Abstract:SummaryA finite element modelling pipeline was adopted to predict femur strength in a retrospective cohort of 100 women. The effects of the imaging protocol and the meshing technique on the ability of the femur strength to classify the fracture and the control groups were analysed.IntroductionThe clinical standard to estimate the risk of osteoporotic hip fracture is based on the areal bone mineral density (aBMD). A few retrospective studies have concluded that finite element (FE)-based femoral strength is a be… Show more
“…In a potential improvement, it is now also possible to perform a "biomechanical CT" (BCT) (19) analysis on such previously taken hipcontaining CT scans. (14)(15)(16) The BCT analysis uses finite element analysis-based virtual stress testing, based on the information contained within the CT scan, to provide an estimate of the breaking strength of the femur and has been validated for predicting hip fracture in various research settings; (20)(21)(22)(23)(24)(25)(26) its clinical implementation also includes measurement of the DXA-equivalent hip BMD T-score. (14)(15)(16) Our goal in this study was to evaluate the diagnostic performance of ancillary BCT when used in this fashion in a large multicenter clinical setting.…”
“…In a potential improvement, it is now also possible to perform a "biomechanical CT" (BCT) (19) analysis on such previously taken hipcontaining CT scans. (14)(15)(16) The BCT analysis uses finite element analysis-based virtual stress testing, based on the information contained within the CT scan, to provide an estimate of the breaking strength of the femur and has been validated for predicting hip fracture in various research settings; (20)(21)(22)(23)(24)(25)(26) its clinical implementation also includes measurement of the DXA-equivalent hip BMD T-score. (14)(15)(16) Our goal in this study was to evaluate the diagnostic performance of ancillary BCT when used in this fashion in a large multicenter clinical setting.…”
“…6) shows that the most optimal classification at the ARF0 = 37.4% threshold, with 77.6% specificity (95% CI: 63.4%–86.5%) and 81.6% sensitivity (95% CI: 68.3%–91.1%). The area under the ROC curve AUC = 0.852 (95% CI, 0.753–0.918) was significantly higher for ARF0 when compared to AUC = 0.750 corresponding to the standard-of-care predictor which is the DXA-based T-score at the femoral neck (Qasim et al 2016), and also when compared to AUC = 0.82 corresponding to the CT-FE based minimum bone strength predictor (Viceconti et al 2018). The classification by ARF0 was found to be significant after adjusting for femoral neck T-score ( p < 0.001).Fig.…”
Osteoporotic hip fractures are a major healthcare problem. Fall severity and bone strength are important risk factors of hip fracture. This study aims to obtain a mechanistic explanation for fracture risk in dependence of these risk factors. A novel modelling approach is developed that combines models at different scales to overcome the challenge of a large space–time domain of interest and considers the variability of impact forces between potential falls in a subject. The multiscale model and its component models are verified with respect to numerical approximations made therein, the propagation of measurement uncertainties of model inputs is quantified, and model predictions are validated against experimental and clinical data. The main results are model predicted absolute risk of current fracture (ARF0) that ranged from 1.93 to 81.6% (median 36.1%) for subjects in a retrospective cohort of 98 postmenopausal British women (49 fracture cases and 49 controls); ARF0 was computed up to a precision of 1.92 percentage points (pp) due to numerical approximations made in the model; ARF0 possessed an uncertainty of 4.00 pp due to uncertainties in measuring model inputs; ARF0 classified observed fracture status in the above cohort with AUC = 0.852 (95% CI 0.753–0.918), 77.6% specificity (95% CI 63.4–86.5%) and 81.6% sensitivity (95% CI 68.3–91.1%). These results demonstrate that ARF0 can be computed using the model with sufficient precision to distinguish between subjects and that the novel mechanism of fracture risk determination based on fall dynamics, hip impact and bone strength can be considered validated.
“…In the fields of biomechanics, FE analysis‐based bone strength assessment, age‐related (or drug‐related) bone remodeling, and bone reconstruction with bioresorbable material have been recently developed and attempted to investigate bone microstructure for various purposes. However, due to a lack of in vivo HR imaging techniques, these methods have struggled for further clinical applications.…”
Inspired by the self-optimizing capabilities of bone, a new concept of bone microstructure reconstruction has been recently introduced by using 2D synthetic skeletal images. As a preliminary clinical study, this paper proposes a topology optimization-based method that can estimate 3D trabecular bone microstructure for the volume of interest (VOI) from 3D computed tomography (CT) scan data with enhanced computational efficiency and phenomenological accuracy. For this purpose, a localized finite element (FE) model is constructed by segmenting a target bone from CT scan data and determining the physiological local loads for the VOI. Then, topology optimization is conducted with multiresolution bone mineral density (BMD) deviation constraints to preserve the patient-specific spatial bone distribution obtained from the CT scan data. For the first time, to our knowledge, this study has demonstrated that 60-μm resolution trabecular bone images can be reconstructed from 600-μm resolution CT scan data (a 62-year-old woman with no metabolic bone disorder) for the 4 VOIs in the proximal femur. The reconstructed trabecular bone includes the characteristic trabecular patterns and has morphometric indices that are in good agreement with the anatomical data in the literature. As for computational efficiency, the localization for the VOI reduces the number of FEs by 99%, compared with that of the full FE model. Compared with the previous single-resolution BMD deviation constraint, the proposed multiresolution BMD deviation constraints enable at least 65% and 47% reductions in the number of iterations and computing time, respectively. These results demonstrate the clinical feasibility and potential of the proposed method.
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