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Artificial Intelligence and Corneal Confocal Microscopy: The Start of a Beautiful Relationship
Corneal confocal microscopy (CCM) is a rapid non-invasive in vivo ophthalmic imaging technique that images the cornea. Historically, it was utilised in the diagnosis and clinical management of corneal epithelial and stromal disorders. However, over the past 20 years, CCM has been increasingly used t...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9604848/ https://www.ncbi.nlm.nih.gov/pubmed/36294519 http://dx.doi.org/10.3390/jcm11206199 |
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author | Alam, Uazman Anson, Matthew Meng, Yanda Preston, Frank Kirthi, Varo Jackson, Timothy L. Nderitu, Paul Cuthbertson, Daniel J. Malik, Rayaz A. Zheng, Yalin Petropoulos, Ioannis N. |
author_facet | Alam, Uazman Anson, Matthew Meng, Yanda Preston, Frank Kirthi, Varo Jackson, Timothy L. Nderitu, Paul Cuthbertson, Daniel J. Malik, Rayaz A. Zheng, Yalin Petropoulos, Ioannis N. |
author_sort | Alam, Uazman |
collection | PubMed |
description | Corneal confocal microscopy (CCM) is a rapid non-invasive in vivo ophthalmic imaging technique that images the cornea. Historically, it was utilised in the diagnosis and clinical management of corneal epithelial and stromal disorders. However, over the past 20 years, CCM has been increasingly used to image sub-basal small nerve fibres in a variety of peripheral neuropathies and central neurodegenerative diseases. CCM has been used to identify subclinical nerve damage and to predict the development of diabetic peripheral neuropathy (DPN). The complex structure of the corneal sub-basal nerve plexus can be readily analysed through nerve segmentation with manual or automated quantification of parameters such as corneal nerve fibre length (CNFL), nerve fibre density (CNFD), and nerve branch density (CNBD). Large quantities of 2D corneal nerve images lend themselves to the application of artificial intelligence (AI)-based deep learning algorithms (DLA). Indeed, DLA have demonstrated performance comparable to manual but superior to automated quantification of corneal nerve morphology. Recently, our end-to-end classification with a 3 class AI model demonstrated high sensitivity and specificity in differentiating healthy volunteers from people with and without peripheral neuropathy. We believe there is significant scope and need to apply AI to help differentiate between peripheral neuropathies and also central neurodegenerative disorders. AI has significant potential to enhance the diagnostic and prognostic utility of CCM in the management of both peripheral and central neurodegenerative diseases. |
format | Online Article Text |
id | pubmed-9604848 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96048482022-10-27 Artificial Intelligence and Corneal Confocal Microscopy: The Start of a Beautiful Relationship Alam, Uazman Anson, Matthew Meng, Yanda Preston, Frank Kirthi, Varo Jackson, Timothy L. Nderitu, Paul Cuthbertson, Daniel J. Malik, Rayaz A. Zheng, Yalin Petropoulos, Ioannis N. J Clin Med Review Corneal confocal microscopy (CCM) is a rapid non-invasive in vivo ophthalmic imaging technique that images the cornea. Historically, it was utilised in the diagnosis and clinical management of corneal epithelial and stromal disorders. However, over the past 20 years, CCM has been increasingly used to image sub-basal small nerve fibres in a variety of peripheral neuropathies and central neurodegenerative diseases. CCM has been used to identify subclinical nerve damage and to predict the development of diabetic peripheral neuropathy (DPN). The complex structure of the corneal sub-basal nerve plexus can be readily analysed through nerve segmentation with manual or automated quantification of parameters such as corneal nerve fibre length (CNFL), nerve fibre density (CNFD), and nerve branch density (CNBD). Large quantities of 2D corneal nerve images lend themselves to the application of artificial intelligence (AI)-based deep learning algorithms (DLA). Indeed, DLA have demonstrated performance comparable to manual but superior to automated quantification of corneal nerve morphology. Recently, our end-to-end classification with a 3 class AI model demonstrated high sensitivity and specificity in differentiating healthy volunteers from people with and without peripheral neuropathy. We believe there is significant scope and need to apply AI to help differentiate between peripheral neuropathies and also central neurodegenerative disorders. AI has significant potential to enhance the diagnostic and prognostic utility of CCM in the management of both peripheral and central neurodegenerative diseases. MDPI 2022-10-20 /pmc/articles/PMC9604848/ /pubmed/36294519 http://dx.doi.org/10.3390/jcm11206199 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Alam, Uazman Anson, Matthew Meng, Yanda Preston, Frank Kirthi, Varo Jackson, Timothy L. Nderitu, Paul Cuthbertson, Daniel J. Malik, Rayaz A. Zheng, Yalin Petropoulos, Ioannis N. Artificial Intelligence and Corneal Confocal Microscopy: The Start of a Beautiful Relationship |
title | Artificial Intelligence and Corneal Confocal Microscopy: The Start of a Beautiful Relationship |
title_full | Artificial Intelligence and Corneal Confocal Microscopy: The Start of a Beautiful Relationship |
title_fullStr | Artificial Intelligence and Corneal Confocal Microscopy: The Start of a Beautiful Relationship |
title_full_unstemmed | Artificial Intelligence and Corneal Confocal Microscopy: The Start of a Beautiful Relationship |
title_short | Artificial Intelligence and Corneal Confocal Microscopy: The Start of a Beautiful Relationship |
title_sort | artificial intelligence and corneal confocal microscopy: the start of a beautiful relationship |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9604848/ https://www.ncbi.nlm.nih.gov/pubmed/36294519 http://dx.doi.org/10.3390/jcm11206199 |
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