Quantitative ultrasound (QUS) is a promising tool to estimate bone structure characteristics and predict fragile fracture. The aim of this pilot cross-sectional study was to evaluate the performance of a multi-channel residual network (MResNet) based on ultrasonic radiofrequency (RF) signal to discriminate fragile fractures retrospectively in postmenopausal women. Methods. RF signal and speed of sound (SOS) were obtained using an axial transmission QUS at one‐third distal radius for 246 postmenopausal women. Based on the involved RF signal, we conducted a MResNet, which combines multi-channel training with original ResNet, to classify the high risk of fragility fractures patients from all subjects. The bone mineral density (BMD) at lumber, hip and femoral neck acquired with DXA was recorded on the same day. The fracture history of all subjects in adulthood were collected. To assess the ability of the different methods in the discrimination of fragile fracture, the odds ratios (OR) calculated using binomial logistic regression analysis and the area under the receiver operator characteristic curves (AUC) were analyzed. Results. Among the 246 postmenopausal women, 170 belonged to the non-fracture group, 50 to the vertebral group, and 26 to the non-vertebral fracture group. MResNet was discriminant for all fragile fractures (OR = 2.64; AUC = 0.74), for Vertebral fracture (OR = 3.02; AUC = 0.77), for non-vertebral fracture (OR = 2.01; AUC = 0.69). MResNet showed comparable performance to that of BMD of hip and lumbar with all types of fractures, and significantly better performance than SOS all types of fractures. Conclusions. the MResNet model based on the ultrasonic RF signal can significantly improve the ability of QUS device to recognize previous fragile fractures. Moreover, the performance of the proposed model modified by age, weight, and height is further optimized. These results open perspectives to evaluate the risk of fragile fracture applying a deep learning model to analyze ultrasonic RF signal

Abstract. Objectives. Young patients receiving metallic bone implants after surgical resection of bone cancer require implants that last into adulthood, and ideally life-long. Porous implants with similar stiffness to bone can promote bone ingrowth and thus beneficial clinical outcomes. A mechanical remodelling stimulus, strain energy density (SED), is thought to be the primary control variable of the process of bone growth into porous implants. The sequential process of bone growth needs to be taken into account to develop an accurate and validated bone remodelling algorithm, which can be employed to improve porous implant design and achieve better clinical outcomes. Methods. A bone remodelling algorithm was developed, incorporating the concept of bone connectivity (sequential growth of bone from existing bone) to make the algorithm more physiologically relevant. The algorithm includes adaptive elastic modulus based on apparent bone density, using a node-based model to simulate local remodelling variations while alleviating numerical checkerboard problems. Strain energy density (SED) incorporating stress and strain effects in all directions was used as the primary stimulus for bone remodelling. The simulations were developed to run in MATLAB interfacing with the commercial FEA software ABAQUS and Python. The algorithm was applied to predict bone ingrowth into a porous implant for comparison against data from a sheep model. Results. The accuracy of the predicted bone remodelling was verified for standard loading cases (bending, torsion) against analytical calculations. Good convergence was achieved. The algorithm predicted good bone remodelling and growth into the investigated porous implant. Using the standard algorithm without connectivity, bone started to remodel at locations unconnected to any bone, which is physiologically implausible. The implementation of bone connectivity ensures the gradual process of bone growth into the implant pores from the sides. The bone connectivity algorithm predicted that the full remodelling required more time (approximately 50% longer), which should be considered when developing post-surgical rehabilitation strategies for patients. Both algorithms with and without bone connectivity implementation converged to same final stiffness (less than 0.01% difference). Almost all nodes reached the same density value, with only a limited number of nodes (less than 1%) in transition areas with a strong density gradient having noticeable differences. Conclusions. An improved bone remodelling algorithm based on strain energy density that modelled the sequential process of bone growth has been developed and tested. For a porous metallic bone implant the same final bone density distribution as for the original adaptive elasticity theory was predicted, with a slower and more fidelic process of growth from existing surrounding bone into the porous implant. Declaration of Interest. (b) declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported:I declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research project

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Metabolic syndrome and low-grade systemic inflammation are associated with knee osteoarthritis (OA), but the relationships between these factors and OA in other synovial joints are unclear. The aim of this study was to determine if a high-fat/high-sucrose (HFS) diet results in OA-like joint damage in the shoulders, knees, and hips of rats after induction of obesity, and to identify potential joint-specific risks for OA-like changes.

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The aim of this study was to examine whether asymmetric loading influences macrophage elastase (MMP12) expression in different parts of a rat tail intervertebral disc and growth plate and if MMP12 expression is correlated with the severity of the deformity.