Concepción Gómez-Gavara, Itxarone Bilbao, Gemma Piella, Javier Vazquez-Corral, Berta Benet-Cugat, Elizabeth Pando, José Andrés Molino, María Teresa Salcedo, Mar Dalmau, Laura Vidal, Daniel Esono, Miguel Ángel Cordobés, Ángela Bilbao, Josa Prats, Mar Moya, Cristina Dopazo, Christopher Mazo, Mireia Caralt, Ernest Hidalgo, Ramon Charco
{"title":"利用机器学习和彩色图像处理增强肝脏脂肪变性评估的人工智能方法：肝脏颜色项目。","authors":"Concepción Gómez-Gavara, Itxarone Bilbao, Gemma Piella, Javier Vazquez-Corral, Berta Benet-Cugat, Elizabeth Pando, José Andrés Molino, María Teresa Salcedo, Mar Dalmau, Laura Vidal, Daniel Esono, Miguel Ángel Cordobés, Ángela Bilbao, Josa Prats, Mar Moya, Cristina Dopazo, Christopher Mazo, Mireia Caralt, Ernest Hidalgo, Ramon Charco","doi":"10.1111/ctr.15465","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>Machine learning coupled with liver texture and color analysis of photographs taken with smartphones provides excellent accuracy for determining liver steatosis.</p>\n </section>\n </div>","PeriodicalId":10467,"journal":{"name":"Clinical Transplantation","volume":"38 10","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/ctr.15465","citationCount":"0","resultStr":"{\"title\":\"Enhanced Artificial Intelligence Methods for Liver Steatosis Assessment Using Machine Learning and Color Image Processing: Liver Color Project\",\"authors\":\"Concepción Gómez-Gavara, Itxarone Bilbao, Gemma Piella, Javier Vazquez-Corral, Berta Benet-Cugat, Elizabeth Pando, José Andrés Molino, María Teresa Salcedo, Mar Dalmau, Laura Vidal, Daniel Esono, Miguel Ángel Cordobés, Ángela Bilbao, Josa Prats, Mar Moya, Cristina Dopazo, Christopher Mazo, Mireia Caralt, Ernest Hidalgo, Ramon Charco\",\"doi\":\"10.1111/ctr.15465\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>The use of livers with significant steatosis is associated with worse transplantation outcomes. 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Enhanced Artificial Intelligence Methods for Liver Steatosis Assessment Using Machine Learning and Color Image Processing: Liver Color Project
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.
期刊介绍：
Clinical Transplantation: The Journal of Clinical and Translational Research aims to serve as a channel of rapid communication for all those involved in the care of patients who require, or have had, organ or tissue transplants, including: kidney, intestine, liver, pancreas, islets, heart, heart valves, lung, bone marrow, cornea, skin, bone, and cartilage, viable or stored.
Published monthly, Clinical Transplantation’s scope is focused on the complete spectrum of present transplant therapies, as well as also those that are experimental or may become possible in future. Topics include:
Immunology and immunosuppression;
Patient preparation;
Social, ethical, and psychological issues;
Complications, short- and long-term results;
Artificial organs;
Donation and preservation of organ and tissue;
Translational studies;
Advances in tissue typing;
Updates on transplant pathology;.
Clinical and translational studies are particularly welcome, as well as focused reviews. Full-length papers and short communications are invited. Clinical reviews are encouraged, as well as seminal papers in basic science which might lead to immediate clinical application. Prominence is regularly given to the results of cooperative surveys conducted by the organ and tissue transplant registries.
Clinical Transplantation: The Journal of Clinical and Translational Research is essential reading for clinicians and researchers in the diverse field of transplantation: surgeons; clinical immunologists; cryobiologists; hematologists; gastroenterologists; hepatologists; pulmonologists; nephrologists; cardiologists; and endocrinologists. It will also be of interest to sociologists, psychologists, research workers, and to all health professionals whose combined efforts will improve the prognosis of transplant recipients.
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