Metabolic characteristics of transmembrane prolyl 4-hydroxylase (P4H-TM) deficient mice.

Transmembrane prolyl 4-hydroxylase (P4H-TM) is an enigmatic enzyme whose cellular function and primary substrate remain to be identified. Its loss-of-function mutations cause a severe neurological HIDEA syndrome with hypotonia, intellectual disability, dysautonomia and hypoventilation. Previously, P4H-TM deficiency in mice was associated with reduced atherogenesis and lower serum triglyceride levels. Here, we characterized the glucose and lipid metabolism of P4h-tm-/- mice in physiological and tissue analyses. P4h-tm-/- mice showed variations in 24-h oscillations of energy expenditure, VO2 and VCO2 and locomotor activity compared to wild-type (WT) mice. Their rearing activity was reduced, and they showed significant muscle weakness and compromised coordination. Sedated P4h-tm-/- mice had better glucose tolerance, lower fasting insulin levels, higher fasting lactate levels and lower fasting free fatty acid levels compared to WT. These alterations were not present in conscious P4h-tm-/- mice. Fasted P4h-tm-/- mice presented with faster hepatic glycogenolysis. The respiratory rate of conscious P4h-tm-/- mice was significantly lower compared to the WT, the decrease being further exacerbated by sedation and associated with acidosis and a reduced ventilatory response to both hypoxia and hypercapnia. P4H-TM deficiency in mice is associated with alterations in whole-body energy metabolism, day-night rhythm of activity, glucose homeostasis and neuromuscular and respiratory functions. Although the underlying mechanism(s) are not yet fully understood, the phenotype appears to have neurological origins, controlled by brain and central nervous system circuits. The phenotype of P4h-tm-/- mice recapitulates some of the symptoms of HIDEA patients, making this mouse model a valuable tool to study and develop tailored therapies.

Lack of collagen XVIII leads to lipodystrophy and perturbs hepatic glucose and lipid homeostasis.

KEY POINTS: Extracellular matrix is highly remodelled in obesity and associates with the development of metabolic disorders, such as insulin resistance. Previously, we have shown that the lack of specific collagen XVIII isoforms impairs adipocyte differentiation in mice. Here, we show that mice lacking the medium and long isoforms of collagen XVIII develop insulin resistance and glucose intolerance and show elevated serum triglycerides and fat accumulation in the liver. We report that collagen XVIII-deficient mice have increased heat production at low temperatures. These results reveal a new role for collagen XVIII in the regulation of glucose and lipid metabolism, and they expand the understanding of the development of metabolic disorders. ABSTRACT: Liver and adipose tissues play important roles in the regulation of systemic glucose and lipid metabolism. Extracellular matrix synthesis and remodelling are significantly altered in these tissues in obesity and type 2 diabetes. Collagen XVIII is a ubiquitous extracellular matrix component, and it occurs in three isoforms which differ in terms of molecular size, domain structure and tissue distribution. We recently showed that, in mice, the lack of collagen XVIII, and especially its medium and long isoforms, leads to reduced adiposity and dyslipidaemia. To address the metabolic consequences of these intriguing observations, we assessed whole-body glucose homeostasis in mice challenged with a high-fat diet and in normal physiological conditions. We observed that, in the high caloric diet, the overall adiposity was decreased by 30%, serum triglyceride values were threefold higher and the steatotic area in liver was twofold larger in collagen XVIII knockout mice compared with controls. We demonstrated that mice lacking either all three collagen XVIII isoforms, or specifically, the medium and long isoforms develop insulin resistance and glucose intolerance. Furthermore, we found that ablation of collagen XVIII leads to increased heat production in low temperatures and to reduction of the high blood triglyceride levels of the knockout mice to the level of wild-type mice. Our data indicate that collagen XVIII plays a role in the regulation of glucose tolerance, insulin sensitivity and lipid homeostasis, principally through its ability to regulate the expansion of the adipose tissue. These findings advance the understanding of metabolic disorders.

Differences in tibial subchondral bone structure evaluated using plain radiographs between knees with and without cartilage damage or bone marrow lesions - the Oulu Knee Osteoarthritis study.

OBJECTIVES: To investigate whether subchondral bone structure from plain radiographs is different between subjects with and without articular cartilage damage or bone marrow lesions (BMLs). METHODS: Radiography-based bone structure was assessed from 80 subjects with different stages of knee osteoarthritis using entropy of Laplacian-based image (ELap) and local binary patterns (ELBP), homogeneity index of local angles (HIAngles,mean), and horizontal (FDHor) and vertical fractal dimensions (FDVer). Medial tibial articular cartilage damage and BMLs were scored using the magnetic resonance imaging osteoarthritis knee score. Level of statistical significance was set to p < 0.05. RESULTS: Subjects with medial tibial cartilage damage had significantly higher FDVer and ELBP as well as lower ELap and HIAngles,mean in the medial tibial subchondral bone region than subjects without damage. FDHor, FDVer, and ELBP were significantly higher, whereas ELap and HIAngles,mean were lower in the medial trabecular bone region. Subjects with medial tibial BMLs had significantly higher FDVer and ELBP as well as lower ELap and HIAngles,mean in medial tibial subchondral bone. FDHor, FDVer, and ELBP were higher, whereas ELap and HIAngles,mean were lower in medial trabecular bone. CONCLUSIONS: Our results support the use of bone structural analysis from radiographs when examining subjects with osteoarthritis or at risk of having it. KEY POINTS: • Knee osteoarthritis causes changes in articular cartilage and subchondral bone • Magnetic resonance imaging is a comprehensive imaging modality for knee osteoarthritis • Radiography-based bone structure analysis can provide additional information of osteoarthritic subjects.

Assessment of risk of femoral neck fracture with radiographic texture parameters: a retrospective study.

PURPOSE: To investigate whether femoral neck fracture can be predicted retrospectively on the basis of clinical radiographs by using the combined analysis of bone geometry, textural analysis of trabecular bone, and bone mineral density (BMD). MATERIALS AND METHODS: Formal ethics committee approval was obtained for the study, and all participants gave informed written consent. Pelvic radiographs and proximal femur BMD measurements were obtained in 53 women aged 79-82 years in 2006. By 2012, 10 of these patients had experienced a low-impact femoral neck fracture. A Laplacian-based semiautomatic custom algorithm was applied to the radiographs to calculate the texture parameters along the trabecular fibers in the lower neck area for all subjects. Intra- and interobserver reproducibility was calculated by using the root mean square average coefficient of variation to evaluate the robustness of the method. RESULTS: The best predictors of hip fracture were entropy (P = .007; reproducibility coefficient of variation < 1%), the neck-shaft angle (NSA) (P = .017), and the BMD (P = .13). For prediction of fracture, the area under the receiver operating characteristic curve was 0.753 for entropy, 0.608 for femoral neck BMD, and 0.698 for NSA. The area increased to 0.816 when entropy and NSA were combined and to 0.902 when entropy, NSA, and BMD were combined. CONCLUSION: Textural analysis of pelvic radiographs enables discrimination of patients at risk for femoral neck fracture, and our results show the potential of this conventional imaging method to yield better prediction than that achieved with dual-energy x-ray absorptiometry-based BMD. The combination of the entropy parameter with NSA and BMD can further enhance predictive accuracy.

A Multimodal Dataset for Automatic Edge-AI Cough Detection.

Counting the number of times a patient coughs per day is an essential biomarker in determining treatment efficacy for novel antitussive therapies and personalizing patient care. Automatic cough counting tools must provide accurate information, while running on a lightweight, portable device that protects the patient's privacy. Several devices and algorithms have been developed for cough counting, but many use only error-prone audio signals, rely on offline processing that compromises data privacy, or utilize processing and memory-intensive neural networks that require more hardware resources than can fit on a wearable device. Therefore, there is a need for wearable devices that employ multimodal sensors to perform accurate, privacy-preserving, automatic cough counting algorithms directly on the device in an edge Artificial Intelligence (edge-AI) fashion. To advance this research field, we contribute the first publicly accessible cough counting dataset of multimodal biosignals. The database contains nearly 4 hours of biosignal data, with both acoustic and kinematic modalities, covering 4,300 annotated cough events from 15 subjects. Furthermore, a variety of non-cough sounds and motion scenarios mimicking daily life activities are also present, which the research community can use to accelerate machine learning (ML) algorithm development. A technical validation of the dataset reveals that it represents a wide variety of signal-to-noise ratios, which can be expected in a real-life use case, as well as consistency across experimental trials. Finally, to demonstrate the usability of the dataset, we train a simple cough vs non-cough signal classifier that obtains a 91% sensitivity, 92% specificity, and 80% precision on unseen test subject data. Such edge-friendly AI algorithms have the potential to provide continuous ambulatory monitoring of the numerous chronic cough patients.

Equine Herpesvirus-1 Outbreak During a Show-Jumping Competition: A Clinical and Epidemiological Study.

A total of 752 horses were involved in the CES Valencia Spring Tour 2021. Due to an equine herpesvirus-1 (EHV-1) outbreak, the competition was cancelled and the site was locked down. The objective of this study was to describe epidemiological, clinical, diagnostic, and outcome data of the 160 horses remaining in Valencia. Clinical and quantitative polymerase chain reaction (qPCR) data were analysed for 60 horses in a retrospective case-control observational study. The risk of developing clinical manifestations was explored using a logistic regression approach. EHV-1 was detected by qPCR, genotyped as A2254 (ORF30) and isolated on cell culture. From the 60 horses, 50 (83.3%) showed fever, 30 horses (50%) showed no further signs and 20 (40%) showed neurological signs, with eight horses (16%) hospitalised, of which two died (3%). Stallions and geldings were six times more likely to develop EHV-1 infection compared to mares. Horses older than 9 years, or housed in the middle of the tent were more likely to develop EHV-1 myeloencephalopathy (EHM). These data show that for EHV-1 infection, the risk factor was male sex. For EHM the risk factors were age > 9-year old and location in the middle of the tent. These data highlight the crucial role of stable design, position, and ventilation in EHV-outbreaks. It also showed that PCR testing of the horses was important to manage the quarantine.

Thermographic findings related to facial pain - a survey of 40 subjects.

Objective: The purpose of this study was to assess how thermography findings relate painful symptoms and signs of temporomandibular disorders (TMD).Methods: Thermography, combined with chewing of paraffin wax, was performed on 40 subjects. The results were analyzed according to gender and pain-related TMD symptoms and clinical signs.Results: The overall temperatures after chewing were higher in TMD patients than in controls. For females, the most significant findings were the thermal increase between the relaxed state and subjects' state after chewing in temporal and temporomandibular joint (TMJ) regions. For males, all calculated parameters demonstrated a poor ability to discriminate TMD from controls.Conclusion: Thermography could be a potential tool in diagnostics of female TMD patients. The results suggest that the thermal information assessed in specific facial areas could help to discriminate TMD patients from non-TMD patients and could be used to quantify the pain associated with TMD.

Acoustic emissions and kinematic instability of the osteoarthritic knee joint: comparison with radiographic findings.

To evaluate the acoustic emissions (AE) and kinematic instability (KI) of the osteoarthritic (OA) knee joints, and to compare these signals to radiographic findings. Sixty-six female and 43 male participants aged 44-67 were recruited. On radiography, joint-space narrowing, osteophytes and Kellgren-Lawrence (KL) grade were evaluated. Based on radiography, 54 subjects (the study group) were diagnosed with radiographic OA (KL-grade ≥ 2) while the remaining 55 subjects (KL-grade < 2) formed the control group. AE and KI were recorded with a custom-made prototype and compared with radiographic findings using area-under-curve (AUC) and independent T-test. Predictive logistic regression models were constructed using leave-one-out cross validation. In females, the parameters reflecting consistency of the AE patterns during specific tasks, KI, BMI and age had a significant statistical difference between the OA and control groups (p = 0.001-0.036). The selected AE signals, KI, age and BMI were used to construct a predictive model for radiographic OA with AUC of 90.3% (95% CI 83.5-97.2%) which showed a statistical improvement of the reference model based on age and BMI, with AUC of 84.2% (95% CI 74.8-93.6%). In males, the predictive model failed to improve the reference model. AE and KI provide complementary information to detect radiographic knee OA in females.

A single intra-articular dose of vitamin D analog calcipotriol alleviates synovitis without adverse effects in rats.

1,25-dihydroxyvitamin-D3 and its derivatives have shown anti-arthritic and chondroprotective effects in experimental animal models with prophylactic dosing. The purpose of this preliminary study was to test the efficacy and safety of calcipotriol, vitamin D analog, as a treatment for a fully-developed knee arthritis in Zymosan-induced arthritis (ZIA) model. Forty 5-month-old male Sprague-Dawley rats were randomized into three arthritis groups and a non-arthritic control group with no injections (10 rats/group). A day after Zymosan (0.1 mg) had been administrated into the right knee joints, the same knees were injected with calcipotriol (0.1 mg/kg), dexamethasone (0.1 mg/kg) or vehicle in a 100 µl volume. The left control knees were injected with saline (PBS) on two consecutive days. All injections, blood sampling and measurements were performed under general anesthesia on days 0, 1, 3 and 8. Internal organs and knees were harvested on day 8 and the histology of the whole knees was assessed blinded. Joints treated with calcipotriol showed a milder histological synovitis than those treated with vehicle (p = 0.041), but there was no statistically significant difference between the dexamethasone and vehicle groups. The clinical severity of arthritis did not differ between the arthritis groups measured by body temperature, swelling of the knee, thermal imaging, clinical scoring or cytokine levels on days 1, 3 and 8. Weight loss was bigger in rats treated with dexamethasone, propably due to loss of appetite,compared to other arthritis groups on days 2-3 (p<0.05). Study drugs did not influence serum calcium ion and glucose levels. Taken together, this preliminary study shows that a single intra-articular injection of calcipotriol reduces histological grade of synovitis a week after the local injection, but dexamethasone did not differ from the vehicle. Calcipotriol may have an early disease-modifying effect in the rat ZIA model without obvious side effects.

Structural asymmetry between the hips and its relation to experimental fracture type.

Experimental analysis with paired femurs provides the opportunity to study within-person differences in fracture type and associated structural side differences. We hypothesized that different fracture types in the hips of a subject are associated with structural asymmetry. Bone mineral density (BMD) and structural measurements of paired cadaver femurs (32 females, 24 males) were performed before mechanical testing in a side-impact configuration. Fractures were classified (cervical or trochanteric) and differences in structural parameters, BMD, and failure load were evaluated between the left and right hips as well as between experimental fracture types. We observed larger dimensions (P < 0.05-0.01), thicker cortices (P < 0.05-0.001), and a smaller femoral shaft diameter (FSD) (P < 0.01) in the left hip than in the right. Seventeen pairs (30.4%) had trochanteric fractures on one side and cervical on the contralateral side. The asymmetric trochanteric fracture side had a higher head/neck diameter ratio (HD/ND) (P < 0.05) and a trend toward a lower neck-shaft angle (NSA) (P = 0.066) than its collateral cervical side in females and a lower HD and higher FSD (P < 0.05) in males. In females, asymmetric fracture cases displayed lower NSA (P < 0.001) and HD/ND (P < 0.01) than symmetric cervical ones. In males, asymmetric fracture cases showed larger dimensions than the other groups (P < 0.05-0.01). BMD increased from symmetric cervical to asymmetric and then to symmetric trochanteric cases (P < 0.05-0.01), with the experimental failure load showing a similar trend. In conclusion, intrasubject structural asymmetry is associated with asymmetric fracture types. Asymmetry should be considered when using the opposite side as control in clinical studies.

Bone Density and Texture from Minimally Post-Processed Knee Radiographs in Subjects with Knee Osteoarthritis.

Plain radiography is the most common modality to assess the stage of osteoarthritis. Our aims were to assess the relationship of radiography-based bone density and texture between radiographs with minimal and clinical post-processing, and to compare the differences in bone characteristics between controls and subjects with knee osteoarthritis or medial tibial bone marrow lesions (BMLs). Tibial bone density and texture was evaluated from radiographs with both minimal and clinical post-processing in 109 subjects with and without osteoarthritis. Bone texture was evaluated using fractal signature analysis. Significant correlations (p < 0.001) were found in all regions (between 0.94 and 0.97) for calibrated bone density between radiographs with minimal and clinical post-processing. Correlations varied between 0.51 and 0.97 (p < 0.001) for FDVer texture parameter and between - 0.10 and 0.97 for FDHor. Bone density and texture were different (p < 0.05) between controls and subjects with osteoarthritis or BMLs mainly in medial tibial regions. When classifying healthy and osteoarthritic subjects using a machine learning-based elastic net model with bone characteristics, area under the receiver operating characteristics (ROCAUC) curve was 0.77. For classifying controls and subjects with BMLs, ROCAUC was 0.85. In conclusion, differences in bone density and texture can be assessed from knee radiographs when using minimal post-processing.

Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Data.

Knee osteoarthritis (OA) is the most common musculoskeletal disease without a cure, and current treatment options are limited to symptomatic relief. Prediction of OA progression is a very challenging and timely issue, and it could, if resolved, accelerate the disease modifying drug development and ultimately help to prevent millions of total joint replacement surgeries performed annually. Here, we present a multi-modal machine learning-based OA progression prediction model that utilises raw radiographic data, clinical examination results and previous medical history of the patient. We validated this approach on an independent test set of 3,918 knee images from 2,129 subjects. Our method yielded area under the ROC curve (AUC) of 0.79 (0.78-0.81) and Average Precision (AP) of 0.68 (0.66-0.70). In contrast, a reference approach, based on logistic regression, yielded AUC of 0.75 (0.74-0.77) and AP of 0.62 (0.60-0.64). The proposed method could significantly improve the subject selection process for OA drug-development trials and help the development of personalised therapeutic plans.

A Survey on Computer Vision for Assistive Medical Diagnosis From Faces.

Automatic medical diagnosis is an emerging center of interest in computer vision as it provides unobtrusive objective information on a patient's condition. The face, as a mirror of health status, can reveal symptomatic indications of specific diseases. Thus, the detection of facial abnormalities or atypical features is at upmost importance when it comes to medical diagnostics. This survey aims to give an overview of the recent developments in medical diagnostics from facial images based on computer vision methods. Various approaches have been considered to assess facial symptoms and to eventually provide further help to the practitioners. However, the developed tools are still seldom used in clinical practice, since their reliability is still a concern due to the lack of clinical validation of the methodologies and their inadequate applicability. Nonetheless, efforts are being made to provide robust solutions suitable for healthcare environments, by dealing with practical issues such as real-time assessment or patients positioning. This survey provides an updated collection of the most relevant and innovative solutions in facial images analysis. The findings show that with the help of computer vision methods, over 30 medical conditions can be preliminarily diagnosed from the automatic detection of some of their symptoms. Furthermore, future perspectives, such as the need for interdisciplinary collaboration and collecting publicly available databases, are highlighted.

Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach.

Knee osteoarthritis (OA) is the most common musculoskeletal disorder. OA diagnosis is currently conducted by assessing symptoms and evaluating plain radiographs, but this process suffers from subjectivity. In this study, we present a new transparent computer-aided diagnosis method based on the Deep Siamese Convolutional Neural Network to automatically score knee OA severity according to the Kellgren-Lawrence grading scale. We trained our method using the data solely from the Multicenter Osteoarthritis Study and validated it on randomly selected 3,000 subjects (5,960 knees) from Osteoarthritis Initiative dataset. Our method yielded a quadratic Kappa coefficient of 0.83 and average multiclass accuracy of 66.71% compared to the annotations given by a committee of clinical experts. Here, we also report a radiological OA diagnosis area under the ROC curve of 0.93. Besides this, we present attention maps highlighting the radiological features affecting the network decision. Such information makes the decision process transparent for the practitioner, which builds better trust toward automatic methods. We believe that our model is useful for clinical decision making and for OA research; therefore, we openly release our training codes and the data set created in this study.

Subchondral bone histology and grading in osteoarthritis.

OBJECTIVE: Osteoarthritis (OA) has often regarded as a disease of articular cartilage only. New evidence has shifted the paradigm towards a system biology approach, where also the surrounding tissue, especially bone is studied more vigorously. However, the histological features of subchondral bone are only poorly characterized in current histological grading scales of OA. The aim of this study is to specifically characterize histological changes occurring in subchondral bone at different stages of OA and propose a simple grading system for them. DESIGN: 20 patients undergoing total knee replacement surgery were randomly selected for the study and series of osteochondral samples were harvested from the tibial plateaus for histological analysis. Cartilage degeneration was assessed using the standardized OARSI grading system, while a novel four-stage grading system was developed to illustrate the changes in subchondral bone. Subchondral bone histology was further quantitatively analyzed by measuring the thickness of uncalcified and calcified cartilage as well as subchondral bone plate. Furthermore, internal structure of calcified cartilage-bone interface was characterized utilizing local binary patterns (LBP) based method. RESULTS: The histological appearance of subchondral bone changed drastically in correlation with the OARSI grading of cartilage degeneration. As the cartilage layer thickness decreases the subchondral plate thickness and disorientation, as measured with LBP, increases. Calcified cartilage thickness was highest in samples with moderate OA. CONCLUSION: The proposed grading system for subchondral bone has significant relationship with the corresponding OARSI grading for cartilage. Our results suggest that subchondral bone remodeling is a fundamental factor already in early stages of cartilage degeneration.

Association between subchondral bone structure and osteoarthritis histopathological grade.

Despite increasing evidence that subchondral bone contributes to osteoarthritis (OA) pathogenesis, little is known about local changes in bone structure compared to cartilage degeneration. This study linked structural adaptation of subchondral bone with histological OA grade. Twenty-five osteochondral samples of macroscopically different degeneration were prepared from tibiae of 14 patients. Samples were scanned with micro-computed tomography (µCT) and both conventional structural parameters and novel 3D parameters based on local patterns were analyzed from the subchondral plate and trabecular bone. Subsequently, samples were processed for histology and evaluated for OARSI grade. Each bone parameter and OARSI grade was compared to assess structural adaptation of bone with OA severity. In addition, thicknesses of cartilage, calcified cartilage, and subchondral plate were analyzed from histological sections and compared with subchondral bone plate thickness from µCT. With increasing OARSI grade, the subchondral plate became thicker along with decreased specific bone surface, while there was no change in tissue mineral density. Histological analysis showed that subchondral plate thickness from µCT also includes calcified cartilage. Entropy of local patterns increased with OA severity, reflecting higher tissue heterogeneity. In the trabecular compartment, bone volume fraction and both trabecular thickness and number increased with OARSI grade while trabecular separation and structure model index decreased. Also, elevation of local patterns became longitudinal in the subchondral plate and axial transverse in trabecular bone with increasing OARSI grade. This study demonstrates the possibility of radiological assessment of OA severity by structural analysis of bone. © 2016 The Authors. Journal of Orthopaedic Research Published by Wiley Periodicals, Inc. J Orthop Res 35:785-792, 2017.

Correlation of Subchondral Bone Density and Structure from Plain Radiographs with Micro Computed Tomography Ex Vivo.

Osteoarthritis causes changes in the subchondral bone structure and composition. Plain radiography is a cheap, fast, and widely available imaging method. Bone tissue can be well seen from plain radiograph, which however is only a 2D projection of the actual 3D structure. Therefore, the aim was to investigate the relationship between bone density- and structure-related parameters from 2D plain radiograph and 3D bone parameters assessed from micro computed tomography (µCT) ex vivo. Right tibiae from eleven cadavers without any diagnosed joint disease were imaged using radiography and with µCT. Bone density- and structure-related parameters were calculated from four different locations from the radiographs of proximal tibia and compared with the volumetric bone microarchitecture from the corresponding regions. Bone density from the plain radiograph was significantly related with the bone volume fraction (r = 0.86; n = 44; p < 0.01). Mean homogeneity index for orientation of local binary patterns (HI(angle,mean)) and fractal dimension of vertical structures (FD(Ver)) were related (p < 0.01) with connectivity density (HI(angle,mean): r = -0.73, FD(Ver): r = 0.69) and trabecular separation (HI(angle,mean): r = 0.73, FD(Ver): r = -0.70) when all ROIs were pooled together (n = 44). Bone density and structure in tibia from standard clinically available 2D radiographs are significantly correlated with true 3D microstructure of bone.

A novel methodology for generating 3D finite element models of the hip from 2D radiographs.

Finite element (FE) modelling has been proposed as a tool for estimating fracture risk and patient-specific FE models are commonly based on computed tomography (CT). Here, we present a novel method to automatically create personalised 3D models from standard 2D hip radiographs. A set of geometrical parameters of the femur were determined from seven a-p hip radiographs and compared to the 3D femoral shape obtained from CT as training material; the error in reconstructing the 3D model from the 2D radiographs was assessed. Using the geometry parameters as the input, the 3D shape of another 21 femora was built and meshed, separating a cortical and trabecular compartment. The material properties were derived from the homogeneity index assessed by texture analysis of the radiographs, with focus on the principal tensile and compressive trabecular systems. The ability of these FE models to predict failure load as determined by experimental biomechanical testing was evaluated and compared to the predictive ability of DXA. The average reconstruction error of the 3D models was 1.77 mm (±1.17 mm), with the error being smallest in the femoral head and neck, and greatest in the trochanter. The correlation of the FE predicted failure load with the experimental failure load was r(2)=64% for the reconstruction FE model, which was significantly better (p<0.05) than that for DXA (r(2)=24%). This novel method for automatically constructing a patient-specific 3D finite element model from standard 2D radiographs shows encouraging results in estimating patient-specific failure loads.

Trabecular homogeneity index derived from plain radiograph to evaluate bone quality.

Radiographic texture analysis has been developed lately to improve the assessment of bone architecture as a determinant of bone quality. We validate here an algorithm for the evaluation of trabecular homogeneity index (HI) in the proximal femur from hip radiographs, with a focus on the impact of the principal compressive system of the trabecular bone, and evaluate its correlation with femoral strength, bone mineral density (BMD), and volumetric trabecular structure parameters. A semiautomatic custom-made algorithm was applied to calculate the HI in the femoral neck and trochanteric areas from radiographs of 178 femoral bone specimens (mean age 79.3 ± 10.4 years). Corresponding neck region was selected in CT scans to calculate volumetric parameters of trabecular structure. The site-specific BMDs were assessed from dual-energy X-ray absorptiometry (DXA), and the femoral strength was experimentally tested in side-impact configuration. Regression analysis was performed between the HI and biomechanical femoral strength, BMD, and volumetric parameters. The correlation between HI and failure load was R(2) = 0.50; this result was improved to R(2) = 0.58 for cervical fractures alone. The discrimination of bones with high risk of fractures (load <3000 N) was similar for HI and BMD (AUC = 0.87). Regression analysis between the HIs versus site-specific BMDs yielded R(2) = 0.66 in neck area, R(2) = 0.60 in trochanteric area, and an overall of R(2) = 0.66 for the total hip. Neck HI and BMD correlated significantly with volumetric structure parameters. We present here a method to assess HI that can explain 50% of an experimental failure load and determines bones with high fracture risk with similar accuracy as BMD. The HI also had good correlation with DXA and computed tomography-derived data.

Ct-based finite element models can be used to estimate experimentally measured failure loads in the proximal femur.

The objective of this experimental finite element (FE) study was to assess the accuracy of a simulation model estimate of the experimentally measured fracture load of the proximal femur in a sideways fall. Sixty-one formalin-fixed cadaver femora (41 female and 20 male) aged 55-100 years (an average of 80 years) were scanned with a multi-detector CT scanner and were mechanically tested for failure in a sideways fall loading configuration. Twenty-one of these femurs were used for training purposes, and 40 femurs were used for validation purposes. The training set FE models were used to establish the strain threshold for the element failure criteria. Bi-linear elastoplastic FE analysis was performed based on the CT images. The validation set was used to estimate the fracture loads. The Drucker-Prager criterion was applied to determine the yielding and the maximum principal stress criteria and the minimum principal strain criteria for element failure in tension and in compression, respectively. The estimated fracture load values were highly correlated with the experimental data (r=0.931; p<0.001). The slope was 0.929, with an intercept of 258 N, which was not significantly different from 1 and 0, respectively. The study shows that it is possible to estimate the fracture load with relatively high accuracy in a sideways fall configuration by using the CT-based FE method. This method may therefore be applied for studying the biomechanical mechanisms of hip fractures.