AIMC Topic: Keratoconus

Clear Filters Showing 1 to 10 of 50 articles

An optimized multi-scale dilated attention layer for keratoconus disease classification.

International ophthalmology Jul 30, 2025
INTRODUCTION: Keratoconus (KCN) is a progressive and non-inflammatory corneal disorder characterized by thinning and conical deformation of the cornea, resulting in visual impairment. Early and accurate detection is crucial to prevent disease progres...
Cornea Deep Learning Keratoconus Corneal Topography Humans Algorithms
View on PubMed DOI

Machine learning-assisted early detection of keratoconus: a comparative analysis of corneal topography and biomechanical data.

Scientific reports Jul 8, 2025
Keratoconus is a progressive eye disease characterized by the thinning and bulging of the cornea, leading to visual impairment. Early and accurate diagnosis is crucial for effective management and treatment. This study investigates the application of...
Cornea Male Adolescent Keratoconus Reproducibility of Results Young Adult Adult Biomechanical Phenomena Corneal Topography Early Diagnosis Humans Machine Learning Algorithms Female
View on PubMed DOI

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).
Humans Neural Networks, Computer Algorithms Keratoconus Corneal Topography Video Recording Diagnostic Techniques, Ophthalmological Male Female Convolutional Neural Networks Young Adult ROC Curve Adult Imaging, Three-Dimensional Cornea
View on PubMed DOI

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...
Humans Corneal Topography Machine Learning Algorithms Cornea Keratoconus
View on PubMed DOI

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...
Female Adult Male Surveys and Questionnaires Comprehension Humans Generative Artificial Intelligence Keratoconus Patient Education as Topic Internet
View on PubMed DOI

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 ...
Keratoconus Tomography, Optical Coherence Automation Cornea Male Image Processing, Computer-Assisted Support Vector Machine Humans Neural Networks, Computer Machine Learning Algorithms Electronic Health Records Female Adult
View on PubMed DOI

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...
Internet Large Language Models Male Surveys and Questionnaires Generative Artificial Intelligence Keratoconus Female Reproducibility of Results Adult Humans Software Language
View on PubMed DOI

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.
Adult Mass Screening Female Sensitivity and Specificity Reproducibility of Results Male Keratoconus Humans Corneal Topography Risk Factors Middle Aged Young Adult Risk Assessment Adolescent Artificial Intelligence Prospective Studies Dilatation, Pathologic Cornea
View on PubMed DOI

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.
Humans Machine Learning Algorithms Retrospective Studies Female Reproducibility of Results Middle Aged Keratoconus Disease Progression Corneal Topography Adolescent Young Adult Corneal Pachymetry ROC Curve Adult Cornea Male
View on PubMed DOI

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 Adolescent Graft Rejection Keratoconus Corneal Transplantation Adult Male Middle Aged Machine Learning Female Risk Factors Humans Young Adult
View on PubMed DOI
Popular Topics
Recent Journals