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[IEEE 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT) - Coimbatore, India (2018.3.1-2018.3.3)] 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT) - Processing Retinal Images to Discover Diseases
摘要： The retina of a human eye consists of billion of photosensitive cells (rods and cones) and alternative nerve cells that acquire and arrange visual information. The retina of a human eye is a thin tissue layer on the inside back wall of your eye. Three of the are Diabetic retinal diseases most Retinopathy, Glaucoma, and Cataract. The world is presently experiencing an epidemic of Diabetic Retinopathy (DR). Current predictions draw an estimation of doubling of the number affected from the current 170 million to an estimated 367 million by 2030. We propose a system wherein we extract blood vessels of the retina to detect eye diseases. Manually extracting the blood vessels of the human retina is a time-consuming task, and thus an automation of this process results in easy implementation of the work. This paper aims to design and consequently implement deep convolutional neural networks to identify the presence of an exudate, and thereby classify it into Diabetic Retinopathy, Glaucoma, and/or Cataract.
关键词： Computer vision,Glaucoma,Diabetic Retinopathy,Cataract,Convolutional Neural Networks,Retinal disease detection,CNN
更新于2025-09-04 15:30:14
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Visualizing Deep Learning Models for the Detection of Referable Diabetic Retinopathy and Glaucoma
摘要： IMPORTANCE Convolutional neural networks have recently been applied to ophthalmic diseases; however, the rationale for the outputs generated by these systems is inscrutable to clinicians. A visualization tool is needed that would enable clinicians to understand important exposure variables in real time. OBJECTIVE To systematically visualize the convolutional neural networks of 2 validated deep learning models for the detection of referable diabetic retinopathy (DR) and glaucomatous optic neuropathy (GON). DESIGN, SETTING, AND PARTICIPANTS The GON and referable DR algorithms were previously developed and validated (holdout method) using 48 116 and 66 790 retinal photographs, respectively, derived from a third-party database (LabelMe) of deidentified photographs from various clinical settings in China. In the present cross-sectional study, a random sample of 100 true-positive photographs and all false-positive cases from each of the GON and DR validation data sets were selected. All data were collected from March to June 2017. The original color fundus images were processed using an adaptive kernel visualization technique. The images were preprocessed by applying a sliding window with a size of 28 × 28 pixels and a stride of 3 pixels to crop images into smaller subimages to produce a feature map. Threshold scales were adjusted to optimal levels for each model to generate heat maps highlighting localized landmarks on the input image. A single optometrist allocated each image to predefined categories based on the generated heat map. MAIN OUTCOMES AND MEASURES Visualization regions of the fundus. RESULTS In the GON data set, 90 of 100 true-positive cases (90%; 95% CI, 82%-95%) and 15 of 22 false-positive cases (68%; 95% CI, 45%-86%) displayed heat map visualization within regions of the optic nerve head only. Lesions typically seen in cases of referable DR (exudate, hemorrhage, or vessel abnormality) were identified as the most important prognostic regions in 96 of 100 true-positive DR cases (96%; 95% CI, 90%-99%). In 39 of 46 false-positive DR cases (85%; 95% CI, 71%-94%), the heat map displayed visualization of nontraditional fundus regions with or without retinal venules. CONCLUSIONS AND RELEVANCE These findings suggest that this visualization method can highlight traditional regions in disease diagnosis, substantiating the validity of the deep learning models investigated. This visualization technique may promote the clinical adoption of these models.
关键词： visualization,convolutional neural networks,deep learning,glaucoma,diabetic retinopathy
更新于2025-09-04 15:30:14
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Choroidal Thickness at the Outside of Fovea in Diabetic Retinopathy Using Spectral-Domain Optical Coherence Tomography
摘要： Purpose: To evaluate choroidal thickness at the outside of the fovea in patients with diabetic retinopathy using spectral-domain optical coherence tomography. Methods: We examined 87 eyes of 87 patients with diabetic retinopathy and 40 eyes of 40 normal patients. Patients with diabetic retinopathy were divided into 3 groups according to the grade of diabetic retinopathy and macular edema. The choroidal thickness was obtained at the fovea and outside of the fovea using enhanced depth imaging of Spectralis optical coherence tomography. One foveal and 8 peripheral images were selected and choroidal thickness was measured from the outer border of the retinal pigment epithelium to the inner scleral border. Results: Subfoveal choroidal thickness was thinner with increasing severity of diabetic retinopathy. However, there was no significant difference between groups without the nasal side of the fovea. A statistically significant difference was observed over the fovea at the superotemporal area. Conclusions: The choroidal thickness outside of the fovea was thinner with the severity of diabetic retinopathy and was more pronounced in the superotemporal area.
关键词： Optical coherence tomography,Diabetic retinopathy,Choroidal thickness,Enhanced depth imaging
更新于2025-09-04 15:30:14
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Multifocal Pupillography Identifies Changes in Visual Sensitivity According to Severity of Diabetic Retinopathy in Type 2 Diabetes
摘要： PURPOSE. Retinal light sensitivity loss has been shown to occur prior to other signs of retinopathy and may predict the sight-threatening sequelae. A rapid, objective perimetric test could augment diabetes care. We investigated the clinical use of multifocal pupillographic objective perimetry (mfPOP) to identify patients with and without diabetic retinopathy. METHODS. Retinopathy severity was determined using the Early Treatment of Diabetic Retinopathy Study (ETDRS) standard for fundus photography. Pupillary responses were measured from both eyes of 25 adults with none to moderate diabetic retinopathy and 24 age-matched controls, using three mfPOP stimulus variants. Multifocal pupillographic objective perimetry stimulus variants tested 44 regions per eye arranged in a ?ve-ring dartboard layout presented within either the central 308 or 608 of ?xation. Receiver operator characteristic (ROC) curves were produced from contraction amplitudes and time to peak responses. RESULTS. Regression analysis revealed that mean amplitude deviations were larger with severity of early retinopathy. On average, the longest delays were measured in patients with no retinopathy. The brightest wide-?eld stimuli produced the highest area under the ROC curve for differentiating eyes with no retinopathy from nonproliferative diabetic retinopathy (NPDR) and from healthy eyes (100 6 0.0%, mean 6 SE). The asymmetry in local delay deviations between eyes tended to produce higher sensitivity and speci?city than amplitude deviations. CONCLUSIONS. Asymmetry in local response delays measured by mfPOP may provide useful information regarding the severity of diabetic retinopathy and may have clinical use as a rapid, noninvasive method for identifying functional loss even in the absence of NPDR.
关键词： type 2 diabetes,pupils,objective perimetry,diabetic retinopathy,multifocal
更新于2025-09-04 15:30:14
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A comprehensive competitive endogenous RNA network pinpoints key molecules in diabetic retinopathy
摘要： Diabetic retinopathy (DR) is a severe microvascular complication of diabetes and the primary cause of vision loss in diabetic patients. Previous research has revealed that long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) play pivotal roles in the pathogenesis of DR. However, the roles of lncRNA-miRNA-mRNA interactions in DR are poorly understood. In the present study, we aimed to compute a global triple network of competitive endogenous RNAs (ceRNAs) in order to pinpoint essential molecules. We found that there were 802 nodes (121 lncRNA nodes, 17 miRNA nodes, and 664 mRNA nodes) and 949 edges in the ceRNA network. Further functional analysis suggested that some molecules were specifically related to DR. Surprisingly, these molecules were involved in visual perception, eye development, and lens development in camera-type eye. In summary, our study highlighted specific lncRNAs and miRNAs related to the pathogenesis of DR, which might be used as potential diagnostic biomarkers and therapeutic targets for DR.
关键词： lncRNA,diabetic retinopathy,ceRNA,miRNA
更新于2025-09-04 15:30:14