Pulled from PubMed
Concepción Gómez-Gavara 1 2, Itxarone Bilbao 1 2, Gemma Piella 3, Javier Vazquez-Corral 4, Berta Benet-Cugat 3, Elizabeth Pando 1 2, José Andrés Molino 5, María Teresa Salcedo 6, Mar Dalmau 1 2, Laura Vidal 1 2, Daniel Esono 3, Miguel Ángel Cordobés 3, Ángela Bilbao 1, Josa Prats 3, Mar Moya 3, Cristina Dopazo 1 2, Christopher Mazo 7, Mireia Caralt 2, Ernest Hidalgo 2, Ramon Charco 2
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.