LiverColor : An Artificial Intelligence Platform for Liver Graft Assessment
Piella, Gemma (Universitat Pompeu Fabra. Departament d'Enginyeria)
Farré, Nicolau (Universitat Pompeu Fabra. Departament d'Enginyeria)
Esono, Daniel (Universitat Pompeu Fabra. Departament d'Enginyeria)
Cordobés, Miguel Ángel (Universitat Pompeu Fabra. Departament d'Enginyeria)
Vázquez i Corral, Javier (Universitat Autònoma de Barcelona. Departament de Ciències de la Computació)
Bilbao, Itxarone (Hospital Universitari Vall d'Hebron)
Gómez Gavara, Concepción (Hospital Universitari Vall d'Hebron)

Date:	2024
Description:	15 pàg.
Abstract:	Hepatic steatosis, characterized by excess fat in the liver, is the main reason for discarding livers intended for transplantation due to its association with increased postoperative complications. The current gold standard for evaluating hepatic steatosis is liver biopsy, which, despite its accuracy, is invasive, costly, slow, and not always feasible during liver procurement. Consequently, surgeons often rely on subjective visual assessments based on the liver's colour and texture, which are prone to errors and heavily depend on the surgeon's experience. The aim of this study was to develop and validate a simple, rapid, and accurate method for detecting steatosis in donor livers to improve the decision-making process during liver procurement. We developed LiverColor, a co-designed software platform that integrates image analysis and machine learning to classify a liver graft into valid or non-valid according to its steatosis level. We utilized an in-house dataset of 192 cases to develop and validate the classification models. Colour and texture features were extracted from liver photographs, and graft classification was performed using supervised machine learning techniques (random forests and support vector machine). The performance of the algorithm was compared against biopsy results and surgeons' classifications. Usability was also assessed in simulated and real clinical settings using the Mobile Health App Usability Questionnaire. The predictive models demonstrated an area under the receiver operating characteristic curve of 0. 82, with an accuracy of 85%, significantly surpassing the accuracy of visual inspections by surgeons. Experienced surgeons rated the platform positively, appreciating not only the hepatic steatosis assessment but also the dashboarding functionalities for summarising and displaying procurement-related data. The results indicate that image analysis coupled with machine learning can effectively and safely identify valid livers during procurement. LiverColor has the potential to enhance the accuracy and efficiency of liver assessments, reducing the reliance on subjective visual inspections and improving transplantation outcomes.
Grants:	Agència de Gestió d'Ajuts Universitaris i de Recerca 2023/PROD-00061
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Colour and texture analysis ; Hepatic steatosis ; Liver assessment ; Mobile app ; Organ transplantation
Published in:	Diagnostics, Vol. 14, Issue 15 (July 2024) , art. 1654, ISSN 2075-4418

DOI: 10.3390/diagnostics14151654
PMID: 39125531

15 p, 5.2 MB

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