AIMC Topic: Keratoconus

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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).
Algorithms Female Young Adult Adult Humans Neural Networks, Computer Imaging, Three-Dimensional Keratoconus Cornea Corneal Topography Male Video Recording Convolutional Neural Networks Diagnostic Techniques, Ophthalmological ROC Curve
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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...
Keratoconus Corneal Topography Machine Learning Algorithms Cornea Humans
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Artificial Doctors: Performance of Chatbots as a Tool for Patient Education on Keratoconus.

Eye & contact lens Dec 31, 2024
PURPOSE: We aimed to compare the answers given by ChatGPT, Bard, and Copilot and that obtained from the American Academy of Ophthalmology (AAO) website to patient-written questions related to keratoconus in terms of accuracy, understandability, actio...
Male Surveys and Questionnaires Generative Artificial Intelligence Keratoconus Humans Female Adult Patient Education as Topic Internet Comprehension
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Automated feature selection for early keratoconus screening optimization.

Biomedical physics & engineering express Dec 20, 2024
In this paper, an automated feature selection (FS) method is presented to optimize machine learning (ML) models' performances, enhancing early keratoconus screening. A total of 448 parameters were analyzed from a dataset comprising 3162 observations ...
Humans Image Processing, Computer-Assisted Support Vector Machine Keratoconus Tomography, Optical Coherence Female Adult Automation Cornea Male Neural Networks, Computer Machine Learning Algorithms Electronic Health Records
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Evaluation of Responses to Questions About Keratoconus Using ChatGPT-4.0, Google Gemini and Microsoft Copilot: A Comparative Study of Large Language Models on Keratoconus.

Eye & contact lens Dec 4, 2024
OBJECTIVES: Large language models (LLMs) are increasingly being used today and are becoming increasingly important for providing accurate clinical information to patients and physicians. This study aimed to evaluate the effectiveness of generative pr...
Software Internet Language Large Language Models Female Reproducibility of Results Male Adult Surveys and Questionnaires Generative Artificial Intelligence Keratoconus Humans
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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.
Corneal Topography Sensitivity and Specificity Reproducibility of Results Dilatation, Pathologic Humans Risk Factors Cornea Young Adult Artificial Intelligence Male Adult Prospective Studies Adolescent Middle Aged Female Mass Screening Risk Assessment Keratoconus
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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.
Corneal Pachymetry Adolescent ROC Curve Keratoconus Corneal Topography Retrospective Studies Female Reproducibility of Results Young Adult Male Middle Aged Disease Progression Adult Cornea Humans Machine Learning Algorithms
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Machine learning methods to identify risk factors for corneal graft rejection in keratoconus.

Scientific reports Nov 25, 2024
Machine learning can be used to identify risk factors associated with graft rejection after corneal transplantation for keratoconus. The study included all keratoconus eyes that underwent primary corneal transplantation from 1994 to 2021. Data relati...
Keratoplasty, Penetrating Risk Factors Young Adult Adult Male Graft Rejection Keratoconus Corneal Transplantation Middle Aged Adolescent Humans Machine Learning Female
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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...
Retrospective Studies Female Young Adult Adult Humans Neural Networks, Computer Artificial Intelligence Keratoconus Corneal Topography Contact Lenses Prosthesis Fitting Cornea Male Middle Aged Adolescent
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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.
Retrospective Studies Female Risk Factors Young Adult Male Middle Aged Disease Progression Adult Cornea Humans Machine Learning Tomography Adolescent ROC Curve Keratoconus Corneal Topography
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