Latest AI and machine learning research in ophthalmology for healthcare professionals.
PURPOSE: This article is a scoping review of published and peer-reviewed articles using deep-learning (DL) applied to ultra-widefield (UWF) imaging. This study provides an overview of the published uses of DL and UWF imaging for the detection of ophthalmic and systemic diseases, generative image synthesis, quality assessment of images, and segmentation and localization of ophthalmic image features...
Glaucoma is an eye condition that leads to loss of vision and blindness if not diagnosed in time. Diagnosis requires human experts to estimate in a limited time subtle changes in the shape of the optic disc from retinal fundus images. Deep learning methods have been satisfactory in classifying and segmenting diseases in retinal fundus images, assisting in analyzing the increasing amount of images....
Automatic and accurate optical coherence tomography (OCT) image classification is of great significance to computer-assisted diagnosis of retinal dise...
Zero-shot learning (ZSL) is a pretty intriguing topic in the computer vision community since it handles novel instances and unseen categories. In a ty...
This article examines the importance of integrating locomotion and cognitive information for achieving dynamic locomotion from a viewpoint combining b...
Despite the tremendous success in computer vision, deep convolutional networks suffer from serious computation costs and redundancies. Although previo...
Visual relationship detection (VRD) is one newly developed computer vision task, aiming to recognize relations or interactions between objects in an i...
Data augmentation is an established technique in computer vision to foster the generalization of training and to deal with low data volume. Most data ...
PURPOSE OF REVIEW: This review highlights the artificial intelligence, machine learning, and deep learning initiatives supported by the National Insti...
Retinal images acquired using fundus cameras are often visually blurred due to imperfect imaging conditions, refractive medium turbidity, and motion b...
Limited availability of medical imaging datasets is a vital limitation when using "data hungry" deep learning to gain performance improvements. Dealin...
The work intends to optimize the situation that interactive art devices and remote control based on traditional technology cannot meet people's actual...
Recently, the novel coronavirus disease 2019 (COVID-19) has posed many challenges to the research community by presenting grievous severe acute respir...
PURPOSE: Ophthalmic surgery involves the manipulation of micron-level sized structures such as the internal limiting membrane where tactile sensation ...
BACKGROUND/OBJECTIVES: We aim to develop an objective fully automated Artificial intelligence (AI) algorithm for MNV lesion size and leakage area segm...
The retinal vasculature provides important clues in the diagnosis and monitoring of systemic diseases including hypertension and diabetes. The microva...
When deep neural network (DNN) was first introduced to the medical image analysis community, researchers were impressed by its performance. However, i...
Vision processing for control of agile autonomous robots requires low-latency computation, within a limited power and space budget. This is challengin...
Dry eye disease (DED) is a common eye condition worldwide and a primary reason for visits to the ophthalmologist. DED diagnosis is performed through a...
With the widespread dissemination of robotic surgical platforms, pathologies that were previously deemed challenging can now be treated more reliably ...