Automatic Liver Viability Scoring with Deep Learning and Hyperspectral Imaging.
CNNs
artificial intelligence
convolutional networks
deep learning
hepatic artery occlusion
hyperspectral imaging
liver viability
Journal
Diagnostics (Basel, Switzerland)
ISSN: 2075-4418
Titre abrégé: Diagnostics (Basel)
Pays: Switzerland
ID NLM: 101658402
Informations de publication
Date de publication:
24 Aug 2021
24 Aug 2021
Historique:
received:
18
06
2021
revised:
12
08
2021
accepted:
21
08
2021
entrez:
28
9
2021
pubmed:
29
9
2021
medline:
29
9
2021
Statut:
epublish
Résumé
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,
Identifiants
pubmed: 34573869
pii: diagnostics11091527
doi: 10.3390/diagnostics11091527
pmc: PMC8472457
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : Fondation ARC pour la Recherche sur le Cancer
ID : ELIOS
Organisme : Agence Nationale de la Recherche
ID : ANR-18-CE19-0026
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