ABSTRACT
Osteoporotic fractures of the femur are associated with poor healing, disability, reduced quality of life, and high mortality rates within 1 year. Moreover, osteoporotic fractures of the femur are still considered to be an unsolved problem in orthopedic surgery. In order to more effectively identify osteoporosis-related fracture risk and develop advanced treatment approaches for femur fractures, it is necessary to acquire a greater understanding of how osteoporosis alters the diaphyseal structure and biomechanical characteristics. The current investigation uses computational analyses to comprehensively examine how femur structure and its associated properties differ between healthy and osteoporotic bones. The results indicate statistically significant differences in multiple geometric properties between healthy femurs and osteoporotic femurs. Additionally, localized disparities in the geometric properties are evident. Overall, this approach will be beneficial in the development of new diagnostic procedures for highly detailed patient-specific detection of fracture risk, for establishing novel injury prevention treatments, and for informing advanced surgical solutions.
ABSTRACT
There are still numerous problems with modern joint replacement prostheses, which negatively influence patient health and recovery. For example, it is especially important to avoid failures and complications following hip arthroplasty because the loss of hip joint function is commonly associated with increased demand on the healthcare system, reoperation, loss of independence, physical disability, and death. The current study uses hip arthroplasty as a model system to present a new strategy of computationally generating patient-specific statistical reconstructions of complete healthy anatomical structures from computed tomography (CT) scans of damaged anatomical structures. The 3D model morphological data were evaluated from damaged femurs repaired with prosthetic devices and the respective damaged femurs that had been restored using statistical reconstruction. The results from all morphological measurements (i.e., maximum femoral length, Hausdorff distance, femoral neck anteversion, length of rotational center divergence, and angle of inclination) indicated that the values of femurs repaired with traditional prostheses did not fall within the +/-3 standard deviations of the respective patient-specific healthy anatomical structures. These results demonstrate that there are quantitative differences in the morphology of femurs repaired with traditional prostheses and the morphology of patient-specific statistical reconstructions. This approach of generating patient-specific statistical reconstructions of healthy anatomical structures might help to inform prosthetic designs so that new prostheses more closely resemble natural healthy morphology and preserve biomechanical function. Additionally, the patient-specific statistical reconstructions of healthy anatomical structures might be valuable for surgeons in that prosthetic devices could be selected and positioned to more accurately restore natural biomechanical function. All in all, this contribution establishes the novel approach of generating patient-specific statistical reconstructions of healthy anatomical structures from the CT scans of individuals' damaged anatomical structures to improve treatments and patient outcomes.
ABSTRACT
Finite element analysis is a powerful computational technique for augmenting biomedical research, prosthetics design, and preoperative surgical assessment. However, the validity of biomechanical data obtained from finite element analysis is dependent on the quality of the preceding data processing. Until now, little information was available about the effect of the segmentation process on finite element models and biomechanical data. The current investigation applied 4 segmentation approaches to 129 femur specimens, yielding a total of 516 finite element models. Biomechanical data including average displacement, pressure, stress, and strain were collected from experimental groups based on the different segmentation approaches. The results indicate that only a 5.0% variation in the segmentation process leads to statistically significant differences in all 4 biomechanical measurements. These results suggest that it is crucial for consistent segmentation procedures to be applied to all specimens within a study. This methodological advancement will help to ensure that finite element data will be more accurate and that research conclusions will have greater validity.
