ArticleBiomedical optics express2019
CorneaNet: fast segmentation of cornea OCT scans of healthy and keratoconic eyes using deep learning.
Article in Biomedical optics express, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers, 4 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
68 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Early Detection of Keratoconus: Diagnostic Advances and Their Impact on Visual Outcomes: A Systematic Review.Medicina (Kaunas, Lithuania) · 2025Pooled it
- Comparison of different corneal imaging modalities using artificial intelligence for diagnosis of keratoconus: a systematic review and meta-analysis.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2024Pooled it
- Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review.Frontiers in ophthalmology · 2024Pooled it
- Artificial intelligence for detecting keratoconus.The Cochrane database of systematic reviews · 2023Pooled it
- Outer retinal band segmentation in healthy subjects: comparative study between human grading and deep convolutional neural networks.Quantitative imaging in medicine and surgery · 2026Article
- Artificial intelligence for diagnosis of keratoconus using Scheimpflug based corneal tomography.International journal of ophthalmology · 2026Article
- Automated quantification of central corneal epithelial thickness for the diagnosis of limbal stem cell deficiency.PloS one · 2026Article
- Artificial Intelligence in Ophthalmology: Practical Applications, Subspecialty Evidence and Real-World Deployment.Cureus · 2025Review
- Proposed Nomenclature for Landmarks in Anterior-Segment OCT: The APOSTEL-AS Panel Consensus.JAMA ophthalmology · 2025Article
- Clinical Applications of Artificial Intelligence in Corneal Diseases.Vision (Basel, Switzerland) · 2025Review
- Corneal Layer Segmentation in Healthy and Pathological Eyes: A Joint Super-Resolution Generative Adversarial Network and Adaptive Graph Theory Approach.Translational vision science & technology · 2025Article
- Optical coherence tomography-enabled classification of the human venoatrial junction.Journal of biomedical optics · 2025Article
- Prevalence of keratoconus and keratoconus suspect, and their characteristics on corneal tomography in a population-based study.PloS one · 2025Article
- Article
- Imaging the eye as a window to brain health: frontier approaches and future directions.Journal of neuroinflammation · 2024Review
- Deep learning based highly accurate transplanted bioengineered corneal equivalent thickness measurement using optical coherence tomography.NPJ digital medicine · 2024Article
- A Comprehensive Survey on Emerging Assistive Technologies for Visually Impaired Persons: Lighting the Path with Visible Light Communications and Artificial Intelligence Innovations.Sensors (Basel, Switzerland) · 2024Review
- ScLNet: A cornea with scleral lens OCT layers segmentation dataset and new multi-task model.Heliyon · 2024Article
- Transformer enhanced autoencoder rendering cleaning of noisy optical coherence tomography images.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Potential applications of artificial intelligence in image analysis in cornea diseases: a review.Eye and vision (London, England) · 2024Review
8 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning has dramatically improved object recognition, speech recognition, medical image analysis and many other fields. Optical coherence tomography (OCT) has become a standard of care imaging modality for ophthalmology. We asked whether deep learning could be used to segment cornea OCT images. Using a custom-built ultrahigh-resolution OCT system, we scanned 72 healthy eyes and 70 keratoconic eyes. In total, 20,160 images were labeled and used for the training in a supervised learning approach. A custom neural network architecture called CorneaNet was designed and trained. Our results show that CorneaNet is able to segment both healthy and keratoconus images with high accuracy (validation accuracy: 99.56%). Thickness maps of the three main corneal layers (epithelium, Bowman's layer and stroma) were generated both in healthy subjects and subjects suffering from keratoconus. CorneaNet is more than 50 times faster than our previous algorithm. Our results show that deep learning algorithms can be used for OCT image segmentation and could be applied in various clinical settings. In particular, CorneaNet could be used for early detection of keratoconus and more generally to study other diseases altering corneal morphology.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.