Enhanced Artificial Intelligence Methods for Liver Steatosis Assessment Using Machine Learning and Color Image Processing: Liver Color Project

Abstract

Artificial intelligence; Liver steatosis; Liver color

Intel·ligència artificial; Esteatosi hepàtica; Color del fetge

Inteligencia artificial; Esteatosis hepática; Color del hígado

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.

The project that gave rise to these results has received funding from Research support by “Fundación Mutua Madrileña,” “Instituto de Salud Carlos III” and Fondos FEDER. Somos Europa, “La Caixa” Foundation and the European Institute of Innovation and Technology, EIT (body of the European Union that receives support from the European Union's Horizon 2020 research and innovation programme), under the grant agreement CI21-00064. It has also been funded by UPF INNOValora programme, which is co-financed by the Generalitat de Catalunya and the European Regional Development Fund. G. Piella was supported by ICREA Academia.