scholarly journals Sensitivity and specificity of automated analysis of single-field non-mydriatic fundus photographs by Bosch DR Algorithm—Comparison with mydriatic fundus photography (ETDRS) for screening in undiagnosed diabetic retinopathy

PLoS ONE ◽  
2017 ◽  
Vol 12 (12) ◽  
pp. e0189854 ◽  
Author(s):  
Pritam Bawankar ◽  
Nita Shanbhag ◽  
S. Smitha K. ◽  
Bodhraj Dhawan ◽  
Aratee Palsule ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Sensitivity And Specificity ◽  
Automated Analysis ◽  
Fundus Photography ◽  
Algorithm Comparison ◽  
Fundus Photographs ◽  
Single Field

Computer-aided Analysis of Fundus Photographs

2011 ◽  
Vol 05 (02) ◽  
pp. 104
Author(s):  
Luisa Ribeiro ◽  
Rui Bernardes ◽  
José Cunha-Vaz ◽  
◽  
◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Imaging Modality ◽  
Retinal Disease ◽  
Automated Analysis ◽  
Fundus Photography ◽  
Individual Risk ◽  
Invasive Procedures ◽  
Non Invasive ◽  
Computer Aided Analysis ◽  
Computer Aided

Colour fundus photography is the most frequently used imaging modality because it is non-invasive, well accepted by patients and above all, because it allows documentation and automated analysis of the ophthalmoscopic examination. Colour fundus photography is considered crucial for diabetic retinopathy management to identify disease and its progression in clinical practice. This article focuses on automated computer-aided analysis of fundus digital photographs with special emphasis on microaneurysm dynamics. Together with optical coherence tomography measurements of extracellular space and retinal thickness, both based on non-invasive procedures, this technique allows close follow-up of the main changes in the diabetic retina. Determination of the activity of the retinal disease and individual risk profiles using these non-invasive procedures contribute to personalised management of diabetic retinopathy by identifying eyes at risk from vision-threatening complications, such as macular oedema and proliferative retinopathy.

Single-field fundus photography for diabetic retinopathy screening

Ophthalmology ◽  
2004 ◽  
Vol 111 (5) ◽  
pp. 1055-1062 ◽  
Author(s):  
George A Williams ◽  
Ingrid U Scott ◽  
Julia A Haller ◽  
Albert M Maguire ◽  
Dennis Marcus ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Fundus Photography ◽  
Diabetic Retinopathy Screening ◽  
Single Field ◽  
Retinopathy Screening

scholarly journals Use of Smartphones to Detect Diabetic Retinopathy: Scoping Review and Meta-Analysis of Diagnostic Test Accuracy Studies (Preprint)

2019 ◽  
Author(s):  
Choon Han Tan ◽  
Bhone Myint Kyaw ◽  
Helen Smith ◽  
Colin S Tan ◽  
Lorainne Tudor Car
Keyword(s):  
Diabetic Retinopathy ◽  
Diagnostic Accuracy ◽  
Diagnostic Test ◽  
Sensitivity And Specificity ◽  
Meta Analysis ◽  
Diagnostic Test Accuracy ◽  
Fundus Photography ◽  
Diabetic Patients ◽  
Test Accuracy ◽  
Reference Standard

BACKGROUND Diabetic retinopathy (DR), a common complication of diabetes mellitus, is the leading cause of impaired vision in adults worldwide. Smartphone ophthalmoscopy involves using a smartphone camera for digital retinal imaging. Utilizing smartphones to detect DR is potentially more affordable, accessible, and easier to use than conventional methods. OBJECTIVE This study aimed to determine the diagnostic accuracy of various smartphone ophthalmoscopy approaches for detecting DR in diabetic patients. METHODS We performed an electronic search on the Medical Literature Analysis and Retrieval System Online (MEDLINE), EMBASE, and Cochrane Library for literature published from January 2000 to November 2018. We included studies involving diabetic patients, which compared the diagnostic accuracy of smartphone ophthalmoscopy for detecting DR to an accurate or commonly employed reference standard, such as indirect ophthalmoscopy, slit-lamp biomicroscopy, and tabletop fundus photography. Two reviewers independently screened studies against the inclusion criteria, extracted data, and assessed the quality of included studies using the Quality Assessment of Diagnostic Accuracy Studies–2 tool, with disagreements resolved via consensus. Sensitivity and specificity were pooled using the random effects model. A summary receiver operating characteristic (SROC) curve was constructed. This review is reported in line with the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies guidelines. RESULTS In all, nine studies involving 1430 participants were included. Most studies were of high quality, except one study with limited applicability because of its reference standard. The pooled sensitivity and specificity for detecting any DR was 87% (95% CI 74%-94%) and 94% (95% CI 81%-98%); mild nonproliferative DR (NPDR) was 39% (95% CI 10%-79%) and 95% (95% CI 91%-98%); moderate NPDR was 71% (95% CI 57%-81%) and 95% (95% CI 88%-98%); severe NPDR was 80% (95% CI 49%-94%) and 97% (95% CI 88%-99%); proliferative DR (PDR) was 92% (95% CI 79%-97%) and 99% (95% CI 96%-99%); diabetic macular edema was 79% (95% CI 63%-89%) and 93% (95% CI 82%-97%); and referral-warranted DR was 91% (95% CI 86%-94%) and 89% (95% CI 56%-98%). The area under SROC curve ranged from 0.879 to 0.979. The diagnostic odds ratio ranged from 11.3 to 1225. CONCLUSIONS We found heterogeneous evidence showing that smartphone ophthalmoscopy performs well in detecting DR. The diagnostic accuracy for PDR was highest. Future studies should standardize reference criteria and classification criteria and evaluate other available forms of smartphone ophthalmoscopy in primary care settings.

scholarly journals Transforming Retinal Photographs to Entropy Images in Deep Learning to Improve Automated Detection for Diabetic Retinopathy

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Gen-Min Lin ◽  
Mei-Juan Chen ◽  
Chia-Hung Yeh ◽  
Yu-Yang Lin ◽  
Heng-Yu Kuo ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Sensitivity And Specificity ◽  
Block Size ◽  
Detection Accuracy ◽  
Data Sets ◽  
Feature Maps ◽  
Data Set ◽  
Fundus Photographs ◽  
Grade 3

Entropy images, representing the complexity of original fundus photographs, may strengthen the contrast between diabetic retinopathy (DR) lesions and unaffected areas. The aim of this study is to compare the detection performance for severe DR between original fundus photographs and entropy images by deep learning. A sample of 21,123 interpretable fundus photographs obtained from a publicly available data set was expanded to 33,000 images by rotating and flipping. All photographs were transformed into entropy images using block size 9 and downsized to a standard resolution of 100 × 100 pixels. The stages of DR are classified into 5 grades based on the International Clinical Diabetic Retinopathy Disease Severity Scale: Grade 0 (no DR), Grade 1 (mild nonproliferative DR), Grade 2 (moderate nonproliferative DR), Grade 3 (severe nonproliferative DR), and Grade 4 (proliferative DR). Of these 33,000 photographs, 30,000 images were randomly selected as the training set, and the remaining 3,000 images were used as the testing set. Both the original fundus photographs and the entropy images were used as the inputs of convolutional neural network (CNN), and the results of detecting referable DR (Grades 2–4) as the outputs from the two data sets were compared. The detection accuracy, sensitivity, and specificity of using the original fundus photographs data set were 81.80%, 68.36%, 89.87%, respectively, for the entropy images data set, and the figures significantly increased to 86.10%, 73.24%, and 93.81%, respectively (all p values <0.001). The entropy image quantifies the amount of information in the fundus photograph and efficiently accelerates the generating of feature maps in the CNN. The research results draw the conclusion that transformed entropy imaging of fundus photographs can increase the machinery detection accuracy, sensitivity, and specificity of referable DR for the deep learning-based system.

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