Enhanced Artificial Intelligence Methods for Liver Steatosis Assessment Using Machine Learning and Color Image Processing: Liver Color Project.
Journal
Clinical transplantation
ISSN: 1399-0012
Titre abrégé: Clin Transplant
Pays: Denmark
ID NLM: 8710240
Informations de publication
Date de publication:
Oct 2024
Oct 2024
Historique:
revised:
02
08
2024
received:
25
01
2024
accepted:
08
09
2024
medline:
9
10
2024
pubmed:
9
10
2024
entrez:
9
10
2024
Statut:
ppublish
Résumé
The use of livers with significant steatosis is associated with worse transplantation outcomes. Brain death donor liver acceptance is mostly based on subjective surgeon assessment of liver appearance, since steatotic livers acquire a yellowish tone. The aim of this study was to develop a rapid, robust, accurate, and cost-effective method to assess liver steatosis. From June 1, 2018, to November 30, 2023, photographs and tru-cut needle biopsies were taken from adult brain death donor livers at a single university hospital for the study. All the liver photographs were taken by smartphones then color calibrated, segmented, and divided into patches. Color and texture features were then extracted and used as input, and the machine learning method was applied. This is a collaborative project between Vall d'Hebron University Hospital and Barcelona MedTech, Pompeu Fabra University, and is referred to as LiverColor. A total of 192 livers (362 photographs and 7240 patches) were included. When setting a macrosteatosis threshold of 30%, the best results were obtained using the random forest classifier, achieving an AUROC = 0.74, with 85% accuracy. Machine learning coupled with liver texture and color analysis of photographs taken with smartphones provides excellent accuracy for determining liver steatosis.
Sections du résumé
BACKGROUND
BACKGROUND
The use of livers with significant steatosis is associated with worse transplantation outcomes. Brain death donor liver acceptance is mostly based on subjective surgeon assessment of liver appearance, since steatotic livers acquire a yellowish tone. The aim of this study was to develop a rapid, robust, accurate, and cost-effective method to assess liver steatosis.
METHODS
METHODS
From June 1, 2018, to November 30, 2023, photographs and tru-cut needle biopsies were taken from adult brain death donor livers at a single university hospital for the study. All the liver photographs were taken by smartphones then color calibrated, segmented, and divided into patches. Color and texture features were then extracted and used as input, and the machine learning method was applied. This is a collaborative project between Vall d'Hebron University Hospital and Barcelona MedTech, Pompeu Fabra University, and is referred to as LiverColor.
RESULTS
RESULTS
A total of 192 livers (362 photographs and 7240 patches) were included. When setting a macrosteatosis threshold of 30%, the best results were obtained using the random forest classifier, achieving an AUROC = 0.74, with 85% accuracy.
CONCLUSION
CONCLUSIONS
Machine learning coupled with liver texture and color analysis of photographs taken with smartphones provides excellent accuracy for determining liver steatosis.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e15465Subventions
Organisme : Fundación Mutua Madrileña
Organisme : Instituto de Salud Carlos III
Organisme : "La Caixa" Foundation
Organisme : European Union's Horizon 2020 research and innovation programme
ID : CI21-00064
Organisme : UPF INNOValora programme
Informations de copyright
© 2024 The Author(s). Clinical Transplantation published by Wiley Periodicals LLC.
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