AIMC Topic: Endothelium, Corneal

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

PloS one Nov 12, 2024
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...
Neural Networks, Computer Endothelium, Corneal Algorithms Image Processing, Computer-Assisted Humans
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Predicting Success in Descemet Membrane Endothelial Keratoplasty Using Machine Learning.

Cornea Jun 20, 2024
PURPOSE: This study aimed to predict early graft failure (GF) in patients who underwent Descemet membrane endothelial keratoplasty based on donor characteristics.
ROC Curve Humans Tissue Donors Retrospective Studies Adult Corneal Diseases Graft Survival Machine Learning Female Visual Acuity Male Endothelium, Corneal Aged Graft Rejection Middle Aged Descemet Stripping Endothelial Keratoplasty Cell Count
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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 Apr 4, 2024
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...
Humans Artificial Intelligence Deep Learning Eye Banks Microscopy, Phase-Contrast Endothelium, Corneal Cell Count Corneal Transplantation Quality Control
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Machine learning based endothelial cell image analysis of patients undergoing descemet membrane endothelial keratoplasty surgery.

Biomedizinische Technik. Biomedical engineering Mar 18, 2024
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).
Image Processing, Computer-Assisted Microscopy, Confocal Endothelial Cells Humans Machine Learning Endothelium, Corneal Descemet Membrane Descemet Stripping Endothelial Keratoplasty
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Assessing Fuchs Corneal Endothelial Dystrophy Using Artificial Intelligence-Derived Morphometric Parameters From Specular Microscopy Images.

Cornea Feb 9, 2024
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.
Male Middle Aged Cell Count Microscopy Endothelium, Corneal Fuchs' Endothelial Dystrophy Artificial Intelligence Aged Female Aged, 80 and over Humans Cross-Sectional Studies Neural Networks, Computer
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Mobile-CellNet: Automatic Segmentation of Corneal Endothelium Using an Efficient Hybrid Deep Learning Model.

Cornea Dec 12, 2022
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...
Endothelial Cells Endothelium, Corneal Cornea Deep Learning Cell Count Humans
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Deep Learning for Assessing the Corneal Endothelium from Specular Microscopy Images up to 1 Year after Ultrathin-DSAEK Surgery.

Translational vision science & technology Aug 21, 2020
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.
Deep Learning Endothelium, Corneal Cell Count Reproducibility of Results Microscopy
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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 Mar 18, 2019
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...
Image Processing, Computer-Assisted Cell Count Microscopy Humans Neural Networks, Computer Endothelial Cells Endothelium, Corneal
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Segmentation of corneal endothelium images using a U-Net-based convolutional neural network.

Artificial intelligence in medicine Apr 19, 2018
Diagnostic information regarding the health status of the corneal endothelium may be obtained by analyzing the size and the shape of the endothelial cells in specular microscopy images. Prior to the analysis, the endothelial cells need to be extracte...
Microscopy Databases, Factual Humans Cell Shape Reproducibility of Results Image Interpretation, Computer-Assisted Neural Networks, Computer Cell Size Predictive Value of Tests Endothelial Cells Endothelium, Corneal
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Deep Learning Using Preoperative AS-OCT Predicts Graft Detachment in DMEK.

Translational vision science & technology May 1, 2023
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.
Humans Artificial Intelligence Retrospective Studies Deep Learning Endothelium, Corneal Visual Acuity Descemet Stripping Endothelial Keratoplasty Tomography, Optical Coherence Corneal Diseases
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