ArticleTranslational vision science & technology2020
Deep Learning for Assessing the Corneal Endothelium from Specular Microscopy Images up to 1 Year after Ultrathin-DSAEK Surgery.
Article in Translational vision science & technology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Current Applications of Artificial Intelligence for Fuchs Endothelial Corneal Dystrophy: A Systematic Review.Translational vision science & technology · 2025Pooled it
- Recent advances in corneal specular microscopy image analysis through artificial intelligence.PLOS digital health · 2026Review
- Artificial intelligence in corneal endothelial disease and endothelial keratoplasty: current evidence, clinical readiness, and future directions.Frontiers in ophthalmology · 2026Review
- Clinical Applications of Artificial Intelligence in Corneal Diseases.Vision (Basel, Switzerland) · 2025Review
- Current concepts and recent trends in endothelial keratoplasty.World journal of transplantation · 2025Review
- Deep learning-assisted widefield endothelial imaging in Descemet membrane endothelial keratoplasty.Frontiers in medicine · 2025Article
- Factors Affecting the Density of Corneal Endothelial Cells Cultured from Donor Corneas.International journal of molecular sciences · 2024Article
- Automatic Determination of Endothelial Cell Density From Donor Cornea Endothelial Cell Images.Translational vision science & technology · 2024Article
- Deep learning for detection of Fuchs endothelial dystrophy from widefield specular microscopy imaging: a pilot study.Eye and vision (London, England) · 2024Article
- Applications of Imaging Technologies in Fuchs Endothelial Corneal Dystrophy: A Narrative Literature Review.Bioengineering (Basel, Switzerland) · 2024Review
- U-Net Convolutional Neural Network for Real-Time Prediction of the Number of Cultured Corneal Endothelial Cells for Cellular Therapy.Bioengineering (Basel, Switzerland) · 2024Article
- Deep learning segmentation of endothelial cell images using an active learning paradigm with guided label corrections.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Donor lamella thickness after ultrathin Descemet stripping automated endothelial keratoplasty and its relation to postoperative visual acuity and pre-operative lamella measures.BMC ophthalmology · 2023Article
- Assessing abnormal corneal endothelial cells from in vivo confocal microscopy images using a fully automated deep learning system.Eye and vision (London, England) · 2023Article
- Artificial Intelligence for Anterior Segment Diseases: A Review of Potential Developments and Clinical Applications.Ophthalmology and therapy · 2023Review
- Machine Learning Analysis of Postkeratoplasty Endothelial Cell Images for the Prediction of Future Graft Rejection.Translational vision science & technology · 2023Article
- Corneal endothelium assessment in specular microscopy images with Fuchs' dystrophy via deep regression of signed distance maps.Biomedical optics express · 2023Article
- Guidelines for the application of artificial intelligence in the diagnosis of anterior segment diseases (2023).International journal of ophthalmology · 2023Article
- Big data in corneal diseases and cataract: Current applications and future directions.Frontiers in big data · 2023Review
- Artificial intelligence and corneal diseases.Current opinion in ophthalmology · 2022Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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. Methods: We analyzed 383 post ultrathin Descemet stripping automated endothelial keratoplasty images from 41 eyes acquired with a Topcon SP-1P specular microscope at 1, 3, 6, and 12 months after surgery. The estimated parameters were endothelial cell density (ECD), coefficient of variation (CV), and hexagonality (HEX). Manual segmentation was performed in all images. Results: Our method provided an estimate for ECD, CV, and HEX in 98.4% of the images, whereas Topcon's software had a success rate of 71.5% for ECD/CV and 30.5% for HEX. For the images with estimates, the percentage error in our method was 2.5% for ECD, 5.7% for CV, and 5.7% for HEX, whereas Topcon's software provided an error of 7.5% for ECD, 17.5% for CV, and 18.3% for HEX. Our method was significantly better than Topcon's ( Conclusions: The proposed method obtains reliable and accurate estimations even in challenging specular images of pathologic corneas. Translational Relevance: CV and HEX, not currently used in the clinic owing to a lack of reliability in automatic methods, are useful biomarkers to analyze the postoperative healing process. Our accurate estimations allow now for their clinical use.
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