ArticlePloS one2021
A deep transfer learning framework for the automated assessment of corneal inflammation on in vivo confocal microscopy images.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 26 citations in OpenAlex.
- IVCM-Insight: automated interactive interpretation of in vivo confocal microscopy.NPJ digital medicine · 2026Article
- Deep learning-based segmentation and density estimation of corneal nerves and dendritic cells from In Vivo confocal microscopy images.Scientific reports · 2026Article
- Diagnostic methods for managing dry eyes.World journal of methodology · 2025Article
- An explainable unsupervised learning approach for anomaly detection on cornealFrontiers in bioengineering and biotechnology · 2025Article
- 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
- Assessment of Deep Learning Models for Cutaneous Leishmania Parasite Diagnosis Using Microscopic Images.Diagnostics (Basel, Switzerland) · 2023Article
- An artificial intelligence approach to classify pathogenic fungal genera of fungal keratitis using corneal confocal microscopy images.International ophthalmology · 2023Article
- Automated identification and quantification of activated dendritic cells in central cornea using artificial intelligence.The ocular surface · 2023Article
- In-vivo corneal confocal microscopy: Imaging analysis, biological insights and future directions.Communications biology · 2023Review
- A Hybrid System for Automatic Identification of Corneal Layers on In Vivo Confocal Microscopy Images.Translational vision science & technology · 2023Article
- Deep Convolutional Neural Networks Detect no Morphological Differences Between Culture-Positive and Culture-Negative Infectious Keratitis Images.Translational vision science & technology · 2023Article
- Evaluation of a computer-aided diagnostic model for corneal diseases by analyzingFrontiers in medicine · 2023Article
- Integration of Artificial Intelligence into the Approach for Diagnosis and Monitoring of Dry Eye Disease.Diagnostics (Basel, Switzerland) · 2022Review
- Image-Based Differentiation of Bacterial and Fungal Keratitis Using Deep Convolutional Neural Networks.Ophthalmology science · 2022Article
- Classification of the Confocal Microscopy Images of Colorectal Tumor and Inflammatory Colitis Mucosa Tissue Using Deep Learning.Diagnostics (Basel, Switzerland) · 2022Article
- Advances in artificial intelligence applications for ocular surface diseases diagnosis.Frontiers in cell and developmental biology · 2022Review
- The Clinical Value of Explainable Deep Learning for Diagnosing Fungal Keratitis UsingFrontiers in medicine · 2021Article
- Artificial Intelligence in Clinical Diagnosis and Treatment of Dry Eye: Workflows, Effectiveness, and Evaluation.Journal of current ophthalmologyReview
- Applications of Computer Vision for Infectious Keratitis: A Systematic Review.Ophthalmology scienceReview
Corrections and comments
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Authors and funding
17 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeInfiltration of activated dendritic cells and inflammatory cells in cornea represents an important marker for defining corneal inflammation. Deep transfer learning has presented a promising potential and is gaining more importance in computer assisted diagnosis. This study aimed to develop deep transfer learning models for automatic detection of activated dendritic cells and inflammatory cells using in vivo confocal microscopy images.
methodsA total of 3453 images was used to train the models. External validation was performed on an independent test set of 558 images. A ground-truth label was assigned to each image by a panel of cornea specialists. We constructed a deep transfer learning network that consisted of a pre-trained network and an adaptation layer. In this work, five pre-trained networks were considered, namely VGG-16, ResNet-101, Inception V3, Xception, and Inception-ResNet V2. The performance of each transfer network was evaluated by calculating the area under the curve (AUC) of receiver operating characteristic, accuracy, sensitivity, specificity, and G mean.
resultsThe best performance was achieved by Inception-ResNet V2 transfer model. In the validation set, the best transfer system achieved an AUC of 0.9646 (P<0.001) in identifying activated dendritic cells (accuracy, 0.9319; sensitivity, 0.8171; specificity, 0.9517; and G mean, 0.8872), and 0.9901 (P<0.001) in identifying inflammatory cells (accuracy, 0.9767; sensitivity, 0.9174; specificity, 0.9931; and G mean, 0.9545).
conclusionsThe deep transfer learning models provide a completely automated analysis of corneal inflammatory cellular components with high accuracy. The implementation of such models would greatly benefit the management of corneal diseases and reduce workloads for ophthalmologists.
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