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Automatic Liver Viability Scoring with Deep Learning and Hyperspectral Imaging
Hyperspectral imaging (HSI) is a non-invasive imaging modality already applied to evaluate hepatic oxygenation and to discriminate different models of hepatic ischemia. Nevertheless, the ability of HSI to detect and predict the reperfusion damage intraoperatively was not yet assessed. Hypoxia caused...
Autores principales: | , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8472457/ https://www.ncbi.nlm.nih.gov/pubmed/34573869 http://dx.doi.org/10.3390/diagnostics11091527 |
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author | Felli, Eric Al-Taher, Mahdi Collins, Toby Nkusi, Richard Felli, Emanuele Baiocchini, Andrea Lindner, Veronique Vincent, Cindy Barberio, Manuel Geny, Bernard Ettorre, Giuseppe Maria Hostettler, Alexandre Mutter, Didier Gioux, Sylvain Schuster, Catherine Marescaux, Jacques Gracia-Sancho, Jordi Diana, Michele |
author_facet | Felli, Eric Al-Taher, Mahdi Collins, Toby Nkusi, Richard Felli, Emanuele Baiocchini, Andrea Lindner, Veronique Vincent, Cindy Barberio, Manuel Geny, Bernard Ettorre, Giuseppe Maria Hostettler, Alexandre Mutter, Didier Gioux, Sylvain Schuster, Catherine Marescaux, Jacques Gracia-Sancho, Jordi Diana, Michele |
author_sort | Felli, Eric |
collection | PubMed |
description | Hyperspectral imaging (HSI) is a non-invasive imaging modality already applied to evaluate hepatic oxygenation and to discriminate different models of hepatic ischemia. Nevertheless, the ability of HSI to detect and predict the reperfusion damage intraoperatively was not yet assessed. Hypoxia caused by hepatic artery occlusion (HAO) in the liver brings about dreadful vascular complications known as ischemia-reperfusion injury (IRI). Here, we show the evaluation of liver viability in an HAO model with an artificial intelligence-based analysis of HSI. We have combined the potential of HSI to extract quantitative optical tissue properties with a deep learning-based model using convolutional neural networks. The artificial intelligence (AI) score of liver viability showed a significant correlation with capillary lactate from the liver surface (r = −0.78, p = 0.0320) and Suzuki’s score (r = −0.96, p = 0.0012). CD31 immunostaining confirmed the microvascular damage accordingly with the AI score. Our results ultimately show the potential of an HSI-AI-based analysis to predict liver viability, thereby prompting for intraoperative tool development to explore its application in a clinical setting. |
format | Online Article Text |
id | pubmed-8472457 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-84724572021-09-28 Automatic Liver Viability Scoring with Deep Learning and Hyperspectral Imaging Felli, Eric Al-Taher, Mahdi Collins, Toby Nkusi, Richard Felli, Emanuele Baiocchini, Andrea Lindner, Veronique Vincent, Cindy Barberio, Manuel Geny, Bernard Ettorre, Giuseppe Maria Hostettler, Alexandre Mutter, Didier Gioux, Sylvain Schuster, Catherine Marescaux, Jacques Gracia-Sancho, Jordi Diana, Michele Diagnostics (Basel) Article Hyperspectral imaging (HSI) is a non-invasive imaging modality already applied to evaluate hepatic oxygenation and to discriminate different models of hepatic ischemia. Nevertheless, the ability of HSI to detect and predict the reperfusion damage intraoperatively was not yet assessed. Hypoxia caused by hepatic artery occlusion (HAO) in the liver brings about dreadful vascular complications known as ischemia-reperfusion injury (IRI). Here, we show the evaluation of liver viability in an HAO model with an artificial intelligence-based analysis of HSI. We have combined the potential of HSI to extract quantitative optical tissue properties with a deep learning-based model using convolutional neural networks. The artificial intelligence (AI) score of liver viability showed a significant correlation with capillary lactate from the liver surface (r = −0.78, p = 0.0320) and Suzuki’s score (r = −0.96, p = 0.0012). CD31 immunostaining confirmed the microvascular damage accordingly with the AI score. Our results ultimately show the potential of an HSI-AI-based analysis to predict liver viability, thereby prompting for intraoperative tool development to explore its application in a clinical setting. MDPI 2021-08-24 /pmc/articles/PMC8472457/ /pubmed/34573869 http://dx.doi.org/10.3390/diagnostics11091527 Text en © 2021 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 | Article Felli, Eric Al-Taher, Mahdi Collins, Toby Nkusi, Richard Felli, Emanuele Baiocchini, Andrea Lindner, Veronique Vincent, Cindy Barberio, Manuel Geny, Bernard Ettorre, Giuseppe Maria Hostettler, Alexandre Mutter, Didier Gioux, Sylvain Schuster, Catherine Marescaux, Jacques Gracia-Sancho, Jordi Diana, Michele Automatic Liver Viability Scoring with Deep Learning and Hyperspectral Imaging |
title | Automatic Liver Viability Scoring with Deep Learning and Hyperspectral Imaging |
title_full | Automatic Liver Viability Scoring with Deep Learning and Hyperspectral Imaging |
title_fullStr | Automatic Liver Viability Scoring with Deep Learning and Hyperspectral Imaging |
title_full_unstemmed | Automatic Liver Viability Scoring with Deep Learning and Hyperspectral Imaging |
title_short | Automatic Liver Viability Scoring with Deep Learning and Hyperspectral Imaging |
title_sort | automatic liver viability scoring with deep learning and hyperspectral imaging |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8472457/ https://www.ncbi.nlm.nih.gov/pubmed/34573869 http://dx.doi.org/10.3390/diagnostics11091527 |
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