Activities per year
Project Details
Description
Over 40% of Swedish women and 13% of men over 50 years will have an osteoporotic fracture at the hip, spine, or wrist. An essential step to prevent these fractures is to identify the subjects who are at high risk. The current clinical assessment for fracture risk, based on bone mineral density (BMD) combined with risk factors, fails to identify about 30% of those who actually fracture.
The aim of this project is to develop a more accurate method to assess fracture risk based on the actual bone strength, calculated using patient-specific finite element (FE) models. This new method will be evaluated against data from two large clinical cohorts.
The project combines several advanced computational techniques in a fashion which is compatible with the current clinical scenario. The validation using data from prospective clinical cohorts will represent a milestone to bridge the gap between engineering and medicine.
The timely and accurate prediction of fracture risk that can be achieved by introducing individual bone strength can reduce the number of people incurring in osteoporotic fractures, thus ultimately contributing to a significant improvement of the health status in the population.
The aim of this project is to develop a more accurate method to assess fracture risk based on the actual bone strength, calculated using patient-specific finite element (FE) models. This new method will be evaluated against data from two large clinical cohorts.
The project combines several advanced computational techniques in a fashion which is compatible with the current clinical scenario. The validation using data from prospective clinical cohorts will represent a milestone to bridge the gap between engineering and medicine.
The timely and accurate prediction of fracture risk that can be achieved by introducing individual bone strength can reduce the number of people incurring in osteoporotic fractures, thus ultimately contributing to a significant improvement of the health status in the population.
Status | Finished |
---|---|
Effective start/end date | 2019/05/01 → 2020/04/30 |
Funding
- The Royal Physiographic Society in Lund
UKÄ subject classification
- Other Engineering and Technologies not elsewhere specified
- Medical Image Processing
- Other Medical Engineering
Activities
- 1 Research or teaching at external organisation
-
ETH Zürich
Lorenzo Grassi (Visiting researcher)
2019 Apr 1 → 2019 Sept 30Activity: Visiting an external institution › Research or teaching at external organisation