Enhanced artificial intelligence methods for liver steatosis assessment using machine learning and color image processing: liver color project

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...

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Detalles Bibliográficos
Autores: Gómez-Gavara, Concepción, Bilbao, Itxarone, Piella Fenoy, Gemma, Vázquez-Corral, Javier, Benet-Cugat, Berta, Pando, Elizabeth, Molino, José Andrés, Salcedo, María Teresa, Dalmau, Mar, Vidal, Laura, Esono, Daniel, Cordobés, Miguel Ángel, Bilbao, Ángela, Prats-Valero, Josa, Moya, Mar, Dopazo, Cristina, Mazo, Christopher, Caralt, Mireia, Hidalgo, Ernest, Charco, Ramon
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Universitat Pompeu Fabra
Repositorio:Repositorio Digital de la UPF
OAI Identifier:oai:dnet:rdupf_______::2fe542b11fb787c55e28c738f247fb5e
Acceso en línea:https://hdl.handle.net/10230/72921
http://dx.doi.org/10.1111/ctr.15465
Access Level:acceso abierto
Palabra clave:Intel·ligència artificial
Aprenentatge automàtic
Cervell
Descripción
Sumario: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: 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: 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: Machine learning coupled with liver texture and color analysis of photographs taken with smartphones provides excellent accuracy for determining liver steatosis.

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