AIMC Topic: Optic Disk
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Assessing the Efficacy of Synthetic Optic Disc Images for Detecting Glaucomatous Optic Neuropathy Using Deep Learning.
Translational vision science & technology
Jun 3, 2024
PURPOSE: Deep learning architectures can automatically learn complex features and patterns associated with glaucomatous optic neuropathy (GON). However, developing robust algorithms requires a large number of data sets. We sought to train an adversar...
PallorMetrics: Software for Automatically Quantifying Optic Disc Pallor in Fundus Photographs, and Associations With Peripapillary RNFL Thickness.
Translational vision science & technology
May 1, 2024
PURPOSE: We sough to develop an automatic method of quantifying optic disc pallor in fundus photographs and determine associations with peripapillary retinal nerve fiber layer (pRNFL) thickness.
A novel lightweight deep learning approach for simultaneous optic cup and optic disc segmentation in glaucoma detection.
Mathematical biosciences and engineering : MBE
Mar 4, 2024
Glaucoma is a chronic neurodegenerative disease that can result in irreversible vision loss if not treated in its early stages. The cup-to-disc ratio is a key criterion for glaucoma screening and diagnosis, and it is determined by dividing the area o...
Multi-dimensional dense attention network for pixel-wise segmentation of optic disc in colour fundus images.
Technology and health care : official journal of the European Society for Engineering and Medicine
Jan 1, 2024
BACKGROUND: Segmentation of retinal fragments like blood vessels, Optic Disc (OD), and Optic Cup (OC) enables the early detection of different retinal pathologies like Diabetic Retinopathy (DR), Glaucoma, etc.
Validating the Generalizability of Ophthalmic Artificial Intelligence Models on Real-World Clinical Data.
Translational vision science & technology
Nov 1, 2023
PURPOSE: This study aims to investigate generalizability of deep learning (DL) models trained on commonly used public fundus images to an instance of real-world data (RWD) for glaucoma diagnosis.
3-LbNets: Tri-Labeling Deep Convolutional Neural Network for the Automated Screening of Glaucoma, Glaucoma Suspect, and No Glaucoma in Fundus Images.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2023
Early detection of glaucoma, a widespread visual disease, can prevent vision loss. Unfortunately, ophthalmologists are scarce and clinical diagnosis requires much time and cost. Therefore, we developed a screening Tri-Labeling deep convolutional neur...
A Deep Learning-Based Approach Towards Simultaneous Localization of Optic Disc and Fovea from Retinal Fundus Images.
Studies in health technology and informatics
Jun 29, 2023
In this work, we propose a multi-task learning-based approach towards the localization of optic disc and fovea from human retinal fundus images using a deep learning-based approach. Formulating the task as an image-based regression problem, we propos...
Through the eyes into the brain, using artificial intelligence.
Annals of the Academy of Medicine, Singapore
Feb 1, 2023
INTRODUCTION: Detection of neurological conditions is of high importance in the current context of increasingly ageing populations. Imaging of the retina and the optic nerve head represents a unique opportunity to detect brain diseases, but requires ...
Medical Application of Geometric Deep Learning for the Diagnosis of Glaucoma.
Translational vision science & technology
Feb 1, 2023
PURPOSE: (1) To assess the performance of geometric deep learning in diagnosing glaucoma from a single optical coherence tomography (OCT) scan of the optic nerve head and (2) to compare its performance to that obtained with a three-dimensional (3D) c...
Evaluation of Generative Adversarial Networks for High-Resolution Synthetic Image Generation of Circumpapillary Optical Coherence Tomography Images for Glaucoma.
JAMA ophthalmology
Oct 1, 2022
IMPORTANCE: Deep learning (DL) networks require large data sets for training, which can be challenging to collect clinically. Generative models could be used to generate large numbers of synthetic optical coherence tomography (OCT) images to train su...
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