AIMC Topic: Axial Length, Eye
Clear Filters Showing 11 to 18 of 18 articles
Accuracy of Artificial Intelligence Formulas and Axial Length Adjustments for Highly Myopic Eyes.
American journal of ophthalmology
Sep 18, 2020
PURPOSE: To compare the accuracy of artificial intelligence formulas (Kane formula and Radial Basis Function [RBF] 2.0) and other formulas, including the original and modified Wang-Koch (MWK) adjustment formulas for Holladay 1 (H1-MWK) and SRK/T (SRK...
Predicting the Glaucomatous Central 10-Degree Visual Field From Optical Coherence Tomography Using Deep Learning and Tensor Regression.
American journal of ophthalmology
May 6, 2020
PURPOSE: To predict the visual field (VF) of glaucoma patients within the central 10° from optical coherence tomography (OCT) measurements using deep learning and tensor regression.
Glaucoma, Open-Angle
Gonioscopy
Slit Lamp Microscopy
Axial Length, Eye
Visual Fields
Intraocular Pressure
Deep Learning
Aged
Visual Field Tests
Male
Visual Acuity
Adult
Optic Nerve Diseases
Middle Aged
Vision Disorders
Humans
Sensitivity and Specificity
Tomography, Optical Coherence
Cross-Sectional Studies
Retrospective Studies
Female
Prediction of Axial Length From Macular Optical Coherence Tomography Using Deep Learning Model.
Translational vision science & technology
Sep 3, 2024
PURPOSE: The purpose of this study was to develop a deep learning model for predicting the axial length (AL) of eyes using optical coherence tomography (OCT) images.
Convolutional Neural Network-Based Prediction of Axial Length Using Color Fundus Photography.
Translational vision science & technology
May 1, 2024
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
May 1, 2024
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.
[Preliminary study on automatic quantification and grading of leopard spots fundus based on deep learning technology].
[Zhonghua yan ke za zhi] Chinese journal of ophthalmology
Mar 11, 2024
To achieve automatic segmentation, quantification, and grading of different regions of leopard spots fundus (FT) using deep learning technology. The analysis includes exploring the correlation between novel quantitative indicators, leopard spot fund...
ASSOCIATION OF TESSELLATION DENSITY WITH PROGRESSION OF AXIAL LENGTH AND REFRACTION IN CHILDREN: An Artificial Intelligence-Assisted 4-Year Study.
Retina (Philadelphia, Pa.)
Mar 1, 2024
PURPOSE: To investigate fundus tessellation density (TD) and its association with axial length (AL) elongation and spherical equivalent (SE) progression in children.
Refractive Predictability Using the IOLMaster 700 and Artificial Intelligence-Based IOL Power Formulas Compared to Standard Formulas.
Journal of refractive surgery (Thorofare, N.J. : 1995)
Jul 1, 2020
PURPOSE: To investigate the accuracy of intraocular lens (IOL) power calculation formulas using swept-source optical coherence tomography (SS-OCT).
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