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Optic Disk

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Estimating visual field loss from monoscopic optic disc photography using deep learning model.

Scientific reports
Visual field assessment is recognized as the important criterion of glaucomatous damage judgement; however, it can show large test-retest variability. We developed a deep learning (DL) algorithm that quantitatively predicts mean deviation (MD) of sta...

Computer-aided recognition of myopic tilted optic disc using deep learning algorithms in fundus photography.

BMC ophthalmology
BACKGROUND: It is necessary to consider myopic optic disc tilt as it seriously impacts normal ocular parameters. However, ophthalmologic measurements are within inter-observer variability and time-consuming to get. This study aimed to develop and eva...

Deep-learning-based enhanced optic-disc photography.

PloS one
Optic-disc photography (ODP) has proven to be very useful for optic nerve evaluation in glaucoma. In real clinical practice, however, limited patient cooperation, small pupils, or media opacities can limit the performance of ODP. The purpose of this ...

Deep learning for automated glaucomatous optic neuropathy detection from ultra-widefield fundus images.

The British journal of ophthalmology
BACKGROUND/AIMS: To develop a deep learning system for automated glaucomatous optic neuropathy (GON) detection using ultra-widefield fundus (UWF) images.

Optic Disc Classification by Deep Learning versus Expert Neuro-Ophthalmologists.

Annals of neurology
OBJECTIVE: To compare the diagnostic performance of an artificial intelligence deep learning system with that of expert neuro-ophthalmologists in classifying optic disc appearance.

Explaining the Rationale of Deep Learning Glaucoma Decisions with Adversarial Examples.

Ophthalmology
PURPOSE: To illustrate what is inside the so-called black box of deep learning models (DLMs) so that clinicians can have greater confidence in the conclusions of artificial intelligence by evaluating adversarial explanation on its ability to explain ...

A Retrospective Comparison of Deep Learning to Manual Annotations for Optic Disc and Optic Cup Segmentation in Fundus Photographs.

Translational vision science & technology
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 ...

WGAN domain adaptation for the joint optic disc-and-cup segmentation in fundus images.

International journal of computer assisted radiology and surgery
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...

DeshadowGAN: A Deep Learning Approach to Remove Shadows from Optical Coherence Tomography Images.

Translational vision science & technology
PURPOSE: To remove blood vessel shadows from optical coherence tomography (OCT) images of the optic nerve head (ONH).

Efficacy for Differentiating Nonglaucomatous Versus Glaucomatous Optic Neuropathy Using Deep Learning Systems.

American journal of ophthalmology
PURPOSE: We sought to assess the performance of deep learning approaches for differentiating nonglaucomatous optic neuropathy with disc pallor (NGON) vs glaucomatous optic neuropathy (GON) on color fundus photographs by the use of image recognition.