ArticleNPJ digital medicine2024
Deep learning for multi-type infectious keratitis diagnosis: A nationwide, cross-sectional, multicenter study.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed.
- Article
- Leakage-Aware Visit-Level Benchmarking Reveals Representational Overlap in Deep Learning for Bacterial Versus Fungal Keratitis Classification.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
- Artificial intelligence in microbial keratitis.Indian journal of ophthalmology · 2026Article
- Effective automatic classification methods via deep learning for multi-type infectious keratitis diagnosis.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
- Multicentre Pixel-Level Tear Meniscus Segmentation Dataset with Multimodal Imaging for Dry Eye Diagnosis.Scientific data · 2025Article
- Artificial Intelligence Application in Cornea and External Diseases.Diagnostics (Basel, Switzerland) · 2025Review
- Diagnostic Performance of ChatGPT in Corneal Disease Recognition From Slit-Lamp Photographs.Cureus · 2025Article
- Deep learning radiomics andTranslational lung cancer research · 2025Article
- 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
- Large language model-based multimodal system for detecting and grading ocular surface diseases from smartphone images.Frontiers in cell and developmental biology · 2025Article
- A Domain-Specific Pretrained Model for Detecting Malignant and Premalignant Ocular Surface Tumors: A Multicenter Model Development and Evaluation Study.Research (Washington, D.C.) · 2025Article
- Deep learning-enabled transformation of anterior segment images to corneal fluorescein staining images for enhanced corneal disease screening.Computational and structural biotechnology journal · 2025Article
- Diagnostic performance of deep learning for infectious keratitis: a systematic review and meta-analysis.EClinicalMedicine · 2024Article
- Effective automatic classification methods via deep learning for myopic maculopathy.Frontiers in medicine · 2024Article
- DeepMonitoring: a deep learning-based monitoring system for assessing the quality of cornea images captured by smartphones.Frontiers in cell and developmental biology · 2024Article
- Applications of Computer Vision for Infectious Keratitis: A Systematic Review.Ophthalmology scienceReview
Corrections and comments
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Authors and funding
20 authors.
Funding
Abstract
The main cause of corneal blindness worldwide is keratitis, especially the infectious form caused by bacteria, fungi, viruses, and Acanthamoeba. The key to effective management of infectious keratitis hinges on prompt and precise diagnosis. Nevertheless, the current gold standard, such as cultures of corneal scrapings, remains time-consuming and frequently yields false-negative results. Here, using 23,055 slit-lamp images collected from 12 clinical centers nationwide, this study constructed a clinically feasible deep learning system, DeepIK, that could emulate the diagnostic process of a human expert to identify and differentiate bacterial, fungal, viral, amebic, and noninfectious keratitis. DeepIK exhibited remarkable performance in internal, external, and prospective datasets (all areas under the receiver operating characteristic curves > 0.96) and outperformed three other state-of-the-art algorithms (DenseNet121, InceptionResNetV2, and Swin-Transformer). Our study indicates that DeepIK possesses the capability to assist ophthalmologists in accurately and swiftly identifying various infectious keratitis types from slit-lamp images, thereby facilitating timely and targeted treatment.
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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.