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Latent diffusion augmentation enhances deep learning analysis of neuro-morphology in limbal stem cell deficiency
INTRODUCTION: Limbal Stem Cell Deficiency (LSCD) is a blinding corneal disease characterized by the loss of function or deficiency in adult stem cells located at the junction between the cornea and the sclera (i.e., the limbus), namely the limbal stem cells (LSCs). Recent advances in in vivo imaging...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10613638/ https://www.ncbi.nlm.nih.gov/pubmed/37908848 http://dx.doi.org/10.3389/fmed.2023.1270570 |
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author | Gibson, David Tran, Thai Raveendran, Vidhur Bonnet, Clémence Siu, Nathan Vinet, Micah Stoddard-Bennett, Theo Arnold, Corey Deng, Sophie X. Speier, William |
author_facet | Gibson, David Tran, Thai Raveendran, Vidhur Bonnet, Clémence Siu, Nathan Vinet, Micah Stoddard-Bennett, Theo Arnold, Corey Deng, Sophie X. Speier, William |
author_sort | Gibson, David |
collection | PubMed |
description | INTRODUCTION: Limbal Stem Cell Deficiency (LSCD) is a blinding corneal disease characterized by the loss of function or deficiency in adult stem cells located at the junction between the cornea and the sclera (i.e., the limbus), namely the limbal stem cells (LSCs). Recent advances in in vivo imaging technology have improved disease diagnosis and staging to quantify several biomarkers of in vivo LSC function including epithelial thickness measured by anterior segment optical coherence tomography, and basal epithelial cell density and subbasal nerve plexus by in vivo confocal microscopy. A decrease in central corneal sub-basal nerve density and nerve fiber and branching number has been shown to correlate with the severity of the disease in parallel with increased nerve tortuosity. Yet, image acquisition and manual quantification require a high level of expertise and are time-consuming. Manual quantification presents inevitable interobserver variability. METHODS: The current study employs a novel deep learning approach to classify neuron morphology in various LSCD stages and healthy controls, by integrating images created through latent diffusion augmentation. The proposed model, a residual U-Net, is based in part on the InceptionResNetV2 transfer learning model. RESULTS: Deep learning was able to determine fiber number, branching, and fiber length with high accuracy (R2 of 0.63, 0.63, and 0.80, respectively). The model trained on images generated through latent diffusion on average outperformed the same model when trained on solely original images. The model was also able to detect LSCD with an AUC of 0.867, which showed slightly higher performance compared to classification using manually assessed metrics. DISCUSSION: The results suggest that utilizing latent diffusion to supplement training data may be effective in bolstering model performance. The results of the model emphasize the ability as well as the shortcomings of this novel deep learning approach to predict various nerve morphology metrics as well as LSCD disease severity. |
format | Online Article Text |
id | pubmed-10613638 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-106136382023-10-31 Latent diffusion augmentation enhances deep learning analysis of neuro-morphology in limbal stem cell deficiency Gibson, David Tran, Thai Raveendran, Vidhur Bonnet, Clémence Siu, Nathan Vinet, Micah Stoddard-Bennett, Theo Arnold, Corey Deng, Sophie X. Speier, William Front Med (Lausanne) Medicine INTRODUCTION: Limbal Stem Cell Deficiency (LSCD) is a blinding corneal disease characterized by the loss of function or deficiency in adult stem cells located at the junction between the cornea and the sclera (i.e., the limbus), namely the limbal stem cells (LSCs). Recent advances in in vivo imaging technology have improved disease diagnosis and staging to quantify several biomarkers of in vivo LSC function including epithelial thickness measured by anterior segment optical coherence tomography, and basal epithelial cell density and subbasal nerve plexus by in vivo confocal microscopy. A decrease in central corneal sub-basal nerve density and nerve fiber and branching number has been shown to correlate with the severity of the disease in parallel with increased nerve tortuosity. Yet, image acquisition and manual quantification require a high level of expertise and are time-consuming. Manual quantification presents inevitable interobserver variability. METHODS: The current study employs a novel deep learning approach to classify neuron morphology in various LSCD stages and healthy controls, by integrating images created through latent diffusion augmentation. The proposed model, a residual U-Net, is based in part on the InceptionResNetV2 transfer learning model. RESULTS: Deep learning was able to determine fiber number, branching, and fiber length with high accuracy (R2 of 0.63, 0.63, and 0.80, respectively). The model trained on images generated through latent diffusion on average outperformed the same model when trained on solely original images. The model was also able to detect LSCD with an AUC of 0.867, which showed slightly higher performance compared to classification using manually assessed metrics. DISCUSSION: The results suggest that utilizing latent diffusion to supplement training data may be effective in bolstering model performance. The results of the model emphasize the ability as well as the shortcomings of this novel deep learning approach to predict various nerve morphology metrics as well as LSCD disease severity. Frontiers Media S.A. 2023-10-16 /pmc/articles/PMC10613638/ /pubmed/37908848 http://dx.doi.org/10.3389/fmed.2023.1270570 Text en Copyright © 2023 Gibson, Tran, Raveendran, Bonnet, Siu, Vinet, Stoddard-Bennett, Arnold, Deng and Speier. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Medicine Gibson, David Tran, Thai Raveendran, Vidhur Bonnet, Clémence Siu, Nathan Vinet, Micah Stoddard-Bennett, Theo Arnold, Corey Deng, Sophie X. Speier, William Latent diffusion augmentation enhances deep learning analysis of neuro-morphology in limbal stem cell deficiency |
title | Latent diffusion augmentation enhances deep learning analysis of neuro-morphology in limbal stem cell deficiency |
title_full | Latent diffusion augmentation enhances deep learning analysis of neuro-morphology in limbal stem cell deficiency |
title_fullStr | Latent diffusion augmentation enhances deep learning analysis of neuro-morphology in limbal stem cell deficiency |
title_full_unstemmed | Latent diffusion augmentation enhances deep learning analysis of neuro-morphology in limbal stem cell deficiency |
title_short | Latent diffusion augmentation enhances deep learning analysis of neuro-morphology in limbal stem cell deficiency |
title_sort | latent diffusion augmentation enhances deep learning analysis of neuro-morphology in limbal stem cell deficiency |
topic | Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10613638/ https://www.ncbi.nlm.nih.gov/pubmed/37908848 http://dx.doi.org/10.3389/fmed.2023.1270570 |
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