ArticleAnnals of translational medicine2021
Prediction of keratoconus progression using deep learning of anterior segment optical coherence tomography maps.
Article in Annals of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The application of artificial intelligence-based algorithms in predicting the progression of keratoconus: a systematic review.International ophthalmology · 2025Pooled it
- Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review.Frontiers in ophthalmology · 2024Pooled it
- From Morphology to Biomarkers: Optical Coherence Tomography in Corneal Disease.Diseases (Basel, Switzerland) · 2026Review
- Prediction of Maximum Keratometry Progression With Stratified Progression Criteria in Keratoconus Patients Using Machine Learning.Translational vision science & technology · 2026Article
- Automated Triage for New Keratoconus Referrals Using Multimodal Deep Learning.Ophthalmology science · 2026Article
- [Keratoconus progression assessment].Die Ophthalmologie · 2025Review
- Clinical Applications of Artificial Intelligence in Corneal Diseases.Vision (Basel, Switzerland) · 2025Review
- Can high-resolution optical coherence tomography provide an optical biopsy for ocular surface lesions?Canadian journal of ophthalmology. Journal canadien d'ophtalmologie · 2025Review
- Individual Risk Assessment and Prognostication of Outcomes After Corneal Cross-Linking.Journal of ophthalmology · 2025Article
- Cochrane corner: artificial intelligence for keratoconus.Eye (London, England) · 2024Article
- Keratoconus.Nature reviews. Disease primers · 2024Article
- Keratoconus Progression Determined at the First Visit: A Deep Learning Approach With Fusion of Imaging and Numerical Clinical Data.Translational vision science & technology · 2024Article
- Deep learning prediction of steep and flat corneal curvature using fundus photography in post-COVID telemedicine era.Medical & biological engineering & computing · 2024Article
- Deep Learning-Based Automatic Diagnosis of Keratoconus with Corneal Endothelium Image.Ophthalmology and therapy · 2023Article
- A Deep Feature Fusion of Improved Suspected Keratoconus Detection with Deep Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Artificial intelligence-assisted diagnosis of ocular surface diseases.Frontiers in cell and developmental biology · 2023Review
- Artificial intelligence and corneal diseases.Current opinion in ophthalmology · 2022Review
- Advances in artificial intelligence applications for ocular surface diseases diagnosis.Frontiers in cell and developmental biology · 2022Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo predict keratoconus progression using deep learning of the color-coded maps measured with a swept-source anterior segment optical coherence tomography (As-OCT) device.
methodsWe enrolled 218 keratoconic eyes with and without disease progression. Using deep learning of the 6 color-coded maps (anterior elevation, anterior curvature, posterior elevation, posterior curvature, total refractive power, and pachymetry map) obtained by the As-OCT (CASIA, Tomey), we assessed the accuracy, sensitivity, and specificity of prediction of keratoconus progression in such eyes.
resultsDeep learning of the 6 color-coded maps exhibited an accuracy of 0.794 in discriminating keratoconus with and without progression. For a single map analysis, posterior elevation map (0.798) showed the highest accuracy, followed by anterior curvature map (0.775), posterior corneal curvature map (0.757), anterior elevation map (0.752), total refractive power map (0.729), and pachymetry map (0.720), in distinguishing between progressive and non-progressive keratoconus. The use of the adjusted algorithm by age subgroups improved to an accuracy of 0.849.
conclusionsDeep learning of the As-OCT color-coded maps effectively discriminates progressive keratoconus from non-progressive keratoconus with an accuracy of approximately 85% using the adjusted age algorithm, indicating that it will become an aid for predicting the progression of the disease, which is clinically beneficial for decision-making of the surgical indication of corneal cross-linking (CXL).
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