[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126408-en":3,"doc-seo-126408-105":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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":11,"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},126408,962085571259,"Theodora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Enhanced Artificial Intelligence Methods for Liver Steatosis Assessment Using Machine Learning and Color Image Processing - Liver Color Project","Brain death donor liver acceptance often relies on subjective surgeon assessment of liver appearance, because steatotic livers develop a yellowish tone. The study develops a rapid, robust, accurate, and cost-effective method to assess liver steatosis using smartphone photographs. Color-calibrated, segmented images are processed into patches, with color and texture features fed into machine learning. Results are evaluated against a macrosteatosis threshold, showing strong diagnostic performance.","Clinical Transplantation  \nORIGINAL ARTICLE   \nEnhanced Artificial Intelligence Methods for Liver Steatosis Assessment Using Machine Learning and Color Image Processing: Liver Color Project  \nConcepción Gómez-Gavara1, 2   Itxarone Bilbao1, 2   Gemma Piella3  Javier Vazquez-Corral4  Berta Benet-Cugat3 Elizabeth Pando1, 2  José Andrés Molino5  María Teresa Salcedo6  Mar Dalmau1, 2  Laura Vidal1, 2  Daniel Esono3 Miguel Ángel Cordobés3  Ángela Bilbao1  Josa Prats3  Mar Moya3  Cristina Dopazo1, 2  Christopher Mazo7  Mireia Caralt2  Ernest Hidalgo2  Ramon Charco2  \n1 Barcelona Autonoma University, Universitat Autónoma de Barcelona, Barcelona, Spain  2 Servicio de Cirugía HBP y Trasplante, Hospital Universitari Valld´Hebron, Vall d´Hebron Institute of Research (VHIR), Barcelona, Spain  3 Barcelona MedTech, Universidad Pompeu Fabra, Barcelona, Spain  4 Computer Vision Center and Computer Sciences Department, Universitat Autònoma de Barcelona, Barcelona, Spain  5 Servicio de Cirugía Pediátrica, Hospital Universitari Vall d´Hebron, Barcelona, Spain  6 Servicio de Anatomía Patológica, Hospital Universitari Vall d´Hebron, Barcelona, Spain  7 Coordinación de Trasplantes, Hospital Universitari Vall d´Hebron, Barcelona, Spain  \nCorrespondence: Concepción Gómez-Gavara ([concepcion.gomez@uab.cat](concepcion.gomez@uab.cat))  \nReceived: 25 January 2024  Revised: 2 August 2024  Accepted: 8 September 2024  \nFunding: 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.  \nABSTRACT  \nBackground: 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.  \nMethods: 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.  \nResults: 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.  \nConclusion: Machine learning coupled with liver texture and color analysis of photographs taken with smartphones provides excellent accuracy for determining liver steatosis.  \n\n| Abbreviations: ALT, serum glutamatepyruvate transaminase; AST, serum glutamic oxaloacetic transaminase; GGT, gamma-glutamyl transaminase; HBP, hepatobiliary pancreatic; LBP, local binary pattern; LT, liver transplantation; MASLD, methabolic-dysfunction-associated steatotic liver disease; MS, macrosteatosis; RGB, red, green and blue; SD, standard deviation; SVMs, support vector machines.\u003Cbr>Concepción Gómez-Gavara and Itxarone Bilbao are first co-authors.\u003Cbr> |\n| --- |\n| This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDe","cbCaikuO966HNLGf","https://ap.wps.com/l/cbCaikuO966HNLGf","pdf",886092,2,1,"English","en",105,"# Abstract\n## Background\n## Methods\n## Results\n## Conclusion\n# Introduction","[{\"question\":\"Why is liver steatosis assessment important in transplantation?\",\"answer\":\"Significant steatosis is linked to poorer transplantation outcomes. It also affects acceptance decisions for brain-death donor livers, which are often based on visual appearance.\"},{\"question\":\"What data and image-processing steps were used in the method?\",\"answer\":\"The study used smartphone photographs and tru-cut needle biopsies. Photographs were color calibrated, segmented, divided into patches, and then color and texture features were extracted for machine learning.\"},{\"question\":\"Which machine learning model performed best at the 30% macrosteatosis threshold?\",\"answer\":\"Using a random forest classifier, the study reports AUROC = 0.74 and 85% accuracy at a 30% macrosteatosis threshold.\"}]","Enhanced Artificial Intelligence Methods for Liver Steatosis Assessment Using Machine Learning and Color Image Processing - Liver Color Project | PDF",1785904909,20,{"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},"enhanced-artificial-intelligence-methods-for-liver-steatosis-assessment-using-machine-learning-and-color-image-processing-liver-color-project","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":20},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/enhanced-artificial-intelligence-methods-for-liver-steatosis-assessment-using-machine-learning-and-color-image-processing-liver-color-project/126408/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-19","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is liver steatosis assessment important in transplantation?","Question",{"text":75,"@type":76},"Significant steatosis is linked to poorer transplantation outcomes. It also affects acceptance decisions for brain-death donor livers, which are often based on visual appearance.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and image-processing steps were used in the method?",{"text":80,"@type":76},"The study used smartphone photographs and tru-cut needle biopsies. Photographs were color calibrated, segmented, divided into patches, and then color and texture features were extracted for machine learning.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model performed best at the 30% macrosteatosis threshold?",{"text":84,"@type":76},"Using a random forest classifier, the study reports AUROC = 0.74 and 85% accuracy at a 30% macrosteatosis threshold.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":29,"slug":126},9,"Religion & Spirituality","religion-spirituality",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":29,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]