AIMC Topic: Fundus Oculi
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Leveraging Multimodal Deep Learning Architecture with Retina Lesion Information to Detect Diabetic Retinopathy.
Translational vision science & technology
Jul 16, 2020
PURPOSE: To improve disease severity classification from fundus images using a hybrid architecture with symptom awareness for diabetic retinopathy (DR).
Classification of pachychoroid disease on ultrawide-field indocyanine green angiography using auto-machine learning platform.
The British journal of ophthalmology
Jul 3, 2020
AIMS: Automatic identification of pachychoroid maybe used as an adjunctive method to confirm the condition and be of help in treatment for macular diseases. This study investigated the feasibility of classifying pachychoroid disease on ultra-widefiel...
Effect of color information on the diagnostic performance of glaucoma in deep learning using few fundus images.
International ophthalmology
Jun 27, 2020
PURPOSE: The purpose of this study was to evaluate the accuracy of the convolutional neural network (CNN) model in glaucoma identification with three primary colors (red, green, blue; RGB) and split color channels using fundus photographs with a smal...
A Retrospective Comparison of Deep Learning to Manual Annotations for Optic Disc and Optic Cup Segmentation in Fundus Photographs.
Translational vision science & technology
Jun 24, 2020
PURPOSE: Optic disc (OD) and optic cup (OC) segmentation are fundamental for fundus image analysis. Manual annotation is time consuming, expensive, and highly subjective, whereas an automated system is invaluable to the medical community. The aim of ...
Deep Learning-Based Detection of Pigment Signs for Analysis and Diagnosis of Retinitis Pigmentosa.
Sensors (Basel, Switzerland)
Jun 18, 2020
Ophthalmological analysis plays a vital role in the diagnosis of various eye diseases, such as glaucoma, retinitis pigmentosa (RP), and diabetic and hypertensive retinopathy. RP is a genetic retinal disorder that leads to progressive vision degenerat...
Automated diagnoses of age-related macular degeneration and polypoidal choroidal vasculopathy using bi-modal deep convolutional neural networks.
The British journal of ophthalmology
Jun 4, 2020
AIMS: To investigate the efficacy of a bi-modality deep convolutional neural network (DCNN) framework to categorise age-related macular degeneration (AMD) and polypoidal choroidal vasculopathy (PCV) from colour fundus images and optical coherence tom...
WGAN domain adaptation for the joint optic disc-and-cup segmentation in fundus images.
International journal of computer assisted radiology and surgery
May 22, 2020
PURPOSE: The cup-to-disc ratio (CDR), a clinical metric of the relative size of the optic cup to the optic disc, is a key indicator of glaucoma, a chronic eye disease leading to loss of vision. CDR can be measured from fundus images through the segme...
Prediction of hypertension, hyperglycemia and dyslipidemia from retinal fundus photographs via deep learning: A cross-sectional study of chronic diseases in central China.
PloS one
May 14, 2020
Retinal fundus photography provides a non-invasive approach for identifying early microcirculatory alterations of chronic diseases prior to the onset of overt clinical complications. Here, we developed neural network models to predict hypertension, h...
A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations.
The Lancet. Digital health
May 12, 2020
BACKGROUND: Screening for chronic kidney disease is a challenge in community and primary care settings, even in high-income countries. We developed an artificial intelligence deep learning algorithm (DLA) to detect chronic kidney disease from retinal...
CycleGAN-based deep learning technique for artifact reduction in fundus photography.
Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
May 2, 2020
PURPOSE: A low quality of fundus photograph with artifacts may lead to false diagnosis. Recently, a cycle-consistent generative adversarial network (CycleGAN) has been introduced as a tool to generate images without matching paired images. Therefore,...
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