ArticleScientific reports2023
Automated cornea diagnosis using deep convolutional neural networks based on cornea topography maps.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.
- Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review.Frontiers in ophthalmology · 2024Pooled it
- Orbscan IIz multimodal corneal topography dataset of anterior axial power maps with structured clinical parameters for unsupervised and self-supervised representation learning.Data in brief · 2026Article
- A Deep Learning-Based Graphical User Interface for Predicting Corneal Ectasia Scores from Raw Optical Coherence Tomography Data.Diagnostics (Basel, Switzerland) · 2026Article
- Deep Learning-Based Diagnosis of Corneal Condition by Using Raw Optical Coherence Tomography Data.Diagnostics (Basel, Switzerland) · 2025Article
- AI-assisted automated interpretation of corneal topography in orthokeratology patients: enhancing diagnostic precision and efficiency.International journal of ophthalmology · 2025Article
- Article
- Blast injury: Impact to the cornea.Experimental eye research · 2024Review
- Keratoconus disease classification with multimodel fusion and vision transformer: a pretrained model approach.BMJ open ophthalmology · 2024Article
- A Deep Feature Fusion of Improved Suspected Keratoconus Detection with Deep Learning.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cornea topography maps allow ophthalmologists to screen and diagnose cornea pathologies. We aim to automatically identify any cornea abnormalities based on such cornea topography maps, with focus on diagnosing keratoconus. To do so, we represent the OCT scans as images and apply Convolutional Neural Networks (CNNs) for the automatic analysis. The model is based on a state-of-the-art ConvNeXt CNN architecture with weights fine-tuned for the given specific application using the cornea scans dataset. A set of 1940 consecutive screening scans from the Saarland University Hospital Clinic for Ophthalmology was annotated and used for model training and validation. All scans were recorded with a CASIA2 anterior segment Optical Coherence Tomography (OCT) scanner. The proposed model achieves a sensitivity of 98.46% and a specificity of 91.96% when distinguishing between healthy and pathological corneas. Our approach enables the screening of cornea pathologies and the classification of common pathologies like keratoconus. Furthermore, the approach is independent of the topography scanner and enables the visualization of those scan regions which drive the model's decisions.
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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.