Identification of hip fracture patients from radiographs using Fourier analysis of the trabecular structure: a cross-sectional study

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Governing equations of tissue modelling and remodelling: A unified generalised description of surface and bulk balance

Several biological tissues undergo changes in their geometry and in their bulk material properties by modelling and remodelling processes. Modelling synthesises tissue in some regions and removes tissue in others. Remodelling overwrites old tissue material properties with newly formed, immature tissue properties. As a result, tissues are made up of different "patches", i.e., adjacent tissue regions of different ages and different material properties, within evolving boundaries. In this paper, generalised equations governing the spatio-temporal evolution of such tissues are developed within the continuum model. These equations take into account nonconservative, discontinuous surface mass balance due to creation and destruction of material at moving interfaces, and bulk balance due to tissue maturation. These equations make it possible to model patchy tissue states and their evolution without explicitly maintaining a record of when/where resorption and formation processes occurred. The time evolution of spatially averaged tissue properties is derived systematically by integration. These spatially-averaged equations cannot be written in closed form as they retain traces that tissue destruction is localised at tissue boundaries. The formalism developed in this paper is applied to bone tissues, which exhibit strong material heterogeneities due to their slow mineralisation and remodelling processes. Evolution equations are proposed in particular for osteocyte density and bone mineral density. Effective average equations for bone mineral density (BMD) and tissue mineral density (TMD) are derived using a mean-field approximation. The error made by this approximation when remodelling patchy tissue is investigated. The specific time signatures of BMD or TMD during remodelling events may provide a way to detect these events occurring at lower, unseen spatial resolutions from microCT scans.Comment: 14 pages, 8 figures. V2: minor stylistic changes, more detailed derivation of Eqs (30)-(31), additional comments on implication of BMD and TMD signatures for microCT scan

Artificial neural network analysis of bone quality DXA parameters response to teriparatide in fractured osteoporotic patients

Teriparatide is a bone-forming therapy for osteoporosis that increases bone quantity and texture, with uncertain action on bone geometry. No data are available regarding its influence on bone strain. To investigate teriparatide action on parameters of bone quantity and quality and on Bone Strain Index (BSI), also derived from DXA lumbar scan, based on the mathematical model finite element method. Forty osteoporotic patients with fractures were studied before and after two years of daily subcutaneous 20 mcg of teriparatide with dual X-ray photon absorptiometry to assess bone mineral density (BMD), hip structural analysis (HSA), trabecular bone score (TBS), BSI. Spine deformity index (SDI) was calculated from spine X-ray. Shapiro-Wilks, Wilcoxon and Student's t test were used for classical statistical analysis. Auto Contractive Map was used for Artificial Neural Network Analysis (ANNs). In the entire population, the ameliorations after therapy regarded BSI (-13.9%), TBS (5.08%), BMD (8.36%). HSA parameters of femoral shaft showed a worsening. Dividing patients into responders (BMD increase >10%) and non-responders, the first presented TBS and BSI ameliorations (11.87% and -25.46%, respectively). Non-responders presented an amelioration of BSI only, but less than in the other subgroup (-6.57%). ANNs maps reflect the mentioned bone quality improvements. Teriparatide appears to ameliorate not only BMD and TBS, but also BSI, suggesting an increase of bone strength that may explain the known reduction in fracture risk, not simply justified by BMD increase. BSI appears to be a sensitive index of TPD effect. ANNs appears to be a valid tool to investigate complex clinical systems

In silico methods to evaluate Fracture Risk and Bone Mineral Density changes in patients undergoing Total Hip Replacement

La sostituzione totale d’anca è uno degli interventi chirurgici con le più alte percentuali di successo. Esistono due varianti di protesi d’anca che differiscono in base al metodo di ancoraggio all’osso: cementate (fissaggio tramite cemento osseo) e non cementate (fissaggio tramite forzamento). Ad oggi, i chirurghi non hanno indicazioni quantitative di supporto per la scelta fra le due tipologie di impianto, decidendo solo in base alla loro esperienza. Due delle problematiche che interessano le protesi non cementate sono la possibilità di frattura intra-operatoria durante l’inserimento forzato e il riassorbimento osseo nel periodo di tempo successivo all’intervento. A partire da rilevazioni densitometriche effettuate su immagini da TC di pazienti sottoposti a protesi d’anca non cementata, sono stati sviluppati due metodi: 1) per la valutazione del rischio di frattura intra-operatorio tramite analisi agli elementi finiti; 2) per la valutazione della variazione di densità minerale ossea (tridimensionalmente attorno alla protesi) dopo un anno dall’operazione. Un campione di 5 pazienti è stato selezionato per testare le procedure. Ciascuno dei pazienti è stato scansionato tramite TC in tre momenti differenti: una acquisita prima dell’operazione (pre-op), le altre due acquisite 24 ore (post 24h) e 1 anno dopo l’operazione (post 1y). I risultati ottenuti hanno confermato la fattibilità di entrambi i metodi, riuscendo inoltre a distinguere e a quantificare delle differenze fra i vari pazienti. La fattibilità di entrambe le metodologie suggerisce la loro possibilità di impiego in ambito clinico: 1) conoscere la stima del rischio di frattura intra-operatorio può servire come strumento di guida per il chirurgo nella scelta dell’impianto protesico ottimale; 2) conoscere la variazione di densità minerale ossea dopo un anno dall’operazione può essere utilizzato come strumento di monitoraggio post-operatorio del paziente

Bone strain index as a predictor of further vertebral fracture in osteoporotic women: An artificial intelligence-based analysis

Background Osteoporosis is an asymptomatic disease of high prevalence and incidence, leading to bone fractures burdened by high mortality and disability, mainly when several subsequent fractures occur. A fragility fracture predictive model, Artificial Intelligence-based, to identify dual X-ray absorptiometry (DXA) variables able to characterise those patients who are prone to further fractures called Bone Strain Index, was evaluated in this study. Methods In a prospective, longitudinal, multicentric study 172 female outpatients with at least one vertebral fracture at the first observation were enrolled. They performed a spine X-ray to calculate spine deformity index (SDI) and a lumbar and femoral DXA scan to assess bone mineral density (BMD) and bone strain index (BSI) at baseline and after a follow-up period of 3 years in average. At the end of the follow-up, 93 women developed a further vertebral fracture. The further vertebral fracture was considered as one unit increase of SDI. We assessed the predictive capacity of supervised Artificial Neural Networks (ANNs) to distinguish women who developed a further fracture from those without it, and to detect those variables providing the maximal amount of relevant information to discriminate the two groups. ANNs choose appropriate input data automatically (TWIST-system, Training With Input Selection and Testing). Moreover, we built a semantic connectivity map usingthe Auto Contractive Map to provide further insights about the convoluted connections between the osteoporotic variables under consideration and the two scenarios (further fracture vs no further fracture). Results TWIST system selected 5 out of 13 available variables: age, menopause age, BMI, FTot BMC, FTot BSI. With training testing procedure, ANNs reached predictive accuracy of 79.36%, with a sensitivity of 75% and a specificity of 83.72%. The semantic connectivity map highlighted the role of BSI in predicting the risk of a further fracture. Conclusions Artificial Intelligence is a useful method to analyse a complex system like that regarding osteoporosis, able to identify patients prone to a further fragility fracture. BSI appears to be a useful DXA index in identifying those patients who are at risk of further vertebral fractures. Copyright

Stochastic Assessment of Bone Fragility in Human Lumbar Spine

Osteoporotic fractures are a vital public health concern and create a great economic burden for our society. It is estimated that more than 2 million fractures occur in the United States at a cost of $17 billion each year. Deterioration of microarchitecture of trabecular bone is considered as a major contributor to bone fragility. Current clinical imaging modalities such as Dual-energy X-ray absorptiometry (DXA) are not able to describe bone microarchitecture due to their low resolution. The main objective of this study was to obtain the relationship between stochastic parameters calculated from bone mineral density (BMD) maps of DXA scans and the microarchitecture parameters measured from three dimensional (3D) images of human lumbar vertebrae acquired using a Micro-Computed Tomography (Micro-CT) scanner. Eighteen human lumbar vertebrae with intact posterior elements were scanned in the posterior-anterior projection using a DXA scanner. Stochastic parameters such as correlation length (L), sill variance (C) and nugget variance ( ) were calculated by fitting a theoretical model onto the experimental variogram of the BMD map of the human vertebrae. In addition, microarchitecture parameters such as bone volume fraction (BV/TV), trabecular thickness (Tb.Th), trabecular separation (Tb.Sp), trabecular number (Tb.N), connectivity density (Conn.Dn), and bone surface-to-volume ratio (BS/BV) were measured from 3D images of the same human lumbar vertebrae. Significant correlations were observed between stochastic predictors and microarchitecture parameters of trabecular bone. Specifically, the sill variance was positively correlated with the bone volume fraction, trabecular thickness, trabecular number, connectivity density and negatively correlated with the bone surface to volume ratio and trabecular separation. This study demonstrates that stochastic assessment of the inhomogeneity of bone mineral density from routine clinical DXA scans of human lumbar vertebrae may have the potential to serve as a valuable clinical tool in enhancing the prediction of risks for osteoporotic fractures in the spine. The main advantage of using DXA scans is that it would be cost effective, since most hospitals already have DXA machines and there would be no need for purchasing new equipment

Prediction of osteoporotic hip fracture in postmenopausal women through patient-specific FE analyses and machine learning

[EN] A great challenge in osteoporosis clinical assessment is identifying patients at higher risk of hip fracture. Bone Mineral Density (BMD) measured by Dual-Energy X-Ray Absorptiometry (DXA) is the current gold-standard, but its classification accuracy is limited to 65%. DXA-based Finite Element (FE) models have been developed to predict the mechanical failure of the bone. Yet, their contribution has been modest. In this study, supervised machine learning (ML) is applied in conjunction with clinical and computationally driven mechanical attributes. Through this multi-technique approach, we aimed to obtain a predictive model that outperforms BMD and other clinical data alone, as well as to identify the best-learned ML classifier within a group of suitable algorithms. A total number of 137 postmenopausal women (81.4 +/- 6.95 years) were included in the study and separated into a fracture group (n = 89) and a control group (n = 48). A semi-automatic and patient-specific DXA-based FE model was used to generate mechanical attributes, describing the geometry, the impact force, bone structure and mechanical response of the bone after a sideways-fall. After preprocessing the whole dataset, 19 attributes were selected as predictors. Support Vector Machine (SVM) with radial basis function (RBF), Logistic Regression, Shallow Neural Networks and Random Forest were tested through a comprehensive validation procedure to compare their predictive performance. Clinical attributes were used alone in another experimental setup for the sake of comparison. SVM was confirmed to generate the best-learned algorithm for both experimental setups, including 19 attributes and only clinical attributes. The first, generated the best-learned model and outperformed BMD by 14pp. The results suggests that this approach could be easily integrated for effective prediction of hip fracture without interrupting the actual clinical workflow.This study was partially funded by two grants Catedra UPVFundacion Quaes, obtained by Eduardo Villamor Medina and Antonio Cutillas Pardines, and one FPI grant (FPI-SP20170111) from the Universitat Politecnica de Valencia obtained by Eduardo Villamor Medina.Villamor, E.; Monserrat Aranda, C.; Del Río, L.; Romero-Martín, J.; Rupérez Moreno, MJ. (2020). Prediction of osteoporotic hip fracture in postmenopausal women through patient-specific FE analyses and machine learning. Computer Methods and Programs in Biomedicine. 193:1-11. https://doi.org/10.1016/j.cmpb.2020.105484S111193Holt, G., Smith, R., Duncan, K., Hutchison, J. D., & Reid, D. (2009). Changes in population demographics and the future incidence of hip fracture. 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