Availability and variability in guidelines on diabetic retinopathy screening in Asian countries

Wang, Louis Zizhao; Cheung, Carol Y.; Tapp, Robyn J.; Hamzah, Haslina; Tan, Gavin Siew Wei; Ting, Daniel; Lamoureux, Ecosse L.; Wong, Tien Yin

doi:10.1136/bjophthalmol-2016-310002

Cited by 68 publications

(44 citation statements)

References 70 publications

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“…Retinopathy is characterized by damage to the blood vessels and neurones in the retina ( 7 ) and has been shown to be related to a longer duration of diabetes and hyperglycemia ( 8 ). Initially, there may be minimal alterations in vision, but as the condition progresses color vision may become impaired and eventually there may be total loss of vision ( 9 ). Whilst diabetic retinopathy (DR) is the most common cause of vision loss in people with diabetes mellitus; other causes, including glaucoma, age-related macular degeneration, and cataract must be assessed for ( 10 ).…”

Section: Introductionmentioning

confidence: 99%

Clinical and Biochemical Associations with Diabetic Retinopathy in Male Patients in the Gaza Strip

AbuMustafa

2017

Front. Endocrinol.

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BackgroundThere are limited data on the prevalence and risk factors for diabetic retinopathy (DR) in the Gaza Strip.ObjectiveTo assesses clinical and biochemical associated with DR in males with type 2 diabetes mellitus (T2DM) in the Gaza Strip.MethodsOne hundred and fifty males with T2DM from the Gaza Strip underwent a questionnaire interview, serum biochemical analysis, and assessment of their previous urine and blood results.ResultsThe prevalence of DR was 24.7%. The duration of diabetes and prevalence of neuropathy, nephropathy, cardiovascular disease, and recurrent infections were significantly higher among patients with DR compared with those without DR (p < 0.05). Serum urea, creatinine, glucose, cholesterol, and low-density lipoprotein cholesterol were significantly elevated, whilst eGFR and high-density lipoprotein cholesterol were significantly lower in patients with DR compared with patients without DR (p < 0.05). Urinary albumin concentration and albumin creatinine ratio (ACR) was higher in patients with DR. ACR correlated significantly with the duration of T2DM (r = 0.311, p < 0.001), glucose (r = 0.479, p < 0.001), urea (r = 0.337, p < 0.001), creatinine (r = 0.275, p = 0.001), and GFR (r = −0.275, p < 0.001).ConclusionThese data show a high prevalence of DR in an unselected cohort of patients with T2DM and relationships to modifiable risk factors in Gaza.

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Section: Introductionmentioning

confidence: 99%

Clinical and Biochemical Associations with Diabetic Retinopathy in Male Patients in the Gaza Strip

AbuMustafa

2017

Front. Endocrinol.

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“…This comes most commonly in the form of point-of-care ophthalmoscopy by trained eye care personnel (e.g., ophthalmologists or optometrists) or retinal photography with either local interpretation or telemedicine-based screening programs with centralized grading (8). However, despite growing evidence of the effectiveness of routine assessments and early intervention (9,10), comprehensive DR screening strategies are not widely implemented (11). This is largely because of the inadequate availability of resources, including trained eye care personnel and financing, to cope with the rapidly growing burden of diabetes.…”

mentioning

confidence: 99%

“…This is largely because of the inadequate availability of resources, including trained eye care personnel and financing, to cope with the rapidly growing burden of diabetes. This is particularly important in major developing countries such as China and Indonesia (11)(12)(13).…”

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confidence: 99%

An Automated Grading System for Detection of Vision-Threatening Referable Diabetic Retinopathy on the Basis of Color Fundus Photographs

Keel

Liu³

et al. 2018

Diabetes Care

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202

139

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OBJECTIVEThe goal of this study was to describe the development and validation of an artificial intelligence-based, deep learning algorithm (DLA) for the detection of referable diabetic retinopathy (DR). RESEARCH DESIGN AND METHODSA DLA using a convolutional neural network was developed for automated detection of vision-threatening referable DR (preproliferative DR or worse, diabetic macular edema, or both). The DLA was tested by using a set of 106,244 nonstereoscopic retinal images. A panel of ophthalmologists graded DR severity in retinal photographs included in the development and internal validation data sets (n = 71,043); a reference standard grading was assigned once three graders achieved consistent grading outcomes. For external validation, we tested our DLA using 35,201 images of 14,520 eyes (904 eyes with any DR; 401 eyes with vision-threatening referable DR) from population-based cohorts of Malays, Caucasian Australians, and Indigenous Australians. RESULTSAmong the 71,043 retinal images in the training and validation data sets, 12,329 showed vision-threatening referable DR. In the internal validation data set, the area under the curve (AUC), sensitivity, and specificity of the DLA for vision-threatening referable DR were 0.989, 97.0%, and 91.4%, respectively. Testing against the independent, multiethnic data set achieved an AUC, sensitivity, and specificity of 0.955, 92.5%, and 98.5%, respectively. Among false-positive cases, 85.6% were due to a misclassification of mild or moderate DR. Undetected intraretinal microvascular abnormalities accounted for 77.3% of all false-negative cases. CONCLUSIONSThis artificial intelligence-based DLA can be used with high accuracy in the detection of vision-threatening referable DR in retinal images. This technology offers potential to increase the efficiency and accessibility of DR screening programs.

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“…Retinal telescreening with remote interpretation by an expert for evaluation of DR may be useful in helping rural and medically underserved patients [6]. However, some diabetic patients cannot afford the cost of an ophthalmologist visit [7]. In addition, the assessment of DR severity needs specialized expertise, and the agreement of interpretation results may vary from the graders [8].…”

Section: Introductionmentioning

confidence: 99%

Detection of Diabetic Retinopathy Using Bichannel Convolutional Neural Network

Pao

Lin

Chien

et al. 2020

Journal of Ophthalmology

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Deep learning of fundus photograph has emerged as a practical and cost-effective technique for automatic screening and diagnosis of severer diabetic retinopathy (DR). The entropy image of luminance of fundus photograph has been demonstrated to increase the detection performance for referable DR using a convolutional neural network- (CNN-) based system. In this paper, the entropy image computed by using the green component of fundus photograph is proposed. In addition, image enhancement by unsharp masking (UM) is utilized for preprocessing before calculating the entropy images. The bichannel CNN incorporating the features of both the entropy images of the gray level and the green component preprocessed by UM is also proposed to improve the detection performance of referable DR by deep learning.

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Availability and variability in guidelines on diabetic retinopathy screening in Asian countries

Abstract: Countries in Asia need to establish more comprehensive and evidence-based DR screening guidelines to facilitate the execution of robust screening programmes that could help reduce DR-related blindness, improve patient outcomes and reduce healthcare costs.

Cited by 68 publications

References 70 publications

Clinical and Biochemical Associations with Diabetic Retinopathy in Male Patients in the Gaza Strip

Clinical and Biochemical Associations with Diabetic Retinopathy in Male Patients in the Gaza Strip

An Automated Grading System for Detection of Vision-Threatening Referable Diabetic Retinopathy on the Basis of Color Fundus Photographs

Detection of Diabetic Retinopathy Using Bichannel Convolutional Neural Network

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