ArticleAnnals of translational medicine2020
Deep learning-based automated diagnosis of fungal keratitis with
Article in Annals of translational medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06332703 (Acanthamoeba and Artificial Intelligence), which is not on this map. Cited by 32 papers.
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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.
Acanthamoeba and Artificial Intelligence: Single-center Retrospective Observational Study
Who cites it
32 citing papers in PubMed.
- IVCM-Insight: automated interactive interpretation of in vivo confocal microscopy.NPJ digital medicine · 2026Article
- Bridging Gaps in Fungal Keratitis Management: Novel Diagnostics, Drug Delivery Systems, and Gene Therapies.AAPS PharmSciTech · 2026Review
- Clinical Applications of Artificial Intelligence in Corneal Diseases.Vision (Basel, Switzerland) · 2025Review
- An explainable unsupervised learning approach for anomaly detection on cornealFrontiers in bioengineering and biotechnology · 2025Article
- Diagnostic performance of deep learning for infectious keratitis: a systematic review and meta-analysis.EClinicalMedicine · 2024Article
- Two-stage deep neural network for diagnosing fungal keratitis via in vivo confocal microscopy images.Scientific reports · 2024Article
- Establishment of a corneal ulcer prognostic model based on machine learning.Scientific reports · 2024Article
- Artificial-Intelligence-Enhanced Analysis of In Vivo Confocal Microscopy in Corneal Diseases: A Review.Diagnostics (Basel, Switzerland) · 2024Review
- Potential applications of artificial intelligence in image analysis in cornea diseases: a review.Eye and vision (London, England) · 2024Review
- AI-Based Decision-Support System for Diagnosing Acanthamoeba Keratitis Using In Vivo Confocal Microscopy Images.Translational vision science & technology · 2023Article
- Updates in Diagnostic Imaging for Infectious Keratitis: A Review.Diagnostics (Basel, Switzerland) · 2023Review
- Artificial Intelligence and Infectious Keratitis: Where Are We Now?Life (Basel, Switzerland) · 2023Review
- Diagnosis ofDiagnostics (Basel, Switzerland) · 2023Review
- A Structure-Aware Convolutional Neural Network for Automatic Diagnosis of Fungal Keratitis with In Vivo Confocal Microscopy Images.Journal of digital imaging · 2023Article
- Artificial intelligence in ophthalmology: The path to the real-world clinic.Cell reports. Medicine · 2023Review
- An artificial intelligence approach to classify pathogenic fungal genera of fungal keratitis using corneal confocal microscopy images.International ophthalmology · 2023Article
- Diagnosis of invasive fungal infections: challenges and recent developments.Journal of biomedical science · 2023Review
- Interpretable deep learning for diagnosis of fungal and acanthamoeba keratitis using in vivo confocal microscopy images.Scientific reports · 2023Article
- Artificial Intelligence for Anterior Segment Diseases: A Review of Potential Developments and Clinical Applications.Ophthalmology and therapy · 2023Review
- Segmentation and Classification Approaches of Clinically Relevant Curvilinear Structures: A Review.Journal of medical systems · 2023Review
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Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe aim of this study was to develop an intelligent system based on a deep learning algorithm for automatically diagnosing fungal keratitis (FK) in in vivo confocal microscopy (IVCM) images.
methodsA total of 2,088 IVCM images were included in the training dataset. The positive group consisted of 688 images with fungal hyphae, and the negative group included 1,400 images without fungal hyphae. A total of 535 images in the testing dataset were not included in the training dataset. Deep Residual Learning for Image Recognition (ResNet) was used to build the intelligent system for diagnosing FK automatically. The system was verified by external validation in the testing dataset using the area under the receiver operating characteristic curve (AUC), accuracy, specificity and sensitivity.
resultsIn the testing dataset, 515 images were diagnosed correctly and 20 were misdiagnosed (including 6 with fungal hyphae and 14 without). The system achieved an AUC of 0.9875 with an accuracy of 0.9626 in detecting fungal hyphae. The sensitivity of the system was 0.9186, with a specificity of 0.9834. When 349 diabetic patients were included in the training dataset, 501 images were diagnosed correctly and thirty-four were misdiagnosed (including 4 with fungal hyphae and 30 without). The AUC of the system was 0.9769. The accuracy, specificity and sensitivity were 0.9364, 0.9889 and 0.8256, respectively.
conclusionsThe intelligent system based on a deep learning algorithm exhibited satisfactory diagnostic performance and effectively classified FK in various IVCM images. The context of this deep learning automated diagnostic system can be extended to other types of keratitis.
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