Journal
JOURNAL OF BIOMECHANICS
Volume 126, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.jbiomech.2021.110633
Keywords
Pelvis geometry; Shape variance; Sparse Principal Component Analysis (SPCA); Multivariate linear regression; Morphometric model
Categories
Funding
- Strategic Vehicle Research and Innova-tion (FFI) [2018-04998]
- VINNOVA
- Swedish Transport Administration
- Swedish Energy Agency
- Volvo Cars
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This study found that overall anthropometric variables can only explain 29% of the variance in pelvis geometry, with important shape variations not captured by these variables. This suggests that these unaccounted for shape variations could have significant implications for injury prediction in traffic safety analysis.
Pelvic fractures have been identified as the second most common AIS2+ injury in motor vehicle crashes, with the highest early mortality rate compared to other orthopaedic injuries. Further, the risk is associated with occupant sex, age, stature and body mass index (BMI). In this study, clinical pelvic CT scans from 132 adults (75 females, 57 males) were extracted from a patient database. The population shape variance in pelvis bone geometry was studied by Sparse Principal Component Analysis (SPCA) and a morphometric model was developed by multivariate linear regression using overall anthropometric variables (sex, age, stature, BMI). In the analysis, SPCA identified 15 principal components (PCs) describing 83.6% of the shape variations. Eight of these were significantly captured (alpha < 0.05) by the morphometric model, which predicted 29% of the total variance in pelvis geometry. The overall anthropometric variables were significantly related to geometrical features primarily in the inferior-anterior regions while being unable to significantly capture local sacrum features, shape and position of ASIS and lateral tilt of the iliac wings. In conclusion, a new detailed morphometric model of the pelvis bone demonstrated that overall anthropometric variables account for only 29% of the variance in pelvis geometry. Furthermore, variations in the superior-anterior region of the pelvis, with which the lap belt is intended to interact, were not captured. Depending on the scenario, shape variations not captured by overall anthropometry could have important implications for injury prediction in traffic safety analysis.
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