AIMC Topic: Refraction, Ocular
Clear Filters Showing 41 to 48 of 48 articles
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.
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.
Design and Evaluation of a Puncture Control Method for the Deflection of Ultrafine Needles due to Interfacial Passage-Proposal of Puncture Control Strategy for Increasing.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2023
With the demand for sophisticated techniques to easily prevent the deflection of needles, robotic CT (computed tomography)-guided puncture with an ultrafine needle is being investigated. Quantification of deflection is essential for accurate puncture...
Improvement of Multiple Generations of Intraocular Lens Calculation Formulae with a Novel Approach Using Artificial Intelligence.
Translational vision science & technology
Mar 1, 2021
PURPOSE: Cataract surgery is the most common eye surgery. Appropriate optimization of intraocular lens (IOL) calculation formulae can result in improved patient outcomes. The purpose of this article is to describe a methodology of optimizing existing...
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).
Deep Learning for Predicting Refractive Error From Retinal Fundus Images.
Investigative ophthalmology & visual science
Jun 1, 2018
PURPOSE: We evaluate how deep learning can be applied to extract novel information such as refractive error from retinal fundus imaging.
Predicting Refractive Surgery Outcome: Machine Learning Approach With Big Data.
Journal of refractive surgery (Thorofare, N.J. : 1995)
Sep 1, 2017
PURPOSE: To develop a decision forest for prediction of laser refractive surgery outcome.
Aspherical lens design using hybrid neural-genetic algorithm of contact lenses.
Applied optics
Oct 1, 2015
The design of complex contact lenses involves numerous uncertain variables. How to help an optical designer to first design the optimal contact lens to reduce discomfort when wearing a pair of glasses is an essential design concern. This study examin...
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