Radiographic patterns of osteoporotic proximal humerus fractures

The objectives of the study were: a) to identify osteoporotic proximal humerus fractures in a large consecutive series of patients; b) to identify radiographic fracture patterns among osteoporotic and non-osteoporotic proximal humerus fractures; and c) to calculate intra- and inter-observer reliability of assessment of osteoporosis and of radiographic fracture patterns

Panoramic Radiomorphometric Indices of the Mandible in an Iranian Population

Objectives: Radiomorphometric indices measured on simple radiographic images can be used for qualitative and quantitative assessment of bone. Panoramic radiographic imaging can therefore assist dentists in early detection of osteoporosis in patients. Considering the effect of ethnic and environmental factors on development of osteoporosis and absence of normal values of radiomorphometric indices for the Iranian population, which results in use of normal values of other ethnicities for determining the presence or absence of osteoporosis in patients, the present study was conducted to evaluate radiomorphometric indices in patients presenting to a private oral and maxillofacial radiology clinic in Rafsanjan, Iran.Methods: The present cross-sectional study examined 385 eligible patients who met the inclusion criteria. Their demographic characteristics including age, gender and level of education were recorded. Their panoramic radiographic data, including radiomorphometric indices such as mandibular cortical index (MCI), antegonial index (AI), and gonial index (GI), were evaluated on each image. Multiple logistic regression was used to evaluate the effect of variables on mandibular bone radiomorphometric indices.  P-value less than 0.05 was considered statistically significant.Results: The mean AI and GI were 2.19±0.56 and 0.87±0.31mm, respectively. In assessment of MCI, since none of the patients fell under the C3 category, all patients were categorized as C1 or C2. Assessment of demographic variables and radiomorphometric indices showed that age had a significant relationship with MCI and AI while gender had a significant relationship with GI.Conclusion: Radiomorphometric indices can be used to assess the risk of osteoporosis in patients

Synchrotron Radiation Micro-CT Imaging of Bone Tissue

International audienc

Quantitative imaging techniques for the assessment of osteoporosis and sarcopenia

Bone and muscle are two deeply interconnected organs and a strong relationship between them exists in their development and maintenance. The peak of both bone and muscle mass is achieved in early adulthood, followed by a progressive decline after the age of 40. The increase in life expectancy in developed countries resulted in an increase of degenerative diseases affecting the musculoskeletal system. Osteoporosis and sarcopenia represent a major cause of morbidity and mortality in the elderly population and are associated with a significant increase in healthcare costs. Several imaging techniques are currently available for the non-invasive investigation of bone and muscle mass and quality. Conventional radiology, dual energy X-ray absorptiometry (DXA), computed tomography (CT), magnetic resonance imaging (MRI) and ultrasound often play a complementary role in the study of osteoporosis and sarcopenia, depicting different aspects of the same pathology. This paper presents the different imaging modalities currently used for the investigation of bone and muscle mass and quality in osteoporosis and sarcopenia with special emphasis on the clinical applications and limitations of each technique and with the intent to provide interesting insights into recent advances in the field of conventional imaging, novel high-resolution techniques and fracture risk

Panoramic radiograph analyses for early detection of osteoporosis in the population of Northern Norway

Osteoporosis is a chronic disease affecting bone tissue that may lead to fractures from minor accidents. Roughly 20% of females and 6 % of males have osteoporosis after age 50, but the disease might be present at a younger age. Early diagnosis is challenging because the disease has no symptoms. Dental radiography is a frequent examination that might be useful for early osteoporosis screening at dental clinics. This thesis explores the utility of radiomorphometric indices manually measured on panoramic radiographs and the feasibility of fully automated radiomorphometric indices for osteoporosis screening in Norwegian males and females. The data from the seventh survey of the Tromsø study (Tromsø7) were used. Participants aged 40 and older were examined with dental panoramic radiographs and dual-energy x-ray absorptiometry at the femoral neck. Other demographic, health, and lifestyle data were collected in questionnaires. Mandibular cortical width and shape were assessed. Thin ( ≤ 3 mm) and severely eroded cortex could differentiate osteoporotic from non-osteoporotic females. Combining mandibular cortical width and shape with Fracture Risk Assessment (FRAX) score improved their diagnostic efficacy. T-score was the strongest predictor of mandibular cortical morphology among other factors in females. In males, the T-score was weakly associated with cortical shape, while the efficacy estimates for radiomorphometric indices were inconclusive. The reproducibility of the manually measured indices was suboptimal. Nevertheless, developing a fully automated algorithm for measuring MCW was feasible. Its first step, localization of mental foramen, was best performed by EfficientDet neural network with an accuracy of 79%. To conclude, radiomorphometric indices might be as useful as existing risk-factor-based tools for osteoporosis screening in females, and their combination with the FRAX score has superior diagnostic efficacy. Future extensive studies should further explore the performance of fully automated radiomorphometric indices.Osteoporose er en kronisk sykdom som påvirker beinvev og kan føre til brudd fra mindre ulykker. Omtrent 20% av kvinner og 6% av menn har osteoporose etter fylte 50 år, men sykdommen kan også forekomme i en yngre alder. Tidlig diagnose er utfordrende fordi sykdommen ikke har noen symptomer. Tannrøntgenundersøkelse er en vanlig prosedyre på tannklinikk og kan være nyttig for tidlig osteoporosescreening. Denne avhandlingen utforsker nytten av radiomorfometriske indekser som måles manuelt på panoramarøntgen og gjennomførbarheten av fullt automatiserte radiomorfometriske indekser for osteoporosescreening hos norske menn og kvinner. Data fra den sjuende Tromsøundersøkelsen (Tromsø7) ble brukt. Deltakere i alderen 40 år og eldre ble undersøkt med panorarøntgen og dual-energy x-ray absorptiometry ved lårhalsen. Andre demografiske, helse- og livsstils data ble innsamlet gjennom et spørreskjema. Mandibulær kortikal bredde og erosjon ble vurdert. Tynn (≤ 3 mm) og alvorlig erodert korteks kan skille osteoporotiske kvinner fra ikke-osteoporotiske kvinner. Ved å kombinere mandibulær kortikal bredde og erosjon med Fracture Risk Assessment (FRAX) score forbedret deres diagnostiske egenskaper. T-score var den sterkeste prediktoren for mandibulær kortikal morfologi blant andre faktorer hos kvinner. Hos menn var T-score svakt assosiert med kortikal erosjon, mens diagnostiske egenskaper for radiomorfometriske indekser var uklare. Reproduserbarheten til de manuelt målte indeksene var suboptimal. Likevel var det mulig å utvikle en fullt automatisert algoritme for måling av mandibulær kortikal bredde. Det første trinnet, lokalisering av foramen mentale, ble best utført av EfficientDet nevrale nettverk med en nøyaktighet på 79%. For å konkludere kan radiomorfometriske indekser være like nyttige som eksisterende risikofaktorbaserte verktøy for osteoporosescreening hos kvinner, og deres kombinasjon med FRAX-scoren viser bedre diagnostiske egenskaper enn radiomorfometriske indeksene alene. Fremtidige omfattende studier bør ytterligere utforske ytelsen til fullt automatiserte radiomorfometriske indekser

Deep Learning for Osteoporosis Classification Using Hip Radiographs and Patient Clinical Covariates

This study considers the use of deep learning to diagnose osteoporosis from hip radiographs, and whether adding clinical data improves diagnostic performance over the image mode alone. For objective labeling, we collected a dataset containing 1131 images from patients who underwent both skeletal bone mineral density measurement and hip radiography at a single general hospital between 2014 and 2019. Osteoporosis was assessed from the hip radiographs using five convolutional neural network (CNN) models. We also investigated ensemble models with clinical covariates added to each CNN. The accuracy, precision, recall, specificity, negative predictive value (npv), F1 score, and area under the curve (AUC) score were calculated for each network. In the evaluation of the five CNN models using only hip radiographs, GoogleNet and EfficientNet b3 exhibited the best accuracy, precision, and specificity. Among the five ensemble models, EfficientNet b3 exhibited the best accuracy, recall, npv, F1 score, and AUC score when patient variables were included. The CNN models diagnosed osteoporosis from hip radiographs with high accuracy, and their performance improved further with the addition of clinical covariates from patient records

Hand X-ray absorptiometry for measurement of bone mineral density on a slot-scanning X-ray imaging system

Includes bibliographical references.Bone mineral density (BMD) is an indicator of bone strength. While femoral and spinal BMDs are traditionally used in the management of osteoporosis, BMD at peripheral sites such as the hand has been shown to be useful in evaluating fracture risk for axial sites. These peripheral locations have been suggested as alternatives to the traditional sites for BMD measurement. Dual-energy X-ray absorptiometry (DXA) is the gold standard for measuring BMD due to low radiation dose, high accuracy and proven ability to evaluate fracture risk. Computed digital absorptiometry (CDA) has also been shown to be very effective at measuring the bone mass in hand bones using an aluminium step wedge as a calibration reference. In this project, the aim was to develop algorithm s for accurate measurement of BMD in hand bones on a slot - scanning digital radiography system. The project assess e d the feasibility of measuring bone mineral mass in hand bones using CDA on the current system. Images for CDA - based measurement were acquired using the default settings on the system for a medium sized patient. A method for automatic processing of the hand images to detect the aluminium step wedge, included in the scan for calibration, was developed and the calibration accuracy of the step wedge was evaluated. The CDA method was used for computation of bone mass with units of equivalent aluminium thickness (mmA1). The precision of the method was determined by taking three measurements in each of 1 6 volunteering subjects and computing the root - mean - square coefficient of variation (CV) of the measurements. The utility of the method was assessed by taking measurements of excised bones and assessing the correlation between the measured bone mass and ash weight obtained by incinerating the bones. The project also assessed the feasibility of implementing a DXA technique using two detectors in a slot-scanning digital radiography system to acquire dual-energy X-ray images for measuring areal and volumetric BMD of the middle phalanx of the middle finger. The dual-energy images were captured in two consecutive scans. The first scan captured the low- energy image using the detector in its normal set-up. The second scan captured the high- energy image with the detector modified to include an additional scintillator to simulate the presence of a second detector that would capture the low-energy image in a two-detector system. Scan parameters for acquisition of the dual-energy images were chosen to optimise spectral separation, entrance dose and image quality. Simulations were carried out to evaluate the spectral separation of the low- and high-energy spectra

A shape analysis approach to prediction of bone stiffness using FEXI

The preferred method of assessing the risk of an osteoporosis related fracture is currently a measure of bone mineral density (BMD) by dual energy X-ray absorptiometry (DXA). However, other factors contribute to the overall risk of fracture, including anatomical geometry and the spatial distribution of bone. Finite element analysis can be performed in both two and three dimensions, and predicts the deformation or induced stress when a load is applied to a structure (such as a bone) of defined material composition and shape. The simulation of a mechanical compression test provides a measure of whole bone stiffness (N mm−1). A simulation system was developed to study the sensitivity of BMD, 3D and 2D finite element analysis to variations in geometric parameters of a virtual proximal femur model. This study demonstrated that 3D FE and 2D FE (FEXI) were significantly more sensitive to the anatomical shape and composition of the proximal femur than conventional BMD. The simulation approach helped to analyse and understand how variations in geometric parameters affect the stiffness and hence strength of a bone susceptible to osteoporotic fracture. Originally, the FEXI technique modelled the femur as a thin plate model of an assumed constant depth for finite element analysis (FEA). A better prediction of tissue depth across the bone, based on its geometry, was required to provide a more accurate model for FEA. A shape template was developed for the proximal femur to provide this information for the 3D FE analysis. Geometric morphometric techniques were used to procure and analyse shape information from a set of CT scans of excised human femora. Generalized Procrustes Analysis and Thin Plate Splines were employed to analyse the data and generate a shape template for the proximal femur. 2D Offset and Depth maps generated from the training set data were then combined to model the three-dimensional shape of the bone. The template was used to predict the three-dimensional bone shape from a 2D image of the proximal femur procured through a DXA scan. The error in the predicted 3D shape was measured as the difference in predicted and actual depths at each pixel. The mean error in predicted depths was found to be 1.7mm compared to an average bone depth of 34mm. 3D FEXI analysis on the predicted 3D bone along with 2D FEXI for a stance loading condition and BMD measurement were performed based on 2D radiographic projections of the CT scans and compared to bone stiffness results obtained from finite element analysis of the original 3D CT scans. 3D FEXI provided a significantly higher correlation (R2 = 0.85) with conventional CT derived 3D finite element analysis than achieved with both BMD (R2 = 0.52) and 2D FEXI (R2 = 0.44)