Novel biomechanical test setup

• Project number: SC16_009
• Project management: Dieter Pahr, Karl Landsteiner University for Health Sciences / Division of Biomechanics
• Project duration: 37 months from 1 Ocotber 2017

Background

Osteoporosis (OP) is a silent bone disease that leads to loss of bone density, reduced bone strength and ultimately fracture. It is underestimated, underdiagnosed and undertreated. It affects one in three women and one in five men over 50. OP is responsible for more than 4 million fractures each year in the EU, with hip fracture being the most common type. This translates into more than €40 billion in healthcare costs, of which less than 5% is spent on prevention. Along with diabetes, cardiovascular diseases and cancer, this is one of the most important challenges for health care in the coming decades. In Lower Austria in particular, this alone burdens the health care system with an estimated 200 million euros per year. Early diagnosis of OP is essential for fracture prevention. This in turn increases the patient's quality of life and reduces health and social costs. Bone density is used as a predictor of the risk of osteoporotic fractures. It is measured with DEXA and diagnosed with a derived T-score. However, recent studies have shown that such densitometry measurements are inaccurate and inadequate. For example, more than 50% of surgical fractures occur in patients considered "low risk" by this method, and 15% of patients are wrongly treated because of their "high risk". Improving this situation requires (a) better screening techniques, (b) more screening and (c) improved diagnostic values. The aim of this project is to improve diagnostic tools for osteoporosis. Bone fractures occur due to overload and / or reduced resistance to stress due to bone loss. Finite Element Analysis (FEA) simulation is a non-invasive numerical method to estimate individual bone strength in vivo based on DEXA or Quantitative Computed Tomography (QCT) images. Geometric, structural and material properties are calculated from images and combined with typical physiological loading conditions, including magnitude, direction and frequency of loading. The accuracy of the model results from a good knowledge of all these parameters. FEM-based bone strength can effectively improve the diagnosis, assessment and monitoring of osteoporosis. Despite the significant progress made in the last decade, these predictions still need to be significantly improved through improvements in imaging, mechanical testing and simulation techniques to justify their clinical use.