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.

Machine learning‐based prediction of osteoporosis in postmenopausal women with clinical examined features: A quantitative clinical study

“…After the whole observation, 15 inputs were selected for females. These features included age, physical health, diabetes history, hypertension issues, functioning of the liver, parity, and protein in the blood, waist circumference, electrolytes, thyroid function, 21 lipid condition, obstetrics, and most importantly gynecological records, menopause status, and hormone replacement therapy (HRT) records as well. Having a prior history of diagnosis and a blood pressure reading of more than 140 mm were used to define diabetes and hypertension.…”