ArticleEye and vision (London, England)2020
Deep learning-based analysis of macaque corneal sub-basal nerve fibers in confocal microscopy images.
Article in Eye and vision (London, England), 2020. 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.
- An Annotated Corneal Confocal Microscopy Dataset for Nerve Segmentation and Clinical Characterization.Scientific data · 2026Article
- Applications of Artificial Intelligence in Corneal Nerve Images in Ophthalmology.Diagnostics (Basel, Switzerland) · 2026Review
- Deep learning-based segmentation and density estimation of corneal nerves and dendritic cells from In Vivo confocal microscopy images.Scientific reports · 2026Article
- A Comprehensive Review Comparing Artificial Intelligence and Clinical Diagnostic Approaches for Dry Eye Disease.Diagnostics (Basel, Switzerland) · 2025Review
- Development and validation of a deep learning model for diagnosing neuropathic corneal pain via in vivo confocal microscopy.NPJ digital medicine · 2025Article
- A Switch from Glial to Neuronal Gene Expression Alterations in the Spinal Cord of SIV-infected Macaques on Antiretroviral Therapy.Journal of neuroimmune pharmacology : the official journal of the Society on NeuroImmune Pharmacology · 2024Article
- Decellularized squid mantle scaffolds as tissue-engineered corneal stroma for promoting corneal regeneration.Bioengineering & translational medicine · 2023Article
- In-vivo corneal confocal microscopy: Imaging analysis, biological insights and future directions.Communications biology · 2023Review
- Segmentation and Classification Approaches of Clinically Relevant Curvilinear Structures: A Review.Journal of medical systems · 2023Review
- How long does the recovery of corneal sensitivity in different corneal regions take after LASEK?International journal of ophthalmology · 2023Article
- Corneal Confocal Microscopy as a Quantitative Imaging Biomarker of Diabetic Peripheral Neuropathy: A Review.Journal of clinical medicine · 2022Review
- Corneal Nerve Changes Observed by In Vivo Confocal Microscopy in Patients Receiving Oxaliplatin for Colorectal Cancer: The COCO Study.Journal of clinical medicine · 2022Article
- Segmentation and Evaluation of Corneal Nerves and Dendritic Cells From In Vivo Confocal Microscopy Images Using Deep Learning.Translational vision science & technology · 2022Article
- Artificial intelligence utilising corneal confocal microscopy for the diagnosis of peripheral neuropathy in diabetes mellitus and prediabetes.Diabetologia · 2022Article
- Corneal confocal microscopy demonstrates axonal loss in different courses of multiple sclerosis.Scientific reports · 2021Article
- Artificial Intelligence in Cornea, Refractive Surgery, and Cataract: Basic Principles, Clinical Applications, and Future Directions.Asia-Pacific journal of ophthalmology (Philadelphia, Pa.) · 2021Review
- Generative Adversarial Network Based Automatic Segmentation of Corneal Subbasal Nerves on In Vivo Confocal Microscopy Images.Translational vision science & technology · 2021Article
- Characteristics of Corneal Subbasal Nerves in Different Age Groups: An in vivo Confocal Microscopic Analysis.Clinical ophthalmology (Auckland, N.Z.) · 2021Article
- Comparative Anatomy of the Mammalian Corneal Subbasal Nerve Plexus.Investigative ophthalmology & visual science · 2019Article
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Authors and funding
8 authors.
Funding
Abstract
backgroundTo develop and validate a deep learning-based approach to the fully-automated analysis of macaque corneal sub-basal nerves using in vivo confocal microscopy (IVCM).
methodsIVCM was used to collect 108 images from 35 macaques. 58 of the images from 22 macaques were used to evaluate different deep convolutional neural network (CNN) architectures for the automatic analysis of sub-basal nerves relative to manual tracings. The remaining images were used to independently assess correlations and inter-observer performance relative to three readers.
resultsCorrelation scores using the coefficient of determination between readers and the best CNN averaged 0.80. For inter-observer comparison, inter-correlation coefficients (ICCs) between the three expert readers and the automated approach were 0.75, 0.85 and 0.92. The ICC between all four observers was 0.84, the same as the average between the CNN and individual readers.
conclusionsDeep learning-based segmentation of sub-basal nerves in IVCM images shows high to very high correlation to manual segmentations in macaque data and is indistinguishable across readers. As quantitative measurements of corneal sub-basal nerves are important biomarkers for disease screening and management, the reported work offers utility to a variety of research and clinical studies using IVCM.
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