AIMC Topic: Corneal Topography
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Using 3D Convolutional Neural Network and Corvis ST Corneal Dynamic Video for Detecting Forme Fruste Keratoconus.
Journal of refractive surgery (Thorofare, N.J. : 1995)
Apr 1, 2025
PURPOSE: To evaluate the performance of a three-dimensional convolutional neural network (3D CNN) in detecting forme fruste keratoconus (FFKC).
Advances in machine learning for keratoconus diagnosis.
International ophthalmology
Mar 30, 2025
PURPOSE: To review studies reporting the role of Machine Learning (ML) techniques in the diagnosis of keratoconus (KC) over the past decade, shedding light on recent developments while also highlighting the existing gaps between academic research and...
Hybrid data augmentation strategies for robust deep learning classification of corneal topographic maptopographic map.
Biomedical physics & engineering express
Jan 30, 2025
Deep learning has emerged as a powerful tool in medical imaging, particularly for corneal topographic map classification. However, the scarcity of labeled data poses a significant challenge to achieving robust performance. This study investigates the...
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.
Artificial intelligence-assisted fitting method using corneal topography outcomes enhances success rate in orthokeratology lens fitting.
Contact lens & anterior eye : the journal of the British Contact Lens Association
Dec 16, 2024
PURPOSE: Based on ideal outcomes of corneal topography following orthokeratology (OK), an innovative machine learning algorithm for corneal refractive therapy (CRT) was developed to investigate the precision of artificial intelligence (AI)-assisted O...
Validation of an Artificial Intelligence-based Tool - The Screening Corneal Objective Risk of Ectasia Integrated into Anterion for Detection of Corneal Ectasia/Risk of Ectasia.
Middle East African journal of ophthalmology
Dec 2, 2024
PURPOSE: The purpose of this study was to validate the artificial intelligence-based Screening Corneal Objective Risk of Ectasia (SCORE) for the detection of corneal ectasia/risk of ectasia and to find the mean SCORE value in normal eyes.
Thickness Speed Progression Index: Machine Learning Approach for Keratoconus Detection.
American journal of ophthalmology
Nov 27, 2024
PURPOSE: To develop and validate a pachymetry-based machine learning (ML) index for differentiating keratoconus, keratoconus suspect, and normal corneas.
Artificial intelligence versus conventional methods for RGP lens fitting in keratoconus.
Contact lens & anterior eye : the journal of the British Contact Lens Association
Nov 4, 2024
BACKGROUND: To compare the efficiency of three artificial intelligence (AI) frameworks (Standard Machine Learning (ML), Multi-Layer Perceptron (MLP) and Convolution Neural Networks (CNN)) with a reference method (Mean radius of curvature (K)) to pred...
Machine Learning-Based Identification of Risk Factors of Keratoconus Progression Using Raw Corneal Tomography Data.
Cornea
Aug 20, 2024
PURPOSE: The purpose of this study was to identify early indicators of keratoconus progression in Pentacam data using machine learning (ML) techniques.
Development and evaluation of a deep neural network model for orthokeratology lens fitting.
Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists)
Jul 9, 2024
PURPOSE: To optimise the precision and efficacy of orthokeratology, this investigation evaluated a deep neural network (DNN) model for lens fitting. The objective was to refine the standardisation of fitting procedures and curtail subjective evaluati...
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