ArticleNature communications2021
Preventing corneal blindness caused by keratitis using artificial intelligence.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers, 1 of them a synthesis that pooled it.
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Who cites it
70 citing papers in PubMed, 1 synthesis or guideline pooled it, 117 citations in OpenAlex.
- Accuracy of artificial intelligence model for infectious keratitis classification: a systematic review and meta-analysis.Frontiers in public health · 2023Pooled it
- YOLOv5 Attention Analysis for Anterior Eye Disease Classification: Grad-CAM++ Feature Importance and Cut-and-Paste Validation.Journal of imaging informatics in medicine · 2026Article
- Development of a Deep Learning Model to Estimate Anemia from Palpebral Conjunctiva Taken with a Portable Slit-Lamp Microscope.Bioengineering (Basel, Switzerland) · 2026Article
- Article
- AI for Anterior Segment Disease Using Transfer Learning: Adapting Slit-Lamp-Trained Model for Analysis of Smartphone Corneal Images.Translational vision science & technology · 2026Article
- A Mixture-of-Experts Network for Infectious Keratitis Classification Using Multimodal Slit-Lamp Images: A Multicenter Study.Translational vision science & technology · 2026Article
- Smartphone-based lightweight AI system for real-time multiple anterior segment disease screening: development and real-world validation.BMC medicine · 2026Article
- Deep Learning-Based Classification of Slit-Lamp Photograph Quality in Microbial Keratitis.Ophthalmology science · 2026Article
- Seeing through collagen: integrative pro-regenerative corneal implants for clearer future.NPJ Regenerative medicine · 2026Review
- Validated semi-supervised early and accurate screening for anterior segment diseases: a 3PM-guided conceptual and technological innovation.The EPMA journal · 2026Article
- AI-Assisted detection of corneal nerve structural abnormalities in early diabetic keratopathy: development and validation of a deep learning framework.Scientific reports · 2026Article
- Artificial intelligence-based apps for screening and diagnosing diabetic retinopathy and common ocular disorders.World journal of methodology · 2025Review
- Artificial Intelligence Application in Cornea and External Diseases.Diagnostics (Basel, Switzerland) · 2025Review
- Clinical Applications of Artificial Intelligence in Corneal Diseases.Vision (Basel, Switzerland) · 2025Review
- Enhancing medical students' diagnostic accuracy of infectious keratitis with AI-generated images.BMC medical education · 2025Article
- A deep semi-supervised learning approach to the detection of glaucoma on out-of-distribution retinal fundus image datasets.BMC ophthalmology · 2025Article
- Long-lasting comfort ocular surface drug delivery by in situ formation of an adhesive lubricative Janus nanocoating.Science advances · 2025Article
- An exploration of the ocular mysteries linking nanoparticles to the patho-therapeutic effects against keratitis.Journal of nanobiotechnology · 2025Review
- Artificial intelligence support improves diagnosis accuracy in anterior segment eye diseases.Scientific reports · 2025Article
- Fusing multispectral information for retinal layer segmentation.NPJ digital medicine · 2025Article
10 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Keratitis is the main cause of corneal blindness worldwide. Most vision loss caused by keratitis can be avoidable via early detection and treatment. The diagnosis of keratitis often requires skilled ophthalmologists. However, the world is short of ophthalmologists, especially in resource-limited settings, making the early diagnosis of keratitis challenging. Here, we develop a deep learning system for the automated classification of keratitis, other cornea abnormalities, and normal cornea based on 6,567 slit-lamp images. Our system exhibits remarkable performance in cornea images captured by the different types of digital slit lamp cameras and a smartphone with the super macro mode (all AUCs>0.96). The comparable sensitivity and specificity in keratitis detection are observed between the system and experienced cornea specialists. Our system has the potential to be applied to both digital slit lamp cameras and smartphones to promote the early diagnosis and treatment of keratitis, preventing the corneal blindness caused by keratitis.
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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.