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Efficiency of Machine Learning Algorithms for the Determination of Macrovesicular Steatosis in Frozen Sections Stained with Sudan to Evaluate the Quality of the Graft in Liver Transplantation

Liver transplantation is the only curative treatment option in patients diagnosed with end-stage liver disease. The low availability of organs demands an accurate selection procedure based on histological analysis, in order to evaluate the allograft. This assessment, traditionally carried out by a p...

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Autores principales: Pérez-Sanz, Fernando, Riquelme-Pérez, Miriam, Martínez-Barba, Enrique, de la Peña-Moral, Jesús, Salazar Nicolás, Alejandro, Carpes-Ruiz, Marina, Esteban-Gil, Angel, Legaz-García, María Del Carmen, Parreño-González, María Antonia, Ramírez, Pablo, Martínez, Carlos M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8001362/
https://www.ncbi.nlm.nih.gov/pubmed/33808978
http://dx.doi.org/10.3390/s21061993
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author Pérez-Sanz, Fernando
Riquelme-Pérez, Miriam
Martínez-Barba, Enrique
de la Peña-Moral, Jesús
Salazar Nicolás, Alejandro
Carpes-Ruiz, Marina
Esteban-Gil, Angel
Legaz-García, María Del Carmen
Parreño-González, María Antonia
Ramírez, Pablo
Martínez, Carlos M.
author_facet Pérez-Sanz, Fernando
Riquelme-Pérez, Miriam
Martínez-Barba, Enrique
de la Peña-Moral, Jesús
Salazar Nicolás, Alejandro
Carpes-Ruiz, Marina
Esteban-Gil, Angel
Legaz-García, María Del Carmen
Parreño-González, María Antonia
Ramírez, Pablo
Martínez, Carlos M.
author_sort Pérez-Sanz, Fernando
collection PubMed
description Liver transplantation is the only curative treatment option in patients diagnosed with end-stage liver disease. The low availability of organs demands an accurate selection procedure based on histological analysis, in order to evaluate the allograft. This assessment, traditionally carried out by a pathologist, is not exempt from subjectivity. In this sense, new tools based on machine learning and artificial vision are continuously being developed for the analysis of medical images of different typologies. Accordingly, in this work, we develop a computer vision-based application for the fast and automatic objective quantification of macrovesicular steatosis in histopathological liver section slides stained with Sudan stain. For this purpose, digital microscopy images were used to obtain thousands of feature vectors based on the RGB and CIE L*a*b* pixel values. These vectors, under a supervised process, were labelled as fat vacuole or non-fat vacuole, and a set of classifiers based on different algorithms were trained, accordingly. The results obtained showed an overall high accuracy for all classifiers (>0.99) with a sensitivity between 0.844 and 1, together with a specificity >0.99. In relation to their speed when classifying images, KNN and Naïve Bayes were substantially faster than other classification algorithms. Sudan stain is a convenient technique for evaluating ME in pre-transplant liver biopsies, providing reliable contrast and facilitating fast and accurate quantification through the machine learning algorithms tested.
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spelling pubmed-80013622021-03-28 Efficiency of Machine Learning Algorithms for the Determination of Macrovesicular Steatosis in Frozen Sections Stained with Sudan to Evaluate the Quality of the Graft in Liver Transplantation Pérez-Sanz, Fernando Riquelme-Pérez, Miriam Martínez-Barba, Enrique de la Peña-Moral, Jesús Salazar Nicolás, Alejandro Carpes-Ruiz, Marina Esteban-Gil, Angel Legaz-García, María Del Carmen Parreño-González, María Antonia Ramírez, Pablo Martínez, Carlos M. Sensors (Basel) Article Liver transplantation is the only curative treatment option in patients diagnosed with end-stage liver disease. The low availability of organs demands an accurate selection procedure based on histological analysis, in order to evaluate the allograft. This assessment, traditionally carried out by a pathologist, is not exempt from subjectivity. In this sense, new tools based on machine learning and artificial vision are continuously being developed for the analysis of medical images of different typologies. Accordingly, in this work, we develop a computer vision-based application for the fast and automatic objective quantification of macrovesicular steatosis in histopathological liver section slides stained with Sudan stain. For this purpose, digital microscopy images were used to obtain thousands of feature vectors based on the RGB and CIE L*a*b* pixel values. These vectors, under a supervised process, were labelled as fat vacuole or non-fat vacuole, and a set of classifiers based on different algorithms were trained, accordingly. The results obtained showed an overall high accuracy for all classifiers (>0.99) with a sensitivity between 0.844 and 1, together with a specificity >0.99. In relation to their speed when classifying images, KNN and Naïve Bayes were substantially faster than other classification algorithms. Sudan stain is a convenient technique for evaluating ME in pre-transplant liver biopsies, providing reliable contrast and facilitating fast and accurate quantification through the machine learning algorithms tested. MDPI 2021-03-12 /pmc/articles/PMC8001362/ /pubmed/33808978 http://dx.doi.org/10.3390/s21061993 Text en © 2021 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Pérez-Sanz, Fernando
Riquelme-Pérez, Miriam
Martínez-Barba, Enrique
de la Peña-Moral, Jesús
Salazar Nicolás, Alejandro
Carpes-Ruiz, Marina
Esteban-Gil, Angel
Legaz-García, María Del Carmen
Parreño-González, María Antonia
Ramírez, Pablo
Martínez, Carlos M.
Efficiency of Machine Learning Algorithms for the Determination of Macrovesicular Steatosis in Frozen Sections Stained with Sudan to Evaluate the Quality of the Graft in Liver Transplantation
title Efficiency of Machine Learning Algorithms for the Determination of Macrovesicular Steatosis in Frozen Sections Stained with Sudan to Evaluate the Quality of the Graft in Liver Transplantation
title_full Efficiency of Machine Learning Algorithms for the Determination of Macrovesicular Steatosis in Frozen Sections Stained with Sudan to Evaluate the Quality of the Graft in Liver Transplantation
title_fullStr Efficiency of Machine Learning Algorithms for the Determination of Macrovesicular Steatosis in Frozen Sections Stained with Sudan to Evaluate the Quality of the Graft in Liver Transplantation
title_full_unstemmed Efficiency of Machine Learning Algorithms for the Determination of Macrovesicular Steatosis in Frozen Sections Stained with Sudan to Evaluate the Quality of the Graft in Liver Transplantation
title_short Efficiency of Machine Learning Algorithms for the Determination of Macrovesicular Steatosis in Frozen Sections Stained with Sudan to Evaluate the Quality of the Graft in Liver Transplantation
title_sort efficiency of machine learning algorithms for the determination of macrovesicular steatosis in frozen sections stained with sudan to evaluate the quality of the graft in liver transplantation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8001362/
https://www.ncbi.nlm.nih.gov/pubmed/33808978
http://dx.doi.org/10.3390/s21061993
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