Subtalar Joint Instability: Diagnosis and Conservative Treatment

Subtalar instability may be caused by various ligamentous injuries. Combined instability at the ankle and subtalar joint is not adequately diagnosed. Further, isolated subtalar instability is usually misdiagnosed which may lead to long term damage to the joint. Developing a non-invasive and clinically practical tool to diagnose subtalar joint instability would be an important asset. The ability of an ankle brace, a common treatment for hindfoot instability, to promote stability for the subtalar joint was not well established. The purposes of this study were to 1) assess the kinematics of the subtalar, ankle, and hindfoot in the presence of isolated subtalar instability; 2) investigate the effect of bracing in a calcaneofibular ligament (CFL) deficient foot and with a total rupture of the intrinsic ligaments; 3) implement an optimization method to determine the subtalar joint axis in vivo and apply this method in the diagnosis of subtalar joint instability. Kinematics from nine cadaveric feet were collected with the foot placed in neutral, dorsiflexion and plantarflexion. Motion was applied with and without a brace on an intact foot and after sequentially sectioning the CFL andthe intrinsic ligaments. A two-hinge joint optimization model was developed to approximate the ankle and subtalar joint axis during inversion based on the kinematics of the calcaneus and the tibia. The optimization determined subject-specific subtalar and ankle joint axis for each condition. Isolated CFL sectioning increased ankle joint inversion while sectioning the CFL and intrinsic ligaments affected subtalar joint stability. Additionally, examining the foot in dorsiflexion significantly reduced ankle and subtalar joint motion. The ankle brace limited inversion at both joints. The inclination and deviation angles of the optimized subtalar joint axis were similar to previous studies. The orientation of the subtalar and ankle joint axes did not change after ligament injury. The optimized subtalar and ankle axes were significantly different than the \u27true\u27 subtalar and ankle joint axes determined from inversion-eversion. Future work would improve the optimization to look at the change in the angle of rotation around the optimized subtalar and ankle joint axes to detect subtalar joint instability

Prediction of the rib cage volume and thorax density from anthropometric data.

Rib cage volume and thorax density are useful both for clinical issues (Sverzellati et al. 2013) and for multi-segmental body modeling. In the latter case mean density is generally assumed, using data from Dempster et al (Dempster 1955), which could be over-evaluated due to the lack of consideration of lung density. Bi-planar X-Ray system (Dubousset et al. 2010) combined with 3D reconstruction allows to get both the rib cage and the external body shape. The aim of this study is to estimate the rib cage volume compared to the thorax volume and to propose a refined thorax density estimation

Determination of a new uniform thorax density representative of the living population from 3D external body shape modeling.

Body segment parameters (BSP) for each body's segment are needed for biomechanical analysis. To provide population-specific BSP, precise estimation of body's segments volume and density are needed. Widely used uniform densities, provided by cadavers' studies, did not consider the air present in the lungs when determining the thorax density. The purpose of this study was to propose a new uniform thorax density representative of the living population from 3D external body shape modeling. Bi-planar X-ray radiographies were acquired on 58 participants allowing 3D reconstructions of the spine, rib cage and human body shape. Three methods of computing the thorax mass were compared for 48 subjects: (1) the Dempster Uniform Density Method, currently in use for BSPs calculation, using Dempster density data, (2) the Personalized Method using full-description of the thorax based on 3D reconstruction of the rib cage and spine and (3) the Improved Uniform Density Method using a uniform thorax density resulting from the Personalized Method. For 10 participants, comparison was made between the body mass obtained from a force-plate and the body mass computed with each of the three methods. The Dempster Uniform Density Method presented a mean error of 4.8% in the total body mass compared to the force-plate vs 0.2% for the Personalized Method and 0.4% for the Improved Uniform Density Method. The adjusted thorax density found from the 3D reconstruction was 0.74 g/cm 3 for men and 0.73 g/cm for women instead of the one provided by Dempster (0.92 g/cm 3 ), leading to a better estimate of the thorax mass and body mass

Vertebral strength prediction from Bi-Planar dual energy x-ray absorptiometry under anterior compressive force using a finite element model: An in vitro study

Finite element models (FEM) derived from qCT-scans were developed as a clinical tool to evaluate vertebral strength. However, the high dose, time and cost of qCT-scanner are limitations for routine osteoporotic diagnosis. A new approach considers using bi-planar dual energy (BP2E) X-rays absorptiometry to build vertebral FEM using synchronized sagittal and frontal plane radiographs. The purpose of this study was to compare the performance of the areal bone mineral density (aBMD) measured from DXA, qCT-based FEM and BP2E-based FEM in predicting experimental vertebral strength. Twenty eight vertebrae from eleven lumbar spine segments were imaged with qCT, DXA and BP2E X-rays before destructively tested in anterior compression. FEM were built based on qCT and BP2E images for each vertebra. Subject-specific FEM were built based on 1) the BP2E images using 3D reconstruction and volumetric BMD distribution estimation and 2) the qCT scans using slice by slice segmentation and voxel based calibration. Linear regression analysis was performed to find the best predictor for experimental vertebral strength (Fexpe); aBMD, modeled vertebral strength and vertebral stiffness. Areal BMD was moderately correlated with Fexpe (R2 = 0.74). FEM calculations of vertebral strength were highly to strongly correlated with Fexpe (R2 = 0.84, p < 0.001 for BP2E model and R2 = 0.95, p < 0.001 for qCT model). The results of this study suggest that aBMD accounted for only 74% of Fexpe variability while FE models accounted for at least 84%. For anterior compressive loading on isolated vertebral bodies, simplistic loading condition aimed to replicate anterior wedge fractures, both FEM were good predictors of Fexpe. Therefore FEM based on BP2E X-rays absorptiometry could be a good alternative to replace qCT-based models in the prediction of vertebral strength. However future work should investigate the performance of the BP2E-based model in vivo in discriminating patients with and without vertebral fracture in a prospective study.The authors would like to thank S. Persohn and M. Jeyasankar for contributing to mechanical testing. The authors would also thank Anabela Darbon, advanced research engineer at EOS Imaging, for EOS® dual energy acquisition and calibration. This work was supported by the Banque Publique d’Investissement through the dexEOS project part of the FUI14. The funding agencies had no role in the design and conduct of the study, in the collection, management, analysis and interpretation of the data, or in the preparation, review, or approval of the manuscript

A New Method To Determine Volumetric Bone Mineral Density From Bi-Planar Dual Energy Radiographs Using A Finite Element Model: An Ex-Vivo Study

Finite element models (FEMs) derived from QCT-scans were developed to evaluate vertebral strength but QCT scanners limitations are restrictive for routine osteoporotic diagnosis. A new approach considers using bi-planar dual energy (BP2E) X-rays absorptiometry to build vertebral FEM. The purpose was to propose a FEM based on BP2E absorptiometry and to compare the vertebral strength predicted from this model to a QCT-based FEM. About 46 vertebrae were QCT scanned and imaged with BP2E X-rays. Subject-specific vertebral geometry and bone material properties were obtained from both medical imaging techniques to build FEM for each vertebra. Vertebral body volumetric bone mineral density (vBMD) distribution and vertebral strength prediction from the BP2E-based FEM and the QCT-based FEM were compared. A statistical error of 7[Formula: see text]mg/cm3 with a RMSE of 9.6% and a [Formula: see text] of 0.83 were found in the vBMD distribution differences between the BP2E-based and qCT-based FEM. The average vertebral strength was 3321[Formula: see text][Formula: see text] and 3768[Formula: see text][Formula: see text][Formula: see text for the qCT-based and BP2E-based FEM, respectively, with a RMSE of 641[Formula: see text]N and [Formula: see text] of 0.92. This method was developed to estimate vBMD distribution in lumbar vertebrae from a pair of 2D-BMD images and demonstrated to be accurate to personalize the mechanical properties in vitro

Vertebral strength prediction under anterior compressive force using a finite element model for osteoporosis assessment

Vertebral fractures are one of the most common clinical manifestations with the major adverse consequences of osteoporosis as they usually occur under non-traumatic loading conditions. Height loss, back pain and func-tional disability are the most encountered consequences of vertebral fractures with repetitive fracture experience more likely occurring within a year after the first fracture. Early diagnosis of osteoporosis is therefore important for vertebral fracture prevention as drug treatments are more effective before perforation of the trabeculae (Mc Donnell et al. 2007). Bone mineral density (BMD) measured by dual energy X-ray absorptiometry (DXA) is the most clinically used method to diagnose osteopo-rosis. However this technique can only predict 40–70% of vertebral fractures as it only measures areal BMD which does not account for three dimensional (3D) geometry and BMD distribution (Sornay-Rendu et al. 2005). The combination of patient-specific 3D geometry and 3D BMD distribution is necessary to predict vertebral strength. Finite element models (FEM) derived from quantitative computed tomography (qCT) images are used to predict failure strength of vertebral bodies (Crawford et al. 2003; Imai et al. 2006; Buckley et al. 2007). Most of these models were validated under axial compressive forces to the vertebral body while vertebral fractures are more associated with eccentric compres-sion (Lunt et al. 2003). The purpose of this study was to compare the performance of the aBMD from DXA and qCT-based FEM in predicting experimen-tal vertebral strength. The experimental set up allowed for anterior compression testing on isolated vertebral bodies to ensure repeatable loading condition simulat-ing an anterior wedge-shape fracture

A 3D reconstruction method of the body envelope from biplanar X-rays: Evaluation of its accuracy and reliability

The aim of this study was to propose a novel method for reconstructing the external body envelope from the low dose biplanar X-rays of a person. The 3D body envelope was obtained by deforming a template to match the surface profiles in two X-rays images in three successive steps: global morphing to adopt the position of a person and scale the template׳s body segments, followed by a gross deformation and a fine deformation using two sets of pre-defined control points. To evaluate the method, a biplanar X-ray acquisition was obtained from head to foot for 12 volunteers in a standing posture. Up to 172 radio-opaque skin markers were attached to the body surface and used as reference positions. Each envelope was reconstructed three times by three operators. Results showed a bias lower than 7 mm and a confidence interval (95%) of reproducibility lower than 6 mm for all body parts, comparable to other existing methods matching a template onto stereographic photographs. The proposed method offers the possibility of reconstructing body shape in addition to the skeleton using a low dose biplanar X-rays system.The authors thank the ParisTech BiomecAM chair program on subject-specific musculoskeletal modeling, and in particular COVEA and Société Générale. A part of the evaluation was also performed within the support of the dexEOS project part of the FUI14 program. The authors thank Sonia Simoes, Thomas Joubert and Christophe Gatt for their technical assistance

Personalized Machine Learning Approach to Estimating Knee Kinematics Using Only Shank-Mounted IMU

peer reviewedKnee kinematics is a valuable measure of knee joint function. However, collecting that data outside the clinic is difficult, especially with a limited number of wearable sensors and when you only use an ankle-mounted inertial measurement unit (IMU) to estimate knee kinematics. Due to the cyclic nature of gait, it is possible to use machine learning to extract joint angles from only ankle-mounted sensors. This study aimed to use time-series feature extraction and a random forest regressor to generate a person-specific surrogate model for estimating knee joint flexion angles from a single-mounted IMU above the ankle. Optical motion capture (OMC) and inertial data from ten healthy participants walking on a treadmill were collected to create ten personalized surrogate models for estimating right knee flexion angles during gait. An additional ten models were created for a leave-one-out analysis to test the generalisability of the models. Temporal cross validation of the personalized models and a leave-one-out analysis was performed on the selected feature set. The personalized models achieved an average root-mean-square error (RMSE) of 2.45 \pm 0.65 ( R2 of 0.98) compared to a gold-standard OMC. The generalized models achieved an average RMSE of 6.77 \pm 3.38 ( R2 of 0.83) in the leave-one-out analysis. Time-series feature-based personalized surrogate models could be used to accurately estimate knee kinematics by using a single ankle-mounted sensor. However, more data are required to train a generalized model using the presented method

Alignment of centers of mass of body segments with the gravity line

Sagittal malalignment is associated with increased disability and can lead to postural troubles. The latter is often the result of an inadequate weight distribution on the spine and trigger compensatory mechanisms. No study to date has provided reference values for the alignment of centers of mass (CoM) of different body segments in asymptomatic subjects. The use of a bi-planar X-ray system (Dubousset et al. 2010) combined with 3D reconstruction of the external body shape (Nérot et al. 2015) allows for computation of the body segments’ CoM. The aim of this study was to provide baseline values for the position of each segment’s CoM compared to the gravity line (GL) in the sagittal plane

An Unsupervised Data-Driven Model to Classify Gait Patterns in Children with Cerebral Palsy

Ankle and foot orthoses are commonly prescribed to children with cerebral palsy (CP). It is unclear whether 3D gait analysis (3DGA) provides sufficient and reliable information for clinicians to be consistent when prescribing orthoses. Data-driven modeling can probe such questions by revealing non-intuitive relationships between variables such as 3DGA parameters and gait outcomes of orthoses use. The purpose of this study was to (1) develop a data-driven model to classify children with CP according to their gait biomechanics and (2) identify relationships between orthotics types and gait patterns. 3DGA data were acquired from walking trials of 25 typically developed children and 98 children with CP with additional prescribed orthoses. An unsupervised self-organizing map followed by k-means clustering was developed to group different gait patterns based on children&rsquo;s 3DGA. Model inputs were gait variable scores (GVSs) extracted from the gait profile score, measuring root mean square differences from TD children&rsquo;s gait cycle. The model identified five pathological gait patterns with statistical differences in GVSs. Only 43% of children improved their gait pattern when wearing an orthosis. Orthotics prescriptions were variable even in children with similar gait patterns. This study suggests that quantitative data-driven approaches may provide more clarity and specificity to support orthotics prescription