AI Medical Compendium Topic

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

Refractive Errors

Showing 1 to 10 of 11 articles

Clear Filters

The accuracy of intraocular lens power calculation formulas based on artificial intelligence in highly myopic eyes: a systematic review and network meta-analysis.

Frontiers in public health
OBJECTIVE: To systematically compare and rank the accuracy of AI-based intraocular lens (IOL) power calculation formulas and traditional IOL formulas in highly myopic eyes.

Aspherical lens design using hybrid neural-genetic algorithm of contact lenses.

Applied optics
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...

Deep Learning for Predicting Refractive Error From Retinal Fundus Images.

Investigative ophthalmology & visual science
PURPOSE: We evaluate how deep learning can be applied to extract novel information such as refractive error from retinal fundus imaging.

Corneal Topography Raw Data Classification Using a Convolutional Neural Network.

American journal of ophthalmology
PURPOSE: We investigated the efficiency of a convolutional neural network applied to corneal topography raw data to classify examinations of 3 categories: normal, keratoconus (KC), and history of refractive surgery (RS).

Deep learning for predicting uncorrected refractive error using posterior segment optical coherence tomography images.

Eye (London, England)
BACKGROUND/OBJECTIVES: This study aimed to evaluate a deep learning model for estimating uncorrected refractive error using posterior segment optical coherence tomography (OCT) images.

Automatic Segment and Quantify Choroid Layer in Myopic eyes: Deep Learning based Model.

Seminars in ophthalmology
PURPOSE: To report a rapid and accurate method based upon deep learning for automatic segmentation and measurement of the choroidal thickness (CT) in myopic eyes, and to determine the relationship between refractive error (RE) and CT.

Deep learning for predicting refractive error from multiple photorefraction images.

Biomedical engineering online
BACKGROUND: Refractive error detection is a significant factor in preventing the development of myopia. To improve the efficiency and accuracy of refractive error detection, a refractive error detection network (REDNet) is proposed that combines the ...

Prescription eyeglasses as a forensic physical evidence: Prediction of age based on refractive error measures using machine learning algorithm.

Journal of forensic sciences
Refractive errors (RE) are commonly reported visual impairment problems worldwide. Previous clinical studies demonstrated age-related changes in human eyes. We hypothesized that the binocular RE metrics including sphere and cylinder power, axis orien...

Machine Learning Models for Predicting Cycloplegic Refractive Error and Myopia Status Based on Non-Cycloplegic Data in Chinese Students.

Translational vision science & technology
PURPOSE: To develop and validate machine learning (ML) models for predicting cycloplegic refractive error and myopia status using noncycloplegic refractive error and biometric data.

[Advancements in machine learning applications in refractive surgery].

[Zhonghua yan ke za zhi] Chinese journal of ophthalmology
Refractive error is a significant factor contributing to visual impairment, imposing a relatively large burden on the social economy. Although refractive surgery is an important corrective method, it faces challenges in clinical practice, such as pre...