ArticleScientific reports2024
Two-stage deep neural network for diagnosing fungal keratitis via in vivo confocal microscopy images.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Artificial Intelligence Application in Cornea and External Diseases.Diagnostics (Basel, Switzerland) · 2025Review
- In Vivo Confocal Microscopy and Anterior Segment Optical Coherence Tomography in Optimizing Diagnosis and Therapeutic Management in Fungal Keratitis: Case Reports and Literature Review.Journal of clinical medicine · 2025Article
- Artificial intelligence derived grading of mustard gas induced corneal injury and opacity.Scientific reports · 2025Article
- Ensemble Machine Learning Approaches for Automated Fungal Keratitis Diagnosis Using In Vivo Confocal Microscopy Images.Healthcare technology lettersArticle
- Applications of Computer Vision for Infectious Keratitis: A Systematic Review.Ophthalmology scienceReview
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
Timely and effective diagnosis of fungal keratitis (FK) is necessary for suitable treatment and avoiding irreversible vision loss for patients. In vivo confocal microscopy (IVCM) has been widely adopted to guide the FK diagnosis. We present a deep learning framework for diagnosing fungal keratitis using IVCM images to assist ophthalmologists. Inspired by the real diagnostic process, our method employs a two-stage deep architecture for diagnostic predictions based on both image-level and sequence-level information. To the best of our knowledge, we collected the largest dataset with 96,632 IVCM images in total with expert labeling to train and evaluate our method. The specificity and sensitivity of our method in diagnosing FK on the unseen test set achieved 96.65% and 97.57%, comparable or better than experienced ophthalmologists. The network can provide image-level, sequence-level and patient-level diagnostic suggestions to physicians. The results show great promise for assisting ophthalmologists in FK diagnosis.
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Registered trials
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