Variabilidad inerobservador en el diagnóstico y clasificación de la retinopatía diabética mediante biomicroscopía

Molina-Fernández, E; Valero-Moll, M.S.; Pedregal-González, M; Dı́az-Rodrı́guez, Elena; Sánchez-Ramos, José Luis; Soriano-Villegas, J.M.

doi:10.4321/s0365-66912008000100006

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…Second, grading of DR made in the clinic by the examining physician are not always accurate. Inter-observer variability for grading DR varies from 0.62-0.87 in different studies [20,21]. This system provides a highly accurate and consistent diagnosis and grading of DR, a significant improvement over the human error and variability inherent in human diagnosis.…”

Section: Plos Onementioning

confidence: 98%

A convolutional neural network for the screening and staging of diabetic retinopathy

et al. 2020

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Diabetic retinopathy (DR) is a serious retinal disease and is considered as a leading cause of blindness in the world. Ophthalmologists use optical coherence tomography (OCT) and fundus photography for the purpose of assessing the retinal thickness, and structure, in addition to detecting edema, hemorrhage, and scars. Deep learning models are mainly used to analyze OCT or fundus images, extract unique features for each stage of DR and therefore classify images and stage the disease. Throughout this paper, a deep Convolutional Neural Network (CNN) with 18 convolutional layers and 3 fully connected layers is proposed to analyze fundus images and automatically distinguish between controls (i.e. no DR), moderate DR (i.e. a combination of mild and moderate Non Proliferative DR (NPDR)) and severe DR (i.e. a group of severe NPDR, and Proliferative DR (PDR)) with a validation accuracy of 88%-89%, a sensitivity of 87%-89%, a specificity of 94%-95%, and a Quadratic Weighted Kappa Score of 0.91-0.92 when both 5-fold, and 10-fold cross validation methods were used respectively. A prior pre-processing stage was deployed where image resizing and a class-specific data augmentation were used. The proposed approach is considerably accurate in objectively diagnosing and grading diabetic retinopathy, which obviates the need for a retina specialist and expands access to retinal care. This technology enables both early diagnosis and objective tracking of disease progression which may help optimize medical therapy to minimize vision loss.

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Section: Plos Onementioning

confidence: 98%