| Title: | Machine Learning Analysis for prediction of Osteoporosis using Multimodal Data |
| Lead Institution: | University of Nevada Las Vegas |
| Principal investigator: | Dr Qing Wu |
Application 58122
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About
Bone fracture due to osteoporosis is an enormous health issue as millions of people over age of 50 around the world are affected. When Bone Mineral Density decreases, the bone becomes porous and fragile. It leads to bone fracture and severe health complications. Research studies have linked various genetic factors to osteoporosis. DXA imaging technique is common and useful to measure BMD and diagnose osteoporosis. Researches are going on to process the images effectively to extract features from the DXA images and use those features to diagnose osteoporosis and osteoporotic fracture. Results alone from DXA scan are not enough to precisely diagnose the disease. There could be various genomic and conventional factors behind osteoporosis. So, we need a robust model that studies genomic, phenotype and image data together to predict osteoporosis. We aim to build a model that will use genotype, phenotype and DXA image data. Using machine learning algorithms on genotype and phenotype data, we can identify people with risk of osteoporosis. The algorithms will generate results which contain genetic factors, phenotype factors behind osteoporosis. The model built by using DXA images of the patients can be used to calculate BMD and predict osteoporosis which will produce efficient results combined with results from genomic analysis. The results will help people get early and proper treatment The research project will continue over a span of 5 years where various machine learning techniques, new algorithms will be tested, and the model will be built and experimented to produce accurate results. Various data pre-processing techniques will be studied and experimented to produce efficient model. Early diagnosis and treatment of osteoporosis can provide early treatment and avoid life-threatening complications for people with low BMD. This research study aims to identify people with risk of osteoporosis. This can save lives as well as save millions of dollars on post osteoporosis treatment.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 10946 | The clinical utility of the BMD-related comprehensive genome-wide polygenic score in identifying individuals with a high risk of osteoporotic fractures | Xiangxue Xiao (+1) | 2023 | Osteoporosis International |
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