[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118238-en":3,"doc-seo-118238-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118238,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",7,"Healthcare","LiverColor - An Artificial Intelligence Platform for Liver Graft Assessment","Hepatic steatosis, defined by excess fat in the liver, is the leading cause for discarding donor livers because it is linked to higher postoperative complications. While liver biopsy remains the gold standard, it is invasive, costly, slow, and often impractical during procurement, and it may suffer sampling bias due to uneven fat distribution. This study develops and validates LiverColor, a co-designed software platform combining image analysis and supervised machine learning to classify liver grafts as valid or non-valid. Using 192 cases, models based on color and texture features achieved AUROC 0.82 and 85% accuracy, outperforming surgeons’ visual assessments.","diagnostics  \nArticle  \nLiverColor: An Artificial Intelligence Platform for Liver Graft Assessment  \nGemma Piella 1, *, Nicolau Farré 1, Daniel Esono 1, Miguel Ángel Cordobés 1, Javier Vázquez-Corral 2, Itxarone Bilbao 3 and Concepción Gómez-Gavara 3  \nCitation: Piella, G.; Farré, N.; Esono, D.; Cordobés, M.Á .; Vázquez-Corral, J.; Bilbao, I.; Gómez-Gavara, C. LiverColor: An Artificial Intelligence Platform for Liver Graft Assessment. Diagnostics 2024, 14, 1654. [https://](https://)[ ](https://)[doi.org/10.3390/diagnostics14151654](doi.org/10.3390/diagnostics14151654)  \nAcademic Editor: Dania Cioni  \nReceived: 30 June 2024  \nRevised: 29 July 2024  \nAccepted: 30 July 2024  \nPublished: 31 July 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Engineering Department, Universitat Pompeu Fabra, 08018 Barcelona, Spain; [miguelangel.cordobes@upf.edu](miguelangel.cordobes@upf.edu) (M.Á .C.)  \n2 Computer Vision Center and Computer Sciences Department, Universitat Autònoma de Barcelona, 08193 Barcelona, Spain; [jvazquez@cvc.uab.cat](jvazquez@cvc.uab.cat)  \n3 Servicio de Cirugía HBP y Trasplante, Hospital Universitari Vall d’Hebron, Vall d’Hebron Institute of Research (VHIR), 08035 Barcelona, Spain; [concepcion.gomez@vhebron.cat](concepcion.gomez@vhebron.cat) (C.G.-G.)  \n* Correspondence: [gemma.piella@upf.edu](gemma.piella@upf.edu)  \nAbstract: 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.  \nKeywords: mobile app; colour and texture analysis; liver assessment; organ transplantation; hepatic steatosis  \n1. Introduction  \nH","cbCaihBIf34iPAU5","https://ap.wps.com/l/cbCaihBIf34iPAU5","pdf",5420507,1,15,"English","en",105,"# Introduction\n## Hepatic steatosis and the need for reliable assessment\n# LiverColor platform and study design\n## Dataset and feature extraction\n## Machine learning models and evaluation","[{\"question\":\"Why is hepatic steatosis a critical factor in liver transplantation decisions?\",\"answer\":\"Hepatic steatosis increases the risk of postoperative complications and can make donor livers more vulnerable to preservation damage, raising the likelihood of early allograft dysfunction.\"},{\"question\":\"What limitation does liver biopsy have during liver procurement?\",\"answer\":\"Liver biopsy is invasive, costly, and slow, and it is not always feasible during procurement. It also samples only a small part of the liver, which can introduce sampling error when steatosis is unevenly distributed.\"},{\"question\":\"How does LiverColor improve assessment compared with visual inspection?\",\"answer\":\"LiverColor uses color and texture features from liver photographs combined with supervised machine learning to classify grafts. The study reports AUROC 0.82 and 85% accuracy, significantly higher than surgeons’ visual inspections.\"}]","LiverColor - An Artificial Intelligence Platform for Liver Graft Assessment | PDF",1785682567,38,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"livercolor-an-artificial-intelligence-platform-for-liver-graft-assessment","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/livercolor-an-artificial-intelligence-platform-for-liver-graft-assessment/118238/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is hepatic steatosis a critical factor in liver transplantation decisions?","Question",{"text":75,"@type":76},"Hepatic steatosis increases the risk of postoperative complications and can make donor livers more vulnerable to preservation damage, raising the likelihood of early allograft dysfunction.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What limitation does liver biopsy have during liver procurement?",{"text":80,"@type":76},"Liver biopsy is invasive, costly, and slow, and it is not always feasible during procurement. It also samples only a small part of the liver, which can introduce sampling error when steatosis is unevenly distributed.",{"name":82,"@type":73,"acceptedAnswer":83},"How does LiverColor improve assessment compared with visual inspection?",{"text":84,"@type":76},"LiverColor uses color and texture features from liver photographs combined with supervised machine learning to classify grafts. 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