Machine Learning and Radiomics for Osteoporosis Risk Prediction Using X-ray Imaging
Machine Learning and Radiomics for Osteoporosis Risk Prediction Using X-ray Imaging
Abstract Osteoporosis is a significant health and economic issue, as it predisposes patients to a higher risk of bone fracture. Measuring bone mineral density has been shown to be an accurate way to assess the risk for osteoporosis. The most common way for bone density testing is a dual-energy X-ray absorptiometry (DEXA) scan, which may be recommended for patients with increased risk of osteoporosis. Radiograph imaging is widely available in clinical settings and acquired for many reasons, such as trauma or pain. The goal of this project is to extract radiomics information from pelvic X-rays (both the hip and femoral neck regions) to assess the risk of osteoporosis (triaging patients into “normal” vs. “at-risk”, or “low risk” vs. “high risk” categories). The motivation here is not to replace the DEXA scan but to proactively identify patients at risk for osteoporosis and appropriately refer them to management options. We apply machine learning-based radiomics techniques on a study cohort of 565 patients. Our preliminary results show that a correlation between the radiomics features extracted from pelvic X-rays and the level of osteoporosis risk derived from the DEXA test results.
Wu Shandong、Yang Lu、Clancy Kadie、Kitamura Gene、Dadsetan Saba、Arefan Dooman、Guo Yuan
Intelligent Systems Program, School of Computing and Information, University of Pittsburgh||Department of Radiology, School of Medicine, University of Pittsburgh||Department of Biomedical Informatics and Department of Bioengineering, University of PittsburghDepartment of Radiology, School of Medicine, University of Pittsburgh||Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer HospitalComputer Science Department, School of Computing and Information, University of PittsburghDepartment of Radiology, School of Medicine, University of PittsburghIntelligent Systems Program, School of Computing and Information, University of PittsburghDepartment of Radiology, School of Medicine, University of PittsburghDepartment of Radiology, School of Medicine, University of Pittsburgh||Department of Radiology, Guangzhou First People?ˉs Hospital
医学研究方法基础医学临床医学
machine learningradiomicsDEXAosteoporosisrisk prediction
Wu Shandong,Yang Lu,Clancy Kadie,Kitamura Gene,Dadsetan Saba,Arefan Dooman,Guo Yuan.Machine Learning and Radiomics for Osteoporosis Risk Prediction Using X-ray Imaging[EB/OL].(2025-03-28)[2025-08-11].https://www.medrxiv.org/content/10.1101/2022.02.03.22270400.点此复制
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