagnostic accuracy of detecting diabetic retinopathy through machine learning have shown to be successful for OCT images, the accuracy of ultra-widefield fundus images have yet to be fully reported. This paper describes a method to non-invasively detect and diagnose diabetic retinopathy from ultra-widefield fundus images.
Fundus (uterus)
Cite
Citations (0)
High blood glucose levels cause lesions on the retina of the eye, resulting in a degenerative condition known as diabetic retinopathy (DR), which impacts vision and can cause irreversible vision loss. The most common cause of blindness in diabetic people is thought to be diabetic retinopathy. Early diagnosis of diabetic retinopathy is essential to efficiently maintaining the patient's vision. We attempted to give first-hand verification to this fundamental problem of DR detection to save time, money and efforts of ophthalmologists. The latter also proved to be more challenging, especially early on in the disease, when disease characteristics are less obvious in the fundus images. Deep learning algorithms and machine learning-based medical image analysis have aided in the early identification of diabetic retinopathy along with the evaluation of retinal fundus images. This paper attempts to preprocess and binary classify fundus images from the famous Aptos dataset using finetuned ResNet50 as well as features extraction from ResNet50 and later classifying using machine learning models. We have achieved an accuracy of 0.9802, an AUC score of 0.9937, F1 score of 0.9870, a precision of 0.9890, a recall as 0.9845 and kappa score of 0.9481 on the evaluation data by fine-tuning of ResNet50.
Fundus (uterus)
Cite
Citations (2)
To identify whether tessellated fundus unrelated to myopia modifies the rate of diabetic patients with retinopathy.Diabetic patients evaluated ophthalmoscopically for the first time from September 1998 to December 1999 were retrospectively reviewed, and the presence of diabetic retinopathy was compared with those with tessellated fundus (group 1) and without (group 2). Duration of diabetes and presence of retinopathy were evaluated. Differences were analysed with x2.621 patients were evaluated, 138 of whom (22%) had tessellated fundus. The rate of patients with diabetic retinopathy was statistically lower in group 1 than in group 2 (p = 0.00); this difference was maintained after correction for cumulated diabetes duration.The presence of tessellated fundus was associated with a lower rate of patients with diabetic retinopathy and with delayed onset. Manifestations of diabetic retinopathy in patients with tessellated fundus might have a different expression, and this expression might not necessarily reflect the microvascular status of the patient.
Fundus (uterus)
Choroid
Cite
Citations (4)
Abstract Background:Purtscher’s retinopathy is an occlusive microvasculopathy caused by traumatic injuries, especially like head and chest injuries. The exact mechanism of injury remains unclear, and Most case reports about Purtscher’s retinopathy focused on the symptoms of fundus, and did not provide detailed medical treatments. Case presentation: In this case, the patient suffered suddenly vision loss in the right eye one day after a traffic accident with moderate head and chest injuries. Ocular examinations revealed typical characteristics of cotton wool spots, and minimal intraretinal hemorrhages around the optic disc. 5 different drugs were used in this patient. 15 days after the treatment (corticosteroids and traditional Chinese medicine), the vision of the patient was improved and the symptoms (cotton wool spots, hemorrhages, retinal edema and visual field) were alleviated obviously. Conclusions: Corticosteroids and traditional Chinese medicines used in our case did improve the vision of the patient, and alleviated the symptoms of fundus.
Cotton wool spots
Fundus (uterus)
Presentation (obstetrics)
Case presentation
Cite
Citations (1)