AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Retinal Diseases

Showing 91 to 100 of 150 articles

Clear Filters

Generating retinal flow maps from structural optical coherence tomography with artificial intelligence.

Scientific reports
Despite advances in artificial intelligence (AI), its application in medical imaging has been burdened and limited by expert-generated labels. We used images from optical coherence tomography angiography (OCTA), a relatively new imaging modality that...

A Novel Weakly Supervised Multitask Architecture for Retinal Lesions Segmentation on Fundus Images.

IEEE transactions on medical imaging
Obtaining the complete segmentation map of retinal lesions is the first step toward an automated diagnosis tool for retinopathy that is interpretable in its decision-making. However, the limited availability of ground truth lesion detection maps at a...

Attention to Lesion: Lesion-Aware Convolutional Neural Network for Retinal Optical Coherence Tomography Image Classification.

IEEE transactions on medical imaging
Automatic and accurate classification of retinal optical coherence tomography (OCT) images is essential to assist ophthalmologist in the diagnosis and grading of macular diseases. Clinically, ophthalmologists usually diagnose macular diseases accordi...

Fully automated detection of retinal disorders by image-based deep learning.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
PURPOSE: With the aging population and the global diabetes epidemic, the prevalence of age-related macular degeneration (AMD) and diabetic macular edema (DME) diseases which are the leading causes of blindness is further increasing. Intravitreal inje...

Human/robotic interaction: vision limits performance in simulated vitreoretinal surgery.

Acta ophthalmologica
PURPOSE: Compare accuracy and precision in XYZ of stationary and dynamic tasks performed by surgeons with and without the use of a tele-operated robotic micromanipulator in a simulated vitreoretinal environment. The tasks were performed using a surgi...

Application of a deep convolutional neural network in the diagnosis of neonatal ocular fundus hemorrhage.

Bioscience reports
There is a disparity between the increasing application of digital retinal imaging to neonatal ocular screening and slowly growing number of pediatric ophthalmologists. Assistant tools that can automatically detect ocular disorders may be needed. In ...

Weakly Supervised Lesion Detection From Fundus Images.

IEEE transactions on medical imaging
Early diagnosis and continuous monitoring of patients suffering from eye diseases have been major concerns in the computer-aided detection techniques. Detecting one or several specific types of retinal lesions has made a significant breakthrough in c...

A novel retinal vessel detection approach based on multiple deep convolution neural networks.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Computer aided detection (CAD) offers an efficient way to assist doctors to interpret fundus images. In a CAD system, retinal vessel (RV) detection is a crucial step to identify the retinal disease regions. However, RV detec...

Multilayered Deep Structure Tensor Delaunay Triangulation and Morphing Based Automated Diagnosis and 3D Presentation of Human Macula.

Journal of medical systems
Maculopathy is the group of diseases that affects central vision of a person and they are often associated with diabetes. Many researchers reported automated diagnosis of maculopathy from optical coherence tomography (OCT) images. However, to the bes...