An articulated shape model to predict paediatric lower limb bone geometry using sparse landmarks.

Creating musculoskeletal models in a paediatric population currently involves either creating an image-based model from medical imaging data or a generic model using linear scaling. Image-based models provide a high level of accuracy but are time-consuming and costly to implement, on the other hand, linear scaling of an adult template musculoskeletal model is faster and common practice, but the output errors are significantly higher. An articulated shape model incorporates pose and shape to predict geometry for use in musculoskeletal models based on existing information from a population to provide both a fast and accurate method. From a population of 333 children aged 4-18 years old, we have developed an articulated shape model of paediatric lower limb bones to predict bone geometry from eight bone landmarks commonly used for motion capture. Bone surface root mean squared errors were found to be 2.63 ± 0.90 mm, 1.97 ± 0.61 mm, and 1.72 ± 0.51 mm for the pelvis, femur, and tibia/fibula, respectively. Linear scaling produced bone surface errors of 4.79 ± 1.39 mm, 4.38 ± 0.72 mm, and 4.39 ± 0.86 mm for the pelvis, femur, and tibia/fibula, respectively. Clinical bone measurement errors were low across all bones predicted using the articulated shape model, which outperformed linear scaling for all measurements. However, the model failed to accurately capture torsional measures (femoral anteversion and tibial torsion). Overall, the articulated shape model was shown to be a fast and accurate method to predict lower limb bone geometry in a paediatric population, superior to linear scaling.

3D gait analysis in children using wearable sensors: feasibility of predicting joint kinematics and kinetics with personalized machine learning models and inertial measurement units.

Introduction: Children's walking patterns evolve with age, exhibiting less repetitiveness at a young age and more variability than adults. Three-dimensional gait analysis (3DGA) is crucial for understanding and treating lower limb movement disorders in children, traditionally performed using Optical Motion Capture (OMC). Inertial Measurement Units (IMUs) offer a cost-effective alternative to OMC, although challenges like drift errors persist. Machine learning (ML) models can mitigate these issues in adults, prompting an investigation into their applicability to a heterogeneous pediatric population. This study aimed at 1) quantifying personalized and generalized ML models' performance for predicting gait time series in typically developed (TD) children using IMUs data, 2) Comparing random forest (RF) and convolutional neural networks (CNN) models' performance, 3) Finding the optimal number of IMUs required for accurate predictions. Methodology: Seventeen TD children, aged 6 to 15, participated in data collection involving OMC, force plates, and IMU sensors. Joint kinematics and kinetics (targets) were computed from OMC and force plates' data using OpenSim. Tsfresh, a Python package, extracted features from raw IMU data. Each target's ten most important features were input in the development of personalized and generalized RF and CNN models. This procedure was initially conducted with 7 IMUs placed on all lower limb segments and then performed using only two IMUs on the feet. Results: Findings suggested that the RF and CNN models demonstrated comparable performance. RF predicted joint kinematics with a 9.5% and 19.9% NRMSE for personalized and generalized models, respectively, and joint kinetics with an NRMSE of 10.7% for personalized and 15.2% for generalized models in TD children. Personalized models provided accurate estimations from IMU data in children, while generalized models lacked accuracy due to the limited dataset. Furthermore, reducing the number of IMUs from 7 to 2 did not affect the results, and the performance remained consistent. Discussion: This study proposed a promising personalized approach for gait time series prediction in children, involving an RF model and two IMUs on the feet.

The Biomechanics Research and Innovation Challenge: Development, Implementation, Uptake, and Reflections on the Inaugural Program.

Biomechanics as a discipline is ideally placed to increase awareness and participation of girls and women in science, technology, engineering, and mathematics. A nationwide Biomechanics and Research Innovation Challenge (BRInC) centered on mentoring and role modeling was developed to engage high school girls (mentees) and early-mid-career women (mentors) in the field of biomechanics through the completion of a 100-day research and/or innovation project. This manuscript describes the development, implementation, and uptake of the inaugural BRInC program and synthesizes the research and innovation projects undertaken, providing a framework for adoption of this program within the global biomechanics community. Eighty-seven high school girls in years 9 and 10 (age range: 14-16 y) were mentored in teams (n = 17) by women in biomechanics (n = 24). Using a design thinking approach, teams generated solutions to biomechanics-based problem(s)/research question(s). Eight key reflections on program strengths, as well as areas for improvement and planned changes for future iterations of the BRInC program, are outlined. These key reflections highlight the innovation, impact, and scalability of the program; the importance of a program framework and effective communication tools; and implementation of strategies to sustain the program as well as the importance of diversity and building a sense of community.

A Narrative Review of Personalized Musculoskeletal Modeling Using the Physiome and Musculoskeletal Atlas Projects.

In this narrative review, we explore developments in the field of computational musculoskeletal model personalization using the Physiome and Musculoskeletal Atlas Projects. Model geometry personalization; statistical shape modeling; and its impact on segmentation, classification, and model creation are explored. Examples include the trapeziometacarpal and tibiofemoral joints, Achilles tendon, gastrocnemius muscle, and pediatric lower limb bones. Finally, a more general approach to model personalization is discussed based on the idea of multiscale personalization called scaffolds.

Sex differences in linear bone measurements occur following puberty but do not influence femoral or tibial torsion.

Torsional, angular, and linear measurements in a paediatric population are clinically important but not well defined and understood. Different methods of measurement and discrepancies between assessors leads to a lack of understanding of what should be defined as typical or atypical for the growing skeleton. From a large dataset of 333 paediatric CT scans, we extracted three-dimensional torsional, angular, and linear measurements from the pelvis, femur, and tibia/fibula. Sex differences in linear measurements were observed in bones of children aged 13+ (around puberty), but femoral and tibial torsion were similar between males and females. The rotational profile (femoral anteversion minus tibial torsion) tended to increase with growth. Epicondylar, condylar, and malleolar widths were smaller in females than males for the same bone length after the age of 13 years, which could explain why females may be more at risk for sport injuries during adolescence. This rich dataset can be used as an atlas for researchers and clinicians to understand typical development of critical rotational profiles and linear bone measurements in children.

Roadmap for an imaging and modelling paediatric study in rural NZ.

Our study methodology is motivated from three disparate needs: one, imaging studies have existed in silo and study organs but not across organ systems; two, there are gaps in our understanding of paediatric structure and function; three, lack of representative data in New Zealand. Our research aims to address these issues in part, through the combination of magnetic resonance imaging, advanced image processing algorithms and computational modelling. Our study demonstrated the need to take an organ-system approach and scan multiple organs on the same child. We have pilot tested an imaging protocol to be minimally disruptive to the children and demonstrated state-of-the-art image processing and personalized computational models using the imaging data. Our imaging protocol spans brain, lungs, heart, muscle, bones, abdominal and vascular systems. Our initial set of results demonstrated child-specific measurements on one dataset. This work is novel and interesting as we have run multiple computational physiology workflows to generate personalized computational models. Our proposed work is the first step towards achieving the integration of imaging and modelling improving our understanding of the human body in paediatric health and disease.

A comparison of machine learning models' accuracy in predicting lower-limb joints' kinematics, kinetics, and muscle forces from wearable sensors.

A combination of wearable sensors' data and Machine Learning (ML) techniques has been used in many studies to predict specific joint angles and moments. The aim of this study was to compare the performance of four different non-linear regression ML models to estimate lower-limb joints' kinematics, kinetics, and muscle forces using Inertial Measurement Units (IMUs) and electromyographys' (EMGs) data. Seventeen healthy volunteers (9F, 28 ± 5 years) were asked to walk over-ground for a minimum of 16 trials. For each trial, marker trajectories and three force-plates data were recorded to calculate pelvis, hip, knee, and ankle kinematics and kinetics, and muscle forces (the targets), as well as 7 IMUs and 16 EMGs. The features from sensors' data were extracted using the Tsfresh python package and fed into 4 ML models; Convolutional Neural Networks (CNN), Random Forest (RF), Support Vector Machine, and Multivariate Adaptive Regression Spline for targets' prediction. The RF and CNN models outperformed the other ML models by providing lower prediction errors in all intended targets with a lower computational cost. This study suggested that a combination of wearable sensors' data with an RF or a CNN model is a promising tool to overcome the limitations of traditional optical motion capture for 3D gait analysis.

Predicting the hip joint centre in children: New regression equations, linear scaling, and statistical shape modelling.

Determination of the hip joint centre (HJC) is important to accurately estimate hip joint motion, moments and muscle forces. The most accurate method for HJC estimation without medical imaging is an area of interest in the biomechanics community, especially in a paediatric population, which has not been widely evaluated. HJC locations were calculated by sphere-fitting to the acetabulum of three-dimensional pelvises segmented from 333 CT scans of children aged 4 to 18 years old. Three methods for determining the HJC were compared: regression equations, linear scaling, and shape model prediction. The new regression equations developed in this study produced Euclidean distance errors of 6.23 mm ± 2.90 mm. Linear scaling of paediatric bone produced errors of 3.90 mm ± 2.52 mm and adult bone scaling of 5.45 mm ± 3.26 mm. Prediction of the HJC using a paediatric statistical shape model produced mean Euclidian distance errors of 2.95 mm ± 1.65 mm. Overall, shape model prediction of the HJC produced the lowest errors, with linear scaling of a mean paediatric pelvis providing better estimates than regression equations.

Morphological variation in paediatric lower limb bones.

Available methods for generating paediatric musculoskeletal geometry are to scale generic adult geometry, which is widely accessible but can be inaccurate, or to obtain geometry from medical imaging, which is accurate but time-consuming and costly. A population-based shape model is required to generate accurate and accessible musculoskeletal geometry in a paediatric population. The pelvis, femur, and tibia/fibula were segmented from 333 CT scans of children aged 4-18 years. Bone morphology variation was captured using principal component analysis (PCA). Subsequently, a shape model was developed to predict bone geometry from demographic and linear bone measurements and validated using a leave one out analysis. The shape model was compared to linear scaling of adult and paediatric bone geometry. The PCA captured growth-related changes in bone geometry. The shape model predicted bone geometry with root mean squared error (RMSE) of 2.91 ± 0.99 mm in the pelvis, 2.01 ± 0.62 mm in the femur, and 1.85 ± 0.54 mm in the tibia/fibula. Linear scaling of an adult mesh produced RMSE of 4.79 ± 1.39 mm in the pelvis, 4.38 ± 0.72 mm in the femur, and 4.39 ± 0.86 mm in the tibia/fibula. We have developed a method for capturing and predicting lower limb bone shape variation in a paediatric population more accurately than linear scaling without using medical imaging.

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's 3DGA. Model inputs were gait variable scores (GVSs) extracted from the gait profile score, measuring root mean square differences from TD children'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.

Effect of simulated joint instability and bracing on ankle and subtalar joint flexibility.

It is clinically challenging to distinguish between ankle and subtalar joints instability in vivo. Understanding the changes in load-displacement at the ankle and subtalar joints after ligament injuries may detect specific changes in joint characteristics that cannot be detected by investigating changes in range of motion alone. The effect of restricting joints end range of motion with ankle braces was already established, but little is known about the effect of an ankle brace on the flexibility of the injured ankle and subtalar joints. Therefore, the purposes of this study were to (1) understand how flexibility is affected at the ankle and subtalar joints after sectioning lateral and intrinsic ligaments during combined sagittal foot position and inversion and during internal rotation and (2) investigate the effect of a semi-rigid ankle brace on the ankle and subtalar joint flexibility. Kinematics and kinetics were collected from nine cadaver feet during inversion through the range of ankle flexion and during internal rotation. Motion was applied with and without a brace on an intact foot and after sequentially sectioning the calcaneofibular ligament (CFL) and the intrinsic ligaments. Segmental flexibility was defined as the slope of the angle-moment curve for each 1 Nm interval. Early flexibility significantly increased at the ankle and subtalar joint after CFL sectioning during inversion. The semi-rigid ankle brace significantly decreased early flexibility at the subtalar joint during inversion and internal rotation for all ligament conditions and at the ankle joint after all ligaments were cut.

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.

Effect of Direct Ligament Repair and Tenodesis Reconstruction on Simulated Subtalar Joint Instability.

BACKGROUND: Subtalar instability is associated with up to 80% of patients presenting with chronic ankle instability but is often not considered in the diagnosis or treatment. Operative procedures to repair ankle instability have shown good clinical results, but the effects of these reconstruction procedures on isolated subtalar instability are not well understood. The goal of this study was to investigate the effect of the Gould modification of the Broström procedure and a new tenodesis reconstruction procedure on ankle and subtalar joint kinematics after simulating a subtalar injury. METHODS: Kinematic data were collected on 7 cadaveric ankles during inversion through the range of ankle flexion and during internal rotation. Testing was performed on the intact foot; after sectioning the calcaneofibular ligament, cervical ligament, and interosseous talocalcaneal ligament; after the Gould modification of the Broström procedure was performed; and after tenodesis was performed and sutures from the Gould modification removed. RESULTS: The Gould modification of the Broström procedure significantly decreased subtalar and ankle inversion motion and subtalar internal rotation compared to the unstable condition. The tenodesis method restricted internal rotation at the subtalar joint and ankle inversion compared to the intact state. CONCLUSION: Both operative procedures improved stability of the ankle complex, but tenodesis was unable to restore subtalar inversion and restricted ankle inversion in maximum plantarflexion. CLINICAL RELEVANCE: The Gould modification of Broström ligament repair may be a favorable operative procedure for the restoration of subtalar and ankle joint kinematics.

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 0.74g/cm(3) for men and 0.73g/cm(3) for women instead of the one provided by Dempster (0.92g/cm(3)), leading to a better estimate of the thorax mass and body mass.

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 7mm and a confidence interval (95%) of reproducibility lower than 6mm 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 effects of a semi-rigid ankle brace on a simulated isolated subtalar joint instability.

Subtalar joint instability is hypothesized to occur after injuries to the calcaneofibular ligament (CFL) in isolation or in combination with the cervical and the talocalcaneal interosseous ligaments. A common treatment for hindfoot instability is the application of an ankle brace. However, the ability of an ankle brace to promote subtalar joint stability is not well established. We assessed the kinematics of the subtalar joint, ankle, and hindfoot in the presence of isolated subtalar instability, investigated the effect of bracing in a CFL deficient foot and with a total rupture of the intrinsic ligaments, and evaluated how maximum inversion range of motion is affected by the position of the ankle in the sagittal plane. Kinematics from nine cadaveric feet were collected with the foot placed in neutral, dorsiflexion, and plantar flexion. Motion was applied with and without a brace on an intact foot and after sequentially sectioning the CFL and the intrinsic ligaments. Isolated CFL sectioning increased ankle joint inversion, while sectioning the CFL and intrinsic ligaments affected subtalar joint stability. The brace limited inversion at the subtalar and ankle joints. Additionally, examining the foot in dorsiflexion reduced ankle and subtalar joint motion.

Influence of kinematic analysis methods on detecting ankle and subtalar joint instability.

Patients with subtalar joint instability may be misdiagnosed with ankle instability, which may lead to chronic instability at the subtalar joint. Therefore, it is important to understand the difference in kinematics after ligament sectioning and differentiate the changes in kinematics between ankle and subtalar instability. Three methods may be used to determine the joint kinematics; the Euler angles, the Joint Coordinate System (JCS) and the helical axis (HA). The purpose of this study was to investigate the influence of using either method to detect subtalar and ankle joints instability. 3D kinematics at the ankle and subtalar joint were analyzed on 8 cadaveric specimens while the foot was intact and after sequentially sectioning the anterior talofibular ligament (ATFL), the calcaneofibular ligament (CFL), the cervical ligament and the interosseous talocalcaneal ligament (ITCL). Comparison in kinematics calculated from sensor and anatomical landmarks was conducted as well as the influence of Euler angles and JCS rotation sequence (between ISB recommendation and previous research) on the subtalar joint. All data showed a significant increase in inversion when the ITCL was sectioned. There were differences in the data calculated using sensors coordinate systems vs. anatomic coordinate systems. Anatomic coordinate systems were recommended for these calculations. The Euler angle and JCS gave similar results. Differences in Euler angles and JCS sequence lead to the same conclusion in detecting instability at the ankle and subtalar joint. As expected, the HA detected instability in plantarflexion at the ankle joint and in inversion at the subtalar joint.