首页|Guangzhou Medical University Reports Findings in Machine Learning (CT-based radi omics of machine-learning to screen high-risk individuals with kidney stones)
Guangzhou Medical University Reports Findings in Machine Learning (CT-based radi omics of machine-learning to screen high-risk individuals with kidney stones)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting from Guangdong, People’s Repu blic of China, by NewsRx journalists, research stated, “Screening high-risk popu lations is crucial for the prevention and treatment of kidney stones. Here, we e mployed radiomics to screen high-risk patients for kidney stones.” The news correspondents obtained a quote from the research from Guangzhou Medica l University, “A total of 513 independent kidneys from our hospital between 2020 and 2022 were randomly allocated to training and validation sets at a 7:3 ratio . Radiomic features were extracted using 3Dslicer software. The least absolute s hrinkage and selection operator (LASSO) method was used to select radiomic featu res from the 107 extracted features, and logistic regression, decision tree, Ada Boost, and support vector machine (SVM) models were subsequently used to constru ct radiomic feature prediction models. Among these, the logistic regression algo rithm demonstrated the best predictive performance and stability. The area under the curve (AUC) of the logistic regression model based on radiomic features was 0.858 in the training cohort and 0.806 in the validation cohort. Furthermore, u nivariate and multivariate logistic regression analyses were performed to identi fy the independent risk factors for kidney stones, which were gender and body ma ss index (BMI). Combining these independent risk factors improved the predictive performance of the model, with AUC values of 0.860 in the training cohort and 0 .814 in the validation cohort. Clinical decision curve analysis (DCA) indicated that the radiomic model provided clinical benefit when the probability ranged fr om 0.2 to 1.0.”
GuangdongPeople's Republic of ChinaA siaCyborgsEmerging TechnologiesMachine LearningRisk and Prevention
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