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| Format: | Artículo Open Access |
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Wiley
2024
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| Online-Zugang: | https://onlinelibrary.wiley.com/doi/10.1111/ctr.15465 |
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| author | Concepción Gómez‐Gavara Itxarone Bilbao Gemma Piella Javier Vazquez‐Corral Berta Benet‐Cugat Elizabeth Pando José Andrés Molino María Teresa Salcedo Mar Dalmau Laura Vidal Daniel Esono Miguel Ángel Cordobés Ángela Bilbao Josa Prats Mar Moya Cristina Dopazo Christopher Mazo Mireia Caralt Ernest Hidalgo Ramon Charco |
| author_facet | Concepción Gómez‐Gavara Itxarone Bilbao Gemma Piella Javier Vazquez‐Corral Berta Benet‐Cugat Elizabeth Pando José Andrés Molino María Teresa Salcedo Mar Dalmau Laura Vidal Daniel Esono Miguel Ángel Cordobés Ángela Bilbao Josa Prats Mar Moya Cristina Dopazo Christopher Mazo Mireia Caralt Ernest Hidalgo Ramon Charco Concepción Gómez‐Gavara Itxarone Bilbao Gemma Piella Javier Vazquez‐Corral Berta Benet‐Cugat Elizabeth Pando José Andrés Molino María Teresa Salcedo Mar Dalmau Laura Vidal Daniel Esono Miguel Ángel Cordobés Ángela Bilbao Josa Prats Mar Moya Cristina Dopazo Christopher Mazo Mireia Caralt Ernest Hidalgo Ramon Charco |
| collection | Wiley Open Access |
| contents | Enhanced Artificial Intelligence Methods for Liver Steatosis Assessment Using Machine Learning and Color Image Processing: Liver Color Project Concepción Gómez‐Gavara Itxarone Bilbao Gemma Piella Javier Vazquez‐Corral Berta Benet‐Cugat Elizabeth Pando José Andrés Molino María Teresa Salcedo Mar Dalmau Laura Vidal Daniel Esono Miguel Ángel Cordobés Ángela Bilbao Josa Prats Mar Moya Cristina Dopazo Christopher Mazo Mireia Caralt Ernest Hidalgo Ramon Charco Clinical Transplantation ABSTRACTBackgroundThe 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.MethodsFrom 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.ResultsA 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.ConclusionMachine learning coupled with liver texture and color analysis of photographs taken with smartphones provides excellent accuracy for determining liver steatosis. 10.1111/ctr.15465 http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| doi_str_mv | 10.1111/ctr.15465 |
| format | Artículo Open Access |
| id | wiley_oa_10_1111_ctr_15465 |
| institution | Wiley Open Access |
| license_str_mv | http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| publishDate | 2024 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Enhanced Artificial Intelligence Methods for Liver Steatosis Assessment Using Machine Learning and Color Image Processing: Liver Color Project Concepción Gómez‐Gavara Itxarone Bilbao Gemma Piella Javier Vazquez‐Corral Berta Benet‐Cugat Elizabeth Pando José Andrés Molino María Teresa Salcedo Mar Dalmau Laura Vidal Daniel Esono Miguel Ángel Cordobés Ángela Bilbao Josa Prats Mar Moya Cristina Dopazo Christopher Mazo Mireia Caralt Ernest Hidalgo Ramon Charco Clinical Transplantation Enhanced Artificial Intelligence Methods for Liver Steatosis Assessment Using Machine Learning and Color Image Processing: Liver Color Project Concepción Gómez‐Gavara Itxarone Bilbao Gemma Piella Javier Vazquez‐Corral Berta Benet‐Cugat Elizabeth Pando José Andrés Molino María Teresa Salcedo Mar Dalmau Laura Vidal Daniel Esono Miguel Ángel Cordobés Ángela Bilbao Josa Prats Mar Moya Cristina Dopazo Christopher Mazo Mireia Caralt Ernest Hidalgo Ramon Charco Clinical Transplantation ABSTRACTBackgroundThe 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.MethodsFrom 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.ResultsA 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.ConclusionMachine learning coupled with liver texture and color analysis of photographs taken with smartphones provides excellent accuracy for determining liver steatosis. 10.1111/ctr.15465 http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| title | Enhanced Artificial Intelligence Methods for Liver Steatosis Assessment Using Machine Learning and Color Image Processing: Liver Color Project |
| topic | Clinical Transplantation |
| url | https://onlinelibrary.wiley.com/doi/10.1111/ctr.15465 |