AIMC Topic: Fundus Oculi

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Development of a deep-learning system for detection of lattice degeneration, retinal breaks, and retinal detachment in tessellated eyes using ultra-wide-field fundus images: a pilot study.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
PURPOSE: To investigate the detection of lattice degeneration, retinal breaks, and retinal detachment in tessellated eyes using ultra-wide-field fundus imaging system (Optos) with convolutional neural network technology.

Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation.

IEEE transactions on neural networks and learning systems
A common shortfall of supervised deep learning for medical imaging is the lack of labeled data, which is often expensive and time consuming to collect. This article presents a new semisupervised method for medical image segmentation, where the networ...

A combined convolutional and recurrent neural network for enhanced glaucoma detection.

Scientific reports
Glaucoma, a leading cause of blindness, is a multifaceted disease with several patho-physiological features manifesting in single fundus images (e.g., optic nerve cupping) as well as fundus videos (e.g., vascular pulsatility index). Current convoluti...

Applications of deep learning in fundus images: A review.

Medical image analysis
The use of fundus images for the early screening of eye diseases is of great clinical importance. Due to its powerful performance, deep learning is becoming more and more popular in related applications, such as lesion segmentation, biomarkers segmen...

An objective structural and functional reference standard in glaucoma.

Scientific reports
The current lack of consensus for diagnosing glaucoma makes it difficult to develop diagnostic tests derived from deep learning (DL) algorithms. In the present study, we propose an objective definition of glaucomatous optic neuropathy (GON) using cle...

Predicting Glaucoma Development With Longitudinal Deep Learning Predictions From Fundus Photographs.

American journal of ophthalmology
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
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
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
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...