Computed tomography assessment of low-energy acetabular fractures in the elderly
Thesis event information
Date and time of the thesis defence
Place of the thesis defence
Leena Palotie auditorium (101A) (Aapistie 5 A) Zoom-link: https://oulu.zoom.us/j/62020067684?pwd=MkgzNmIrajM1ZFVuZlhXVEYydGFwZz09
Topic of the dissertation
Computed tomography assessment of low-energy acetabular fractures in the elderly
Doctoral candidate
Master of Science in Biomedical Engineering and Clinical Technology Robel Kebede Gebre
Faculty and unit
University of Oulu Graduate School, Faculty of Medicine, Research Unit of Medical Imaging, Physics and Technology (MIPT)
Subject of study
Medical Technology, Orthopaedics, Public Health
Opponent
Professor Jari Hyttinen, Tampere University
Custos
Professor Timo Jämsä, University of Oulu
Computed tomography assessment of low-energy acetabular fractures in the elderly
Hip and pelvic fractures represent significant health risks in the aging population. The occurrence of hip fractures has been decreasing, whereas the incidence of pelvic fractures in the elderly has increased. Low-energy acetabular fractures are osteoporotic fractures of the acetabulum that are a consequence of falls. The fundamental biomechanical conditions and the risk factors that lead to such types of fractures are not known. Hence, the objective of this thesis was to investigate the structural and biomechanical risk factors that contribute to low-energy acetabular fractures.
When a person is admitted into a hospital’s emergency center as a result of hip or pelvic trauma, they are scanned with computed tomography (CT) imaging to investigate the extent of the trauma. CT is the preferred imaging modality due to its versatility to image pelvic bone. This thesis analyzed CT images of elderly subjects (n=121) that had sustained acetabular fractures, and gender and age-matched control subjects with no fractures.
First, three-dimensional models of the bones were segmented from the CTs, and the shape and geometry of the proximal femur and acetabular geometries were measured. Second, the trabecular fine structure of the bones was measured from the CT slices. Third, hip osteoarthritis was defined by using a deep learning model. Understanding the association between hip osteoarthritis and pelvic fractures is important since hip osteoarthritis is one of the leading causes of joint disorders in the elderly.
The acetabular fracture cases showed significantly lower neck shaft angle and longer femoral neck axis length, and lower trabecular apparent density at the femoral head and the acetabulum than the controls. Although not statistically significant, a higher prevalence of radiographic hip osteoarthritis was observed in the acetabular fracture cases. Machine learning methods were able to discriminate fractures from controls as well as to classify hip osteoarthritis from the CT data.
In conclusion, this thesis revealed biomechanical and structural risk factors involved in fall-related acetabular fractures that occur in the elderly. The findings can be applied in recognizing individuals with an increased risk of acetabular fracture.
When a person is admitted into a hospital’s emergency center as a result of hip or pelvic trauma, they are scanned with computed tomography (CT) imaging to investigate the extent of the trauma. CT is the preferred imaging modality due to its versatility to image pelvic bone. This thesis analyzed CT images of elderly subjects (n=121) that had sustained acetabular fractures, and gender and age-matched control subjects with no fractures.
First, three-dimensional models of the bones were segmented from the CTs, and the shape and geometry of the proximal femur and acetabular geometries were measured. Second, the trabecular fine structure of the bones was measured from the CT slices. Third, hip osteoarthritis was defined by using a deep learning model. Understanding the association between hip osteoarthritis and pelvic fractures is important since hip osteoarthritis is one of the leading causes of joint disorders in the elderly.
The acetabular fracture cases showed significantly lower neck shaft angle and longer femoral neck axis length, and lower trabecular apparent density at the femoral head and the acetabulum than the controls. Although not statistically significant, a higher prevalence of radiographic hip osteoarthritis was observed in the acetabular fracture cases. Machine learning methods were able to discriminate fractures from controls as well as to classify hip osteoarthritis from the CT data.
In conclusion, this thesis revealed biomechanical and structural risk factors involved in fall-related acetabular fractures that occur in the elderly. The findings can be applied in recognizing individuals with an increased risk of acetabular fracture.
Last updated: 1.3.2023