AI Medical Compendium Journal:
Translational vision science & technology

Showing 101 to 110 of 208 articles

Assessing the Efficacy of Synthetic Optic Disc Images for Detecting Glaucomatous Optic Neuropathy Using Deep Learning.

Translational vision science & technology
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...

Convolutional Neural Network-Based Prediction of Axial Length Using Color Fundus Photography.

Translational vision science & technology
PURPOSE: To develop convolutional neural network (CNN)-based models for predicting the axial length (AL) using color fundus photography (CFP) and explore associated clinical and structural characteristics.

Ocular Biometric Components in Hyperopic Children and a Machine Learning-Based Model to Predict Axial Length.

Translational vision science & technology
PURPOSE: The purpose of this study was to investigate the development of optical biometric components in children with hyperopia, and apply a machine-learning model to predict axial length.

PallorMetrics: Software for Automatically Quantifying Optic Disc Pallor in Fundus Photographs, and Associations With Peripapillary RNFL Thickness.

Translational vision science & technology
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.

Prediction of Visual Outcome After Rhegmatogenous Retinal Detachment Surgery Using Artificial Intelligence Techniques.

Translational vision science & technology
PURPOSE: This study aimed to develop artificial intelligence models for predicting postoperative functional outcomes in patients with rhegmatogenous retinal detachment (RRD).

Keratoconus Progression Determined at the First Visit: A Deep Learning Approach With Fusion of Imaging and Numerical Clinical Data.

Translational vision science & technology
PURPOSE: Multiple clinical visits are necessary to determine progression of keratoconus before offering corneal cross-linking. The purpose of this study was to develop a neural network that can potentially predict progression during the initial visit...

Trends in the Prevalence of Common Retinal and Optic Nerve Diseases in China: An Artificial Intelligence Based National Screening.

Translational vision science & technology
PURPOSE: Retinal and optic nerve diseases have become the primary cause of irreversible vision loss and blindness. However, there is still a lack of thorough evaluation regarding their prevalence in China.

Artificial Intelligence in Cataract Surgery: A Systematic Review.

Translational vision science & technology
PURPOSE: The purpose of this study was to assess the current use and reliability of artificial intelligence (AI)-based algorithms for analyzing cataract surgery videos.

Retinal Fractal Dimension Is a Potential Biomarker for Systemic Health-Evidence From a Mixed-Age, Primary-Care Population.

Translational vision science & technology
PURPOSE: To investigate whether fractal dimension (FD), a retinal trait relating to vascular complexity and a potential "oculomics" biomarker for systemic disease, is applicable to a mixed-age, primary-care population.

Quantitative Assessment of Fundus Tessellated Density in Highly Myopic Glaucoma Using Deep Learning.

Translational vision science & technology
PURPOSE: To characterize the fundus tessellated density (FTD) in highly myopic glaucoma (HMG) and high myopia (HM) for discovering early signs and diagnostic markers.