Automation of a DXA-based finite element tool for clinical assessment of hip fracture risk
Introduction
Fall-induced hip fracture is a common health risk among elderly people, especially for those who have osteoporosis [1], [2], [3], [4]. Osteoporosis can significantly reduce bone strength, it is a ‘silent’ disease as it has no symptom until first fracture. Therefore, subjects of high fracture risk must be diagnosed by clinicians using a reliable assessment tool. A number of tools for assessing fracture risk have been developed from either population-based statistical models [5], [6], [7], [8], [9] or biomechanical models [10], [11], [12], [13], [14], [15], [16]. The existing clinical tools are almost exclusively based on statistical models. T-score calculated from femoral bone mineral density (BMD) is the gold standard reference for screening osteoporosis and for assessing fracture risk [17], [18]. However, extensive clinical studies showed that the majority of patients who sustain low-trauma fractures have T-scores within the WHO (World Health Organization) safe range [19], [20], [21], [22]. Therefore, researchers were motivated to develop assessment tools to consider multiple clinical risk factors [23]. Among these tools FRAX (Fracture Risk Assessment Tool) is the most popular one, which is a web-based calculator to predict an individual's 10-year probability of hip fracture. However, FRAX still has limited accuracy even though 12 clinical risk factors are considered [5], [23], [24], [25]. The main reasons are: the considered clinical factors are biomechanically dependent and some important biomechanical variables such as fall-induced impact force are missing [26].
In view of the limitations of population-based tools, researchers have turned their attention to biomechanical approaches. Theoretically, biomechanical modeling has the potential to more accurately predict an individual's fracture risk than statistical approaches, as biomechanical models are based on well-established mechanical principles. A large number of finite element (FE) models have been developed to determine bone strength and to assess fracture risk. Most of the FE models are constructed from QCT (quantitative computed tomography) [11], [16], [27], [28], [29], [30], [31], [32], [33], [34], since QCT contains actual material and geometry information required to construct a three-dimensional finite element model. However, QCT uses high dosage of radiation and it is not recommended for routine clinical examination [35]. To meet the current clinical needs, DXA-based finite element models [36], [37] have also been developed, although construction of accurate finite element models from DXA images is more challenging and assumptions must be introduced.
Nevertheless, none of the finite element models have been used routinely in clinic. Indeed, there are still unresolved technical issues in the finite element models [27], for example, the discrepancy among different bone material models [38], the lack of a generally accepted risk threshold for clinical intervention and the inconsistency among risk measurements produced by different finite element models. However, even after the technical issues are all resolved, there is still an obstacle for clinical application, that is, the computer programs that implement the finite element models have not been completely automated. User intervention that often requires the knowledge of finite element analysis (FEA) does not appeal to clinicians. We developed a DXA-based FE model for the prediction of hip fracture risk [37], [39]. In this study, we completely automate the computer program with simple input, so that the automated DXA-based FE tool can be used by clinicians with a minimum training and no knowledge of FEA is required.
Section snippets
Automation of DXA-based finite element (Fe) model
The procedure of our DXA-based finite element analysis [37], [39] is shown in Fig. 1. The only input required by the procedure is a raw hip DXA, for example, the enCore file (with extension of DFF) generated by GE Lunar. The procedure starts with a clinical hip DXA of the concerned subject and includes the following steps: (1) Clinical regions of interest (ROI) are identified; (2) Proximal femur is segmented from the DXA and a contour of the femur is obtained; (3) A finite element mesh is
Clinical study
Short-term precision and the ability of discriminating clinical fracture cases from matched controls are the two important performance indicators of clinical tools for assessing hip fracture risk. To study the performance of the automated DXA-based FE tool, clinical cases were acquired from St. Boniface Hospital in Winnipeg under an approval of human subject research ethics, with personal information such as patient name and residence address removed before acquisition. Each case had a unique
Results
The average processing time for each case was about 15 seconds when the cases were processed in batch. If processed cases by case, the processing time was about 17 seconds. Both are acceptable for clinical practice.
Short-term precision of FRI and BMD expressed by coefficient of variation (CV, %) are presented in Table 3. In general, BMD still has the best short-term precision; automated DXA-based FE tool greatly improved the precision compared with the previous semi-automated version.
In the
Discussion
The large improvement in short-term precision of our DXA-based FE tool is mainly attributed to the automation of femur contour segmentation. In the previous semi-automated version [37], manual operation is required in segmentation of femur contour, which introduces random error. Automated segmentation in the new version is able to eliminate the random error and thus improves the short-term precision. However, even the automated FE tool still has much lower short-term precision than BMD. There
Conclusion
In conclusion, this study made a step toward the application of our DXA-based FE model as a clinical tool for assessment of hip fracture risk. As the next step, we will apply our model in prospective studies and in validations of cross clinical centers and DXA scanners. The automated standalone computer program will be distributed to clinical centers. Furthermore, the computer program will be embedded into a web-site as an internet application, so that clinicians over the world will be able to
Conflict of Interest
All the three author declare there is no conflict of interest.
Acknowledgments
The reported research has been supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada (37098) and Research Manitoba (37807), which are gratefully acknowledged.
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