PURPOSE: To investigate the association between retinal microstructure and cone and rod function in geographic atrophy (GA) secondary to age-related macular degeneration (AMD) by using artificial intelligence (AI) algorithms.
Convolutional Neural Networks (CNNs) have become a prominent method of AI implementation in medical classification tasks. Grading Diabetic Retinopathy (DR) has been at the forefront of the development of AI for ophthalmology. However, major obstacles...
PURPOSE: The prediction of atherosclerosis using retinal fundus images and deep learning has not been shown possible. The purpose of this study was to develop a deep learning model which predicted atherosclerosis by using retinal fundus images and to...
Optic disc (OD) and optic cup (OC) segmentation are important steps for automatic screening and diagnosing of optic nerve head abnormalities such as glaucoma. Many recent works formulated the OD and OC segmentation as a pixel classification task. How...
Computer vision has greatly advanced recently. Since AlexNet was first introduced, many modified deep learning architectures have been developed and they are still evolving. However, there are few studies comparing these architectures in the field of...
This paper proposes a new supervised method for blood vessel segmentation using Zernike moment-based shape descriptors. The method implements a pixel wise classification by computing a 11-D feature vector comprising of both statistical (gray-level) f...
We developed a hybrid deep learning model (HDLM) algorithm that quantitatively predicts macular ganglion cell-inner plexiform layer (mGCIPL) thickness from red-free retinal nerve fiber layer photographs (RNFLPs). A total of 789 pairs of RNFLPs and sp...
PURPOSE: To develop and validate a deep learning model for the automatic segmentation of geographic atrophy (GA) using color fundus images (CFIs) and its application to study the growth rate of GA.
Objective and quantitative assessment of fundus image quality is essential for the diagnosis of retinal diseases. The major factors in fundus image quality assessment are image artifact, clarity, and field definition. Unfortunately, most of existing ...
Translational vision science & technology
Jan 30, 2020
PURPOSE: Artificial intelligence (AI) can identify the sex of an individual from color fundus photographs (CFPs). However, the mechanism(s) involved in this identification has not been determined. This study was conducted to determine the information...
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