AI Medical Compendium Journal:
Cornea

Showing 1 to 10 of 20 articles

Computer Vision Identification of Trachomatous Inflammation-Follicular Using Deep Learning.

Cornea
PURPOSE: Trachoma surveys are used to estimate the prevalence of trachomatous inflammation-follicular (TF) to guide mass antibiotic distribution. These surveys currently rely on human graders, introducing a significant resource burden and potential f...

Applications of Artificial Intelligence in Diagnosis of Dry Eye Disease: A Systematic Review and Meta-Analysis.

Cornea
PURPOSE: Clinical diagnosis of dry eye disease is based on a subjective Ocular Surface Disease Index questionnaire or various objective tests, however, these diagnostic methods have several limitations.

Performance of ChatGPT in Diagnosis of Corneal Eye Diseases.

Cornea
PURPOSE: The aim of this study was to assess the capabilities of ChatGPT-4.0 and ChatGPT-3.5 for diagnosing corneal eye diseases based on case reports and compare with human experts.

Assessing Fuchs Corneal Endothelial Dystrophy Using Artificial Intelligence-Derived Morphometric Parameters From Specular Microscopy Images.

Cornea
PURPOSE: The aim of this study was to evaluate the efficacy of artificial intelligence-derived morphometric parameters in characterizing Fuchs corneal endothelial dystrophy (FECD) from specular microscopy images.

Deep Learning Models Used in the Diagnostic Workup of Keratoconus: A Systematic Review and Exploratory Meta-Analysis.

Cornea
PURPOSE: The prevalence of keratoconus in the general population is reported to be up to 1 of 84. Over the past 2 decades, diagnosis and management evolved rapidly, but keratoconus screening in clinical practice is still challenging and asks for impr...

Automatic Classification of Slit-Lamp Photographs by Imaging Illumination.

Cornea
PURPOSE: The aim of this study was to facilitate deep learning systems in image annotations for diagnosing keratitis type by developing an automated algorithm to classify slit-lamp photographs (SLPs) based on illumination technique.

Mobile-CellNet: Automatic Segmentation of Corneal Endothelium Using an Efficient Hybrid Deep Learning Model.

Cornea
PURPOSE: The corneal endothelium, the innermost layer of the human cornea, exhibits a morphology of predominantly hexagonal cells. These endothelial cells are believed to have limited regeneration capacity, and their density decreases over time. Endo...