AI Medical Compendium Topic

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Endothelium, Corneal

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Automated segmentation of the corneal endothelium in a large set of 'real-world' specular microscopy images using the U-Net architecture.

Scientific reports
Monitoring the density of corneal endothelial cells (CEC) is essential in the management of corneal diseases. Its manual calculation is time consuming and prone to errors. U-Net, a neural network for biomedical image segmentation, has shown promising...

Deep Learning for Assessing the Corneal Endothelium from Specular Microscopy Images up to 1 Year after Ultrathin-DSAEK Surgery.

Translational vision science & technology
PURPOSE: To present a fully automatic method to estimate the corneal endothelium parameters from specular microscopy images and to use it to study a one-year follow-up after ultrathin Descemet stripping automated endothelial keratoplasty.

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...

Deep Learning Using Preoperative AS-OCT Predicts Graft Detachment in DMEK.

Translational vision science & technology
PURPOSE: To evaluate a novel deep learning algorithm to distinguish between eyes that may or may not have a graft detachment based on pre-Descemet membrane endothelial keratoplasty (DMEK) anterior segment optical coherence tomography (AS-OCT) images.

Data augmentation via warping transforms for modeling natural variability in the corneal endothelium enhances semi-supervised segmentation.

PloS one
Image segmentation of the corneal endothelium with deep convolutional neural networks (CNN) is challenging due to the scarcity of expert-annotated data. This work proposes a data augmentation technique via warping to enhance the performance of semi-s...

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.

Machine learning based endothelial cell image analysis of patients undergoing descemet membrane endothelial keratoplasty surgery.

Biomedizinische Technik. Biomedical engineering
OBJECTIVES: In this study, we developed a machine learning approach for postoperative corneal endothelial cell images of patients who underwent Descemet's membrane keratoplasty (DMEK).

Quality Control in the Corneal Bank with Artificial Intelligence: Comparison of a New Deep Learning-based Approach with Conventional Endothelial Cell Counting by the "Rhine-Tec Endothelial Analysis System".

Klinische Monatsblatter fur Augenheilkunde
Endothelial cell density (ECD) is a crucial parameter for the release of corneal grafts for transplantation. The Lions Eye Bank of Baden-Württemberg uses the "Rhine-Tec Endothelial Analysis System" for ECD quantification, which is based on a fixed co...