AIMC Topic: Refraction, Ocular
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Interpretable machine learning models for predicting childhood myopia from school-based screening data.
Scientific reports
Jun 5, 2025
This study assessed the efficacy of various diagnostic indicators and machine learning (ML) models in predicting childhood myopia. A total of 2,365 children aged 5-12 years were included in the study. The participants were exposed to non-cycloplegic ...
Investigation of Short-term Crystalline Lens Positional Shifts Following ICL Implantation Using SS-OCT Integrated With Multi-task Deep Learning.
Journal of refractive surgery (Thorofare, N.J. : 1995)
Jun 1, 2025
PURPOSE: To investigate the tilt and decentration of the crystalline lens shortly after Implantable Collamer Lens (ICL) implantation.
Adopting machine learning to predict nomogram for small incision lenticule extraction (SMILE).
International ophthalmology
May 5, 2025
PURPOSE: To predict nomogram for small incision lenticule extraction (SMILE) using machine learning technology and preoperative clinical data.
Intraocular lens calculation formula developed using artificial intelligence for ultrasonic biometry.
Arquivos brasileiros de oftalmologia
Apr 28, 2025
PURPOSE: We developed an artificial intelligence program for calculating intraocular lenses and analyzed its accuracy rate via ultrasonic biometry. This endeavor is aimed at enhancing precision and efficacy in the selection of intraocular lenses, par...
Network meta-analysis of intraocular lens power calculation formulas based on artificial intelligence in short eyes.
BMC ophthalmology
Apr 18, 2025
PURPOSE: To systematically assess and compare the accuracy of artificial intelligence (AI) -based intraocular lens (IOL) power calculation formulas with traditional IOL formulas in patients with short eye length.
Artificial intelligence in the diagnosis and management of refractive errors.
European journal of ophthalmology
Apr 13, 2025
Refractive error is among the leading causes of visual impairment globally. The diagnosis and management of refractive error has traditionally relied on comprehensive eye examinations by eye care professionals, but access to these specialized service...
Machine-learning random forest algorithms predict post-cycloplegic myopic corrections from noncycloplegic clinical data.
Optometry and vision science : official publication of the American Academy of Optometry
Feb 24, 2025
SIGNIFICANCE: Machine learning random forest algorithms were used to predict objective refractive outcomes after cycloplegic refraction using noncycloplegic clinical data. A classification model predicted post-cycloplegic myopia and could be useful i...
Analyzing the Effect of Surgical and Corneal Parameters on the Postoperative Refractive Outcomes of Smile in Myopic Eyes Based on Machine Learning.
American journal of ophthalmology
Dec 27, 2024
PURPOSE: To analyze the influence of individual parameters on the postoperative refractive outcomes of small incision lenticule extraction (SMILE) in myopic eyes using machine learning.
Ocular biometric parameters in Chinese preschool children and physiological axial length growth prediction using machine learning algorithms: a retrospective cross-sectional study.
BMJ open
Dec 20, 2024
OBJECTIVES: To examine the ocular biometric parameters and predict the annual growth rate of the physiological axial length (AL) in Chinese preschool children aged 4-6 years old.
Evaluation of prediction errors in nine intraocular lens calculation formulas using an explainable machine learning model.
BMC ophthalmology
Dec 19, 2024
BACKGROUND: The purpose of the study was to evaluate the relationship between prediction errors (PEs) and ocular biometric variables in cataract surgery using nine intraocular lens (IOL) formulas with an explainable machine learning model.
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