首页|Autonomous University Barcelona (UAB) Reports Findings in Artificial Intelligenc e (Enhanced Artificial Intelligence Methods for Liver Steatosis Assessment Using Machine Learning and Color Image Processing: Liver Color Project)
Autonomous University Barcelona (UAB) Reports Findings in Artificial Intelligenc e (Enhanced Artificial Intelligence Methods for Liver Steatosis Assessment Using Machine Learning and Color Image Processing: Liver Color Project)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Artificial Intelligenc e is the subject of a report. According to news originating from Barcelona, Spai n, by NewsRx correspondents, research stated, “The use of livers with significan t steatosis is associated with worse transplantation outcomes. Brain death donor liver acceptance is mostly based on subjective surgeon assessment of liver appe arance, since steatotic livers acquire a yellowish tone.” Funders for this research include Fundacion Mutua Madrilena, Instituto de Salud Carlos III. Our news journalists obtained a quote from the research from Autonomous Universi ty Barcelona (UAB), “The aim of this study was to develop a rapid, robust, accur ate, and cost-effective method to assess liver steatosis. 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, a nd 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. A total of 192 livers (362 p hotographs and 7240 patches) were included. When setting a macrosteatosis thresh old of 30%, the best results were obtained using the random forest classifier, achieving an AUROC = 0.74, with 85% accuracy.”
BarcelonaSpainEuropeArtificial Int elligenceCyborgsEmerging TechnologiesHealth and MedicineHospitalsMachi ne LearningSteatosis
NETL
NSTL
