Osteoarthritis (OA) is the most common form of human joint disease causing significant pain and disability. Current treatment for hip OA is limited to pain management and joint replacement for end-stage disease. The development of methods for early diagnosis and new treatment options are urgently needed to minimise the impact of the disease. Studies of hip OA have shown that hip joint morphology correlates with susceptibility to hip OA and disease progression. Bone shape analyses play an important role in disease diagnosis, pre-operative planning, and treatment analysis as well as in epidemiological studies aimed at identifying risk factors for hip OA. Statistical Shape Models (SSMs) are being increasingly applied to imaging-based bone shape analyses as they provide a means of quantitatively describing the global shape of the bone. This is in contrast to conventional clinical and research practice where the analysis of bone shape is reduced to a series of measurements of lengths and angles. This thesis describes the development of a novel fully automatic software system that segments the proximal femur from anteroposterior (AP) pelvic radiographs by densely placing 65 points along its contour. These annotations can then be used for the detailed morphometric analysis of proximal femur shape. The performance of the system was evaluated on a large dataset of 839 radiographs of mixed quality. Achieving a mean point-to-curve error of less than 0.9mm for 99% of all 839 AP pelvic radiographs, this is the most accurate and robust automatic method for segmenting the proximal femur in two-dimensional radiographs yet published. The system was also applied to a number of morphometric analyses of the proximal femur, showing that SSM-based radiographic proximal femur shape significantly differs between males and females, and is highly symmetric between the left and right hip joint of an individual. In addition, the research described in this thesis demonstrates how the point annotations resulting from the system can be used for univariate and multivariate genetic association analyses, identifying three novel genetic variants that contribute to radiographic proximal femur shape while also showing an association with hip OA.The developed system will facilitate complex morphometric and genetic analyses of shape variation of the proximal femur across large datasets, paving the way for the development of new options to diagnose, treat and prevent hip OA.

A osteoporose é um enfraquecimento progressivo dos ossos, que ficam cada vez mais sujeitos a fraturas. Embora possa acometer ambos os sexos, ela é mais freqüente nas mulheres após a menopausa devido à diminuição dos hormônios femininos, os estrógenos. É uma doença que avança lentamente, sem sintomas, geralmente sem ser percebida até que aconteça uma fratura. Uma vez instalada a osteoporose, tem que se evitar maior perda óssea para prevenir fraturas. O objetivo deste trabalho é analisar o quadro de instalação de osteopenia em ossos corticais de ratas ovariectomizadas. Foi utilizado um modelo de osteopenia em 29 ratas Wistar ovarectomizadas com massa corpórea de 250 gramas. Os animais foram divididos em 6 grupos experimentais e eutanaziados em períodos diferentes: o primeiro grupo foi eutanaziado 30 dias após a cirurgia, que corresponde ao tempo de início da osteopenia, e a partir daí, os demais grupos foram eutanaziados numa seqüência de 15 dias até o 105º dia. A avaliação do quadro de instalação foi realizada através de medidas antropométricas e de propriedades mecânicas dos fêmures (ensaio de flexão de três pontos). Cada um dos itens obtidos foram comparados utilizando-se o programa GraphPad InStat 3. O teste t-Student foi aplicado para checar a variação do peso corporal com p < 0,05. Aplicaram-se os testes ANOVA e Student-Newmam-Keuls com coeficiente de variação também menor que 5% para os demais itens analisados. Observou-se um aumento significativo no comprimento dos fêmures durante o primeiro mês de experimento, assim como na carga máxima aplicada. Os resultados obtidos neste estudo sugerem que a ovariectomia é um fator que não causa grandes alterações mecânicas e geométricas na região cortical dos fêmures de ratas no período analisado / Osteoporosis is a progressive weakness of bones, which are more and more subjects to fractures. Although it happens to men and women, it is more frequent in postmenopausal women due to a decrease of female hormones. It is a disease without symptoms and it is usually noticed only after a fracture. Once the osteoporosis is installed, a bigger bone loss must be avoided in order to prevent a fracture. This study aims to analyze the osteopenia installation process in cortical bones of ovariectomized rats. An osteopenia model was used in 29 ovariectomized Wistar female rats with body weight of 250 g. Animals were divided in 6 experimental groups, the first group was sacrificed 30 days after the surgery and, the other groups, every 15 days on until the 105th day. The evaluation of installation process was made through anthropometrics and mechanical proprieties of femur (three-point bending test). Program GraphPad InStat 3 was used in order to compare the observed items. T-student test was used to check the body weight variation with p < 0,05 and tests ANOVA and Student-Newmam-Keuls were applied to the other items (p < 0,05). A significant increase in femur length and in maximum bending load were obtained. The found results in this research suggest that ovariectomy is a factor that does not cause significant mechanical and geometric alterations on cortical region of femurs in female rats during the analyzed period

Stage IV cancer is characterized by a cancer’s ability to metastasize, or spread throughout the body. Metastatic disease in bone is a devastating condition affecting hundreds of thousands of people each year. Stage IV cancer patients suffering from metastatic disease in the proximal femur are at high risk of catastrophic pathologic fracture, an event which severely impacts patient health. Although metrics have been created to assess the risk of impending fracture, they lack specificity in the proximal femoral region. Shortcomings of these metrics further complicate clinical decision making related to prophylactic fixation in these medically compromised individuals. Fortunately, by using computational modeling to study this at-risk patient population, the likelihood of fracture due to metastatic lesions in the proximal femur can be more accurately assessed to improve clinical decision making. Finite element analysis (FEA) is a computational modeling technique that can non-invasively provide mechanics information to better assess true fracture risk of a given metastatic lesion. Although FEA has previously been utilized to study metastatic disease, lesions were always modeled as spheres or ellipsoids, while true lesion shapes are far more amorphous. It was the focus of this study to validate FEA’s ability to predict fracture location in cadaveric femora with realistically shaped experimental metastatic lesions. Off-set torsion, or load applied off-set from the fixed long bone axis, was applied to cadaveric specimens with mechanically induced metastatic lesions, and the resultant fracture location was compared to specimen-specific FEA models replicating the mechanical test. FEA was able to correctly predict fracture locations in five models. Determining fracture risk based on objective mechanical data may more accurate and effective in this patient population.

Includes bibliographical references / Background: Dislocation is one of the most common orthopaedic complications after primary total hip arthroplasty (THA). The reported dislocation rate in elective THR is 5 - 8%. This number increases up to 22% for THA done for neck of femur fractures. Larger femoral head sizes increase the head-neck ratio and range of motion before impingement, therefore reducing the dislocation rate. Due to the reported increase in dislocation for trauma, some surgeons prefer to do a hemi-arthroplasty or open reduction and internal fixation (ORIF). Methods: A retrospective review of all THA done for neck of femur fractures during 2006 - 2012 was undertaken at a large referral hospital. Records were reviewed for patient related and surgical risk factors. We excluded all pathological fractures, extra-capsular fractures and failed ORIF. Results: A total of 96 cases were identified as suitable for analysis. Average age at surgery was 73.2 years (range 30 - 81). Delay to surgery was 5.3 days (range 1 - 63). Average follow up period was 18.3 months (range 3 months - 4.3years). Four patients (4.3%) had a confirmed dislocation. The four patients who had confirmed dislocation had the following characteristics, 28 mm femoral head size, age over 60 years, 2 posterior approaches and 3 females, although not statistically significant. Conclusion: The outcomes of THR in patients with neck of femur fractures can be favourable and provide good long-term prosthesis survival. We report on low dislocation rate post total hip replacement for intra-capsular neck of femur fractures.

This master’s thesis deals with the strain-stress analyses of the pig femoral bone in physiological and pathological condition. Pathological condition is determined by a significant defect in diaphysis of the bone fixed by using direct angular stable locking plate with screws. The thesis describes the creation of computational models with special focus on bone tissues models of material, which are based on analysis of digital images from computed tomography (CT). There are created and analyzed three types of computational models: the first one corresponds to physiological condition of femur, the second one to femur with significant defect and fixation plate and the third one to femur with significant defect, fixation plate and rod. Every model is loaded in three ways: the first of them simulating the experimental conditions of pressure testing of the femur, the second one bone stress in the sagittal plane and the third one stress by using load determined from probational release of the pig hind limb. Strain-stress states are determined by finite element method using Ansys computational system. Gained results of stress and strain are compared with experimental measurements. The thesis presents a basic idea of the mechanical behavior of porcine femur and porcine femur with significant bone defect.

The area of clinical biomechanics is a rapidly growing engineering discipline in the world of today. This master's thesis is concerned with the stress-strain analysis of human femoral neck. In the first place it emphasizes the creation of computational model, especially the model of bone material, because of its high level of inhomogeneity. The creation of the computational model has been tested using several software packages, which are often mentioned in connection with the biomechanical tasks. The latter part of the thesis contains finite element method analysis for several material models, which are based on extensive analysis of relations used to transfer the information from computed tomography data to material model properties. Subsequently, the realization of experimental measurement is described. The final part of the thesis contains evaluated results obtained from the FEM analysis, which are also compared with the experiment.

The presented Master’s Thesis aims at determining stress and strain analysis of femur with fracture fixed by intramedullary nail and cerclage cable. The one of the goals of this work was create computational model which will be able simulate problem. Computational model consist of model of femur , it’s geometry was created on basis of CT data, than there was created model of intramedullary nail and cerclage cable on a basis of real objects. In this work was created simply model of femur with the same material features and characteristic proportions as analyzed model of femur. On this model was investigated direct impact of cerclage cables on deformation, respectively displacement in a surroundings of femoral fracture. On a final model were applied the real loads conditions. Preload in a cerclage cable was simulated by cooling down to a temperature from analytical calculation. Concluding analysis was powered by Finite Element Method (FEM) applied in system ANSYS Workbench 14.5 . Due to a results of analysis, we could say that using cerclage cables in combination with intramedullary nail is an effective way for healing femoral shaft fractures.

Osteoarthritis (OA) is a chronic, painful, and currently incurable disease characterized by structural deterioration and loss of function of synovial joints. OA is known to involve profound changes in bone density and microstructure near to, and even distal to, the joint. The prevailing view is that these changes in density and microstructure serve to stiffen the subchondral region thereby altering the mechanical environment (stresses and strains) within the epiphyseal and metaphyseal bone, and that these alterations trigger the aberrant cellular signaling and tissue damage characteristic of the progression of OA. Critically, however, these alterations in mechanical environment have never been well documented in a quantitative fashion in hip OA. Separately, although OA is generally thought to be inversely associated with fragility fracture, recent data challenge this idea and suggest that OA may actually modulate which regions of the proximal femur are at risk of fracture. Therefore, the goal of this work was to provide a spatial assessment of bone density and microstructure in hip OA and then examine the mechanical consequences of these OA-related abnormalities throughout the proximal femur. First, micro-computed tomography and data-driven computational anatomy were used to examine 3-D maps of the distribution of bone density and microstructure in human femoral neck samples with increasing severity of radiographic OA, providing evidence of the heterogeneous and multi-faceted changes in hip OA and discussion of the implications for OA progression and fracture risk. Second, the feasibility of proton density-weighted MRI in image-based finite element (FE) modeling, to examine stress, strain, and risk of failure in the proximal femur under sideways fall, was assessed by comparison to the current standard of CT-based FE modeling. Third, phantom-less calibration for CT-based FE modeling was used with clinically available pre-operative patient scans to assess bone strength and failure risk of the proximal femur in hip OA. Overall, the results of this work provide a rich, quantitative definition of the ways in which the bone mechanical environment under traumatic loading differ in association with hip OA, and then highlight the potential for clinical image-based FE methods to be used opportunistically to assess bone strength and failure risk at the hip. This work is significant because it directly tests the long-standing premise that OA is associated with changes in the mechanical environment of the bone tissue in ways that are impactful for OA progression; further, this work examines how these changes may influence risk of hip fracture. The results can be used to identify mechanistic predictors of OA progression, to inform development of bone-targeting treatments for OA, and to more broadly understand bone damage and fracture in this population.

Atypical Femur Fracture(AFF) is a type of stress fracture that occurs in conjunction with prolonged bisphosphonate treatment. In practice, AFF is very rarely identified from Normal Femur Fracture(NFF) correctly on the first diagnostic X-ray examination. This project aims at developing an algorithm based on deep neural networks to assist clinicians with the diagnosis of atypical femurfracture. Two diagnostic pipelines were constructed using the Convolutional Neural Network (CNN) as the core classifier. One is a fully automatic pipeline, where the X-rays image is directly input into the network with only standardized pre-processing steps. Another interactive pipeline requires the user to re-orient the femur bones above the fractures to a vertical position and move the fracture line to the image center, before the repositioned image is sent to the CNNs. Three most popular CNNs architectures, namely VGG19, InceptionV3 and ResNet50,were tested for classifying the images to either AFF or NFF. Transfer learning technique was used to pre-train these networks using images form ImageNet. The diagnosis accuracy was evaluated using 5-fold cross-validation. With the fully automatic diagnosis pipeline, we achieved diagnosis accuracy of 82.7%, 89.4%, 90.5%, with VGG19, InceptionV3 and ResNet50, respectively. With the interactive diagnostic pipeline, the diagnosis accuracy was improved to 92.2%, 93.4% and 94.4%, respectively. To further validate the results, class activation mapping is used for indicating the discriminative image regions that the neural networks learn to identify a certain class.

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