AIMC Topic: Fundus Oculi
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Predicting Glaucoma Development With Longitudinal Deep Learning Predictions From Fundus Photographs.
American journal of ophthalmology
Jan 7, 2021
PURPOSE: To assess whether longitudinal changes in a deep learning algorithm's predictions of retinal nerve fiber layer (RNFL) thickness based on fundus photographs can predict future development of glaucomatous visual field defects.
Systematic Comparison of Heatmapping Techniques in Deep Learning in the Context of Diabetic Retinopathy Lesion Detection.
Translational vision science & technology
Dec 29, 2020
PURPOSE: Heatmapping techniques can support explainability of deep learning (DL) predictions in medical image analysis. However, individual techniques have been mainly applied in a descriptive way without an objective and systematic evaluation. We in...
Deep learning-based classification of retinal atrophy using fundus autofluorescence imaging.
Computers in biology and medicine
Dec 28, 2020
PURPOSE: To automatically classify retinal atrophy according to its etiology, using fundus autofluorescence (FAF) images, using a deep learning model.
Pathological myopia classification with simultaneous lesion segmentation using deep learning.
Computer methods and programs in biomedicine
Dec 28, 2020
BACKGROUND AND OBJECTIVES: Pathological myopia (PM) is the seventh leading cause of blindness, with a reported global prevalence up to 3%. Early and automated PM detection from fundus images could aid to prevent blindness in a world population that i...
Longitudinal Screening for Diabetic Retinopathy in a Nationwide Screening Program: Comparing Deep Learning and Human Graders.
Journal of diabetes research
Dec 15, 2020
OBJECTIVE: To evaluate diabetic retinopathy (DR) screening via deep learning (DL) and trained human graders (HG) in a longitudinal cohort, as case spectrum shifts based on treatment referral and new-onset DR.
Predictive Value of Tests
Fundus Oculi
Macular Edema
Cell Proliferation
Mass Screening
Longitudinal Studies
Image Interpretation, Computer-Assisted
Photography
National Health Programs
Middle Aged
Severity of Illness Index
Reproducibility of Results
Humans
Prevalence
Deep Learning
Aged
Diabetic Retinopathy
Thailand
Incidence
Male
Female
Development of a deep learning-based image eligibility verification system for detecting and filtering out ineligible fundus images: A multicentre study.
International journal of medical informatics
Dec 13, 2020
BACKGROUND: Recent advances in artificial intelligence (AI) have shown great promise in detecting some diseases based on medical images. Most studies developed AI diagnostic systems only using eligible images. However, in real-world settings, ineligi...
A novel deep learning conditional generative adversarial network for producing angiography images from retinal fundus photographs.
Scientific reports
Dec 9, 2020
Fluorescein angiography (FA) is a procedure used to image the vascular structure of the retina and requires the insertion of an exogenous dye with potential adverse side effects. Currently, there is only one alternative non-invasive system based on O...
How to Extract More Information With Less Burden: Fundus Image Classification and Retinal Disease Localization With Ophthalmologist Intervention.
IEEE journal of biomedical and health informatics
Dec 4, 2020
Image classification using convolutional neural networks (CNNs) outperforms other state-of-the-art methods. Moreover, attention can be visualized as a heatmap to improve the explainability of results of a CNN. We designed a framework that can generat...
Hard Attention Net for Automatic Retinal Vessel Segmentation.
IEEE journal of biomedical and health informatics
Dec 4, 2020
Automated retinal vessel segmentation is among the most significant application and research topics in ophthalmologic image analysis. Deep learning based retinal vessel segmentation models have attracted much attention in the recent years. However, c...
Identifying Mouse Autoimmune Uveitis from Fundus Photographs Using Deep Learning.
Translational vision science & technology
Dec 2, 2020
PURPOSE: To develop a deep learning model for objective evaluation of experimental autoimmune uveitis (EAU), the animal model of posterior uveitis that reveals its essential pathological features via fundus photographs.
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