Biomechanical analysis of combi-hole locking compression plate during fracture healing: A numerical study of screw configuration.

Locking compression plates (LCPs) have become a widely used option for treating femur bone fractures. However, the optimal screw configuration with combi-holes remains a subject of debate. The study aims to create a time-dependent finite element (FE) model to assess the impacts of different screw configurations on LCP fixation stiffness and healing efficiency across four healing stages during a complete fracture healing process. To simulate the healing process, we integrated a time-dependent callus formation mechanism into a FE model of the LCP with combi-holes. Three screw configuration parameters, namely working length, screw number, and screw position, were investigated. Increasing the working length negatively affected axial stiffness and healing efficiency (p < 0.001), while screw number or position had no significant impact (p > 0.01). The time-dependent model displayed a moderate correlation with the conventional time-independent model for axial stiffness and healing efficiency (ρ ≥ 0.733, p ≤ 0.025). The highest healing efficiency (95.2%) was observed in screw configuration C125 during the 4-8-week period. The results provide insights into managing fractures using LCPs with combi-holes over an extended duration. Under axial compressive loading conditions, the use of the C125 screw configuration can enhance callus formation during the 4-12-week period for transverse fractures. When employing the C12345 configuration, it becomes crucial to avoid overconstraint during the 4-8-week period.

Gait Intention Prediction Using a Lower-Limb Musculoskeletal Model and Long Short-Term Memory Neural Networks.

The prediction of gait motion intention is essential for achieving intuitive control of assistive devices and diagnosing gait disorders. To reduce the cost associated with using multimodal signals and signal processing, we proposed a novel method that integrates machine learning with musculoskeletal modelling techniques for the prediction of time-series joint angles, using only kinematic signals. Additionally, we hypothesised that a stacked long short-term memory (LSTM) neural network architecture can perform the task without relying on any ahead-of-motion features typically provided by electromyography signals. Optical cameras and inertial measurement unit (IMU) sensors were used to track level gait kinematics. Joint angles were modelled using the musculoskeletal model. The optimal LSTM architecture in fulfilling the prediction task was determined. Joint angle predictions were performed for joints on the sagittal plane, benefiting from joint angle modelling using signals from optical cameras and IMU sensors. Our proposed method predicted the upcoming joint angles in the prediction time of 10 ms, with an averaged root mean square error of 5.3° and a coefficient of determination of 0.81. Moreover, in support of our hypothesis, the recurrent stacked LSTM network demonstrated its ability to predict intended motion accurately and efficiently in gait, outperforming two other neural network architectures: a feedforward MLP and a hybrid LSTM-MLP. The method paves the way for the development of a cost-effective, single-modal control system for assistive devices in gait rehabilitation.

The effect of muscle atrophy in people with unilateral transtibial amputation for three activities: Gait alone does not tell the whole story.

Amputation imposes significant challenges in locomotion to millions of people with limb loss worldwide. The decline in the use of the residual limb results in muscle atrophy that affects musculoskeletal dynamics in daily activities. The aim of this study was to quantify the lower limb muscle volume discrepancy based on magnetic resonance (MR) imaging and to combine this with motion analysis and musculoskeletal modelling to quantify the effects in the dynamics of key activities of daily living. Eight male participants with traumatic unilateral transtibial amputation were recruited who were at least six months after receiving their definitive prostheses. The muscle volume discrepancies were found to be largest at the knee extensors (35 %, p = 0.008), followed by the hip abductors (17 %, p = 0.008). Daily activities (level walking, standing up from a chair and ascending one step) were measured in a motion analysis laboratory and muscle and joint forces quantified using a detailed musculoskeletal model for people with unilateral transtibial amputation which was calibrated in terms of the muscle volume discrepancies post-amputation at a subject-specific level. Knee extensor muscle forces were lower at the residual limb than the intact limb for all activities (p ≤ 0.008); residual limb muscle forces of the hip abductors (p ≤ 0.031) and adductors (p ≤ 0.031) were lower for standing-up and ascending one step. While the reduced knee extensor force has been reported by other studies, our results suggest a new biomechanically-based mitigation strategy to improve functional mobility, which could be achieved through strengthening of the hip abd/adductor muscles.

A Hybrid Method Integrating A Musculoskeletal Model with Long Short-Term Memory (LSTM) for Human Motion Prediction.

So far, it shows a growing interest in the biomechanics community in the development of wearable technologies and their clinical applications, which enables the diagnosis of movement disorders and design of the rehabilitation interventions. To provide reliable feedback in the human-machine interface for advanced rehabilitation devices, methods to predict motion intention was developed which aim to generate future human motion based on the measured motion. An inertial measurement unit (IMU) is a promising device for motion tracking, with the advantages of low cost and high convenience in sensor placement to measure motion in almost every environment. However, it reveals that few contributions have been devoted to human motion prediction with pure IMU data. Thus, we propose a hybrid method integrating a musculoskeletal (MSK) model and the long short-term memory (LSTM) artificial neural network (ANN) to predict human motion. The proposed method was capable to predict motion in the daily tasks (stand-to-sit-to-stand and walking) for healthy participants: the predicted knee joint angles had an RMSE of 2.93° when compared to measured knee joint angles from the IMU data. The proposed method outperformed the methods based on the ANN/MSK model (RMSE of 31.15°) and LSTM without the integration of the MSK model (RMSE of 31.26°) in the motion prediction. Clinical Relevance- This proposed model based on IMU data alone has the great potential to become a low-cost, easy-to-use alternative in motion prediction to interact with advanced rehabilitation devices in clinical practice.

Understanding lower limb muscle volume adaptations to amputation.

Amputation of a major limb, and the subsequent return to movement with a prosthesis, requires the development of compensatory strategies to account for the loss. Such strategies, over time, lead to regional muscle atrophy and hypertrophy through chronic under or overuse of muscles compared to uninjured individuals. The aim of this study was to quantify the lower limb muscle parameters of persons with transtibial and transfemoral amputations using high resolution MRI to ascertain muscle volume and to determine regression equations for predicting muscle volume using femur- and tibia-length, pelvic-width, height, and mass. Twelve persons with limb loss participated in this study and their data were compared to six matched control subjects. Subjects with unilateral transtibial amputation showed whole-limb muscle volume loss in the residual-limb, whereas minor volume changes in the intact limb were found, providing evidence for a compensation strategy that is dominated by the intact-limb. Subjects with bilateral-transfemoral amputations showed significant muscle volume increases in the short adductor muscles with an insertion not affected by the amputation, the hip flexors, and the gluteus medius, and significant volume decreases in the longer adductor muscles, rectus femoris, and hamstrings. This study presents a benchmark measure of muscle volume discrepancies in persons with limb-loss, and can be used to understand the compensation strategies of persons with limb-loss and the impact on muscle volume, thus enabling the development of optimised intervention protocols, conditioning therapies, surgical techniques, and prosthetic devices that promote and enhance functional capability within the population of persons with limb loss.

An Anatomical Atlas-Based Scaling Study for Quantifying Muscle and Hip Joint Contact Forces in Above and Through-Knee Amputees Using Validated Musculoskeletal Modelling.

OBJECTIVE: Customisation of musculoskeletal modelling using magnetic resonance imaging (MRI) significantly improves the model accuracy, but the process is time consuming and computationally intensive. This study hypothesizes that linear scaling to a lower limb amputee model with anthropometric similarity can accurately predict muscle and joint contact forces. METHODS: An MRI-based anatomical atlas, comprising 18 trans-femoral and through-knee traumatic lower limb amputee models, is developed. Gait data, using a 10-camera motion capture system with two force plates, and surface electromyography (EMG) data were collected. Muscle and hip joint contact forces were quantified using musculoskeletal modelling. The predicted muscle activations from the subject-specific models were validated using EMG recordings. Anthropometry based multiple linear regression models, which minimize errors in force predictions, are presented. RESULTS: All predictions showed excellent (error interval c = 0-0.15), very good (c = 0.15-0.30) or good (c = 0.30-0.45) similarity to the EMG data, demonstrating accurate computation of muscle activations. The primary predictors of discrepancies in force predictions were differences in pelvis width (p < 0.001), body mass index (BMI, p < 0.001) and stump length to pelvis width ratio (p < 0.001) between the respective individual and underlying dataset. CONCLUSION: Linear scaling to a model with the most similar pelvis width, BMI and stump length to pelvis width ratio results in modelling outcomes with minimal errors. SIGNIFICANCE: This study provides robust tools to perform accurate analyses of musculoskeletal mechanics for high-functioning lower limb military amputees, thus facilitating the further understanding and improvement of the amputee's function. The atlas is available in an open source repository.

Higher knee contact forces might underlie increased osteoarthritis rates in high functioning amputees: A pilot study.

High functioning military transtibial amputees (TTAs) with well-fitted state of the art prosthetics have gait that is indistinguishable from healthy individuals, yet they are more likely to develop knee osteoarthritis (OA) of their intact limbs. This contrasts with the information at the knees of the amputated limbs that have been shown to be at a significantly reduced risk of pain and OA. The hypothesis of this study is that biomechanics can explain the difference in knee OA risk. Eleven military unilateral TTAs and eleven matched healthy controls underwent gait analysis. Muscle forces and joint contact forces at the knee were quantified using musculoskeletal modeling, validated using electromyography measurements. Peak knee contact forces for the intact limbs on both the medial and lateral compartments were significantly greater than the healthy controls (P ≤ .006). Additionally, the intact limbs had greater peak semimembranosus (P = .001) and gastrocnemius (P ≤ .001) muscle forces compared to the controls. This study has for the first time provided robust evidence of increased force on the non-affected knees of high functioning TTAs that supports the mechanically based hypothesis to explain the documented higher risk of knee OA in this patient group. The results suggest several protentional strategies to mitigate knee OA of the intact limbs, which may include the improvements of the prosthetic foot control, socket design, and strengthening of the amputated muscles.

A Femoral Clamp to Reduce Soft Tissue Artifact: Accuracy and Reliability in Measuring Three-Dimensional Knee Kinematics During Gait.

The accurate measurement of full six degrees-of-freedom (6DOFs) knee joint kinematics is prohibited by soft tissue artifact (STA), which remains the greatest source of error. The purpose of this study was to present and assess a new femoral clamp to reduce STA at the thigh. It was hypothesized that the device can preserve the natural knee joint kinematics pattern and outperform a conventional marker mounted rigid cluster during gait. Six healthy subjects were asked to walk barefoot on level ground with a cluster marker set (cluster gait) followed by a cluster-clamp-merged marker set (clamp gait) and their kinematics was measured using the cluster method in cluster gait and the cluster and clamp methods simultaneously in clamp gait. Two operators performed the gait measurement. A 6DOFs knee joint model was developed to enable comparison with the gold standard knee joint kinematics measured using a dual fluoroscopic imaging technique. One-dimensional (1D) paired t-tests were used to compare the knee joint kinematics waveforms between cluster gait and clamp gait. The accuracy was assessed in terms of the root-mean-square error (RMSE), coefficient of determination, and Bland-Altman plots. Interoperator reliability was assessed using the intraclass correlation coefficient (ICC). The result showed that the femoral clamp did not change the walking speed and knee joint kinematics waveforms. Additionally, clamp gait reduced the rotation and translation errors in the transverse plane and improved the interoperator reliability when compared to the rigid cluster method, suggesting a more accurate and reliable measurement of knee joint kinematics.

Computational identification of protein-protein interactions in model plant proteomes.

Protein-protein interactions (PPIs) play essential roles in many biological processes. A PPI network provides crucial information on how biological pathways are structured and coordinated from individual protein functions. In the past two decades, large-scale PPI networks of a handful of organisms were determined by experimental techniques. However, these experimental methods are time-consuming, expensive, and are not easy to perform on new target organisms. Large-scale PPI data is particularly sparse in plant organisms. Here, we developed a computational approach for detecting PPIs trained and tested on known PPIs of Arabidopsis thaliana and applied to three plants, Arabidopsis thaliana, Glycine max (soybean), and Zea mays (maize) to discover new PPIs on a genome-scale. Our method considers a variety of features including protein sequences, gene co-expression, functional association, and phylogenetic profiles. This is the first work where a PPI prediction method was developed for is the first PPI prediction method applied on benchmark datasets of Arabidopsis. The method showed a high prediction accuracy of over 90% and very high precision of close to 1.0. We predicted 50,220 PPIs in Arabidopsis thaliana, 13,175,414 PPIs in corn, and 13,527,834 PPIs in soybean. Newly predicted PPIs were classified into three confidence levels according to the availability of existing supporting evidence and discussed. Predicted PPIs in the three plant genomes are made available for future reference.

Extra excitation of biceps femoris during neuromuscular electrical stimulation reduces knee medial loading.

Medial knee joint osteoarthritis (OA) is a debilitating and prevalent condition. Surgical treatment consists of redistributing the forces from the medial to the lateral compartment through osteotomy, or replacing the joint surfaces. As the mediolateral load distribution is related to the action of the musculature around the knee, the aim of this study was to devise a technique to redistribute these forces non-surgically through changes in muscle excitation. Eight healthy subjects participated in the experiment, and neuromuscular electrical stimulation was used to change the muscle forces around the knee. A musculoskeletal model was used to quantify the loading on the medial compartment of the knee, and a novel algorithm devised and implemented to simulate neuromuscular electrical stimulation. The forces and moments at the knee, ground reaction forces, walking velocity and step length were quantified before and after stimulation. Stimulation of the biceps femoris resulted in a significant decrease in the second peak of the medial knee joint loading by up to 0.17 body weight (p = 0.016). Kinematic parameters were not significantly affected. Neuromuscular electrical stimulation can decrease the peak loads on the medial compartment of the knee, and thus offers a promising therapy for medial knee joint OA.

Improving Musculoskeletal Model Scaling Using an Anatomical Atlas: The Importance of Gender and Anthropometric Similarity to Quantify Joint Reaction Forces.

OBJECTIVE: The accuracy of a musculoskeletal model relies heavily on the implementation of the underlying anatomical dataset. Linear scaling of a generic model, despite being time and cost efficient, produces substantial errors as it does not account for gender differences and inter-individual anatomical variations. The hypothesis of this study is that linear scaling to a musculoskeletal model with gender and anthropometric similarity to the individual subject produces similar results to the ones that can be obtained from a subject-specific model. METHODS: A lower limb musculoskeletal anatomical atlas was developed consisting of ten datasets derived from magnetic resonance imaging of healthy subjects and an additional generic dataset from the literature. Predicted muscle activation and joint reaction force were compared with electromyography and literature data. Regressions based on gender and anthropometry were used to identify the use of atlas. RESULTS: Primary predictors of differences for the joint reaction force predictions were mass difference for the ankle (p < 0.001) and length difference for the knee and hip (p ≤ 0.017). Gender difference accounted for an additional 3% of the variance (p ≤ 0.039). Joint reaction force differences at the ankle, knee, and hip were reduced by between 50% and 67% (p = 0.005) when using a musculoskeletal model with the same gender and similar anthropometry in comparison with a generic model. CONCLUSION: Linear scaling with gender and anthropometric similarity can improve joint reaction force predictions in comparison with a scaled generic model. SIGNIFICANCE: The presented scaling approach and atlas can improve the fidelity and utility of musculoskeletal models for subject-specific applications.

Deep Learning for Musculoskeletal Force Prediction.

Musculoskeletal models permit the determination of internal forces acting during dynamic movement, which is clinically useful, but traditional methods may suffer from slowness and a need for extensive input data. Recently, there has been interest in the use of supervised learning to build approximate models for computationally demanding processes, with benefits in speed and flexibility. Here, we use a deep neural network to learn the mapping from movement space to muscle space. Trained on a set of kinematic, kinetic and electromyographic measurements from 156 subjects during gait, the network's predictions of internal force magnitudes show good concordance with those derived by musculoskeletal modelling. In a separate set of experiments, training on data from the most widely known benchmarks of modelling performance, the international Grand Challenge competitions, generates predictions that better those of the winning submissions in four of the six competitions. Computational speedup facilitates incorporation into a lab-based system permitting real-time estimation of forces, and interrogation of the trained neural networks provides novel insights into population-level relationships between kinematic and kinetic factors.

Anthropometric Scaling of Anatomical Datasets for Subject-Specific Musculoskeletal Modelling of the Shoulder.

Linear scaling of generic shoulder models leads to substantial errors in model predictions. Customisation of shoulder modelling through magnetic resonance imaging (MRI) improves modelling outcomes, but model development is time and technology intensive. This study aims to validate 10 MRI-based shoulder models, identify the best combinations of anthropometric parameters for model scaling, and quantify the improvement in model predictions of glenohumeral loading through anthropometric scaling from this anatomical atlas. The shoulder anatomy was modelled using a validated musculoskeletal model (UKNSM). Ten subject-specific models were developed through manual digitisation of model parameters from high-resolution MRI. Kinematic data of 16 functional daily activities were collected using a 10-camera optical motion capture system. Subject-specific model predictions were validated with measured muscle activations. The MRI-based shoulder models show good agreement with measured muscle activations. A tenfold cross-validation using the validated personalised shoulder models demonstrates that linear scaling of anthropometric datasets with the most similar ratio of body height to shoulder width and from the same gender (p < 0.04) yields best modelling outcomes in glenohumeral loading. The improvement in model reliability is significant (p < 0.02) when compared to the linearly scaled-generic UKNSM. This study may facilitate the clinical application of musculoskeletal shoulder modelling to aid surgical decision-making.

Validation and Use of a Musculoskeletal Gait Model to Study the Role of Functional Electrical Stimulation.

OBJECTIVE: Musculoskeletal modeling has been used to predict the effect of functional electrical stimulation (FES) on the mechanics of the musculoskeletal system. However, validation of the resulting muscle activations due to FES is challenging as conventional electromyography (EMG) recording of signals from the stimulated muscle is affected by stimulation artefacts. A validation approach using a combination of musculoskeletal modeling and EMG was proposed, whereby the effect on nonstimulated muscles is assessed using both techniques. The aim is to quantify the effect of FES on biceps femoris long head (BFLH) and validate this directly against EMG of gluteus maximus (GMAX). The hypotheses are that GMAX activation correlates with BFLH activation; and the muscle activation during FES gait can be predicted using musculoskeletal modeling. METHODS: Kinematics, kinetics, and EMG of healthy subjects were measured under four walking conditions (normal walking followed by FES walking with three levels of BFLH stimulation). Measured kinematics and kinetics served as inputs to the musculoskeletal model. RESULTS: Strong positive correlations were found between GMAX activation and BFLH activation in early stance peak (R = 0.78, p = 0.002) and impulse (R = 0.63, p = 0.021). The modeled peak and impulse of GMAX activation increased with EMG peak (p < 0.001) and impulse (p = 0.021). CONCLUSION: Musculoskeletal modeling can be used reliably to quantify the effect of FES in a healthy gait. SIGNIFICANCE: The validation approach using EMG and musculoskeletal modeling developed and tested can potentially be applied to the use of FES for other muscles and activities.

Analysis of Protein Complexes in the Unicellular Cyanobacterium Cyanothece ATCC 51142.

The unicellular cyanobacterium Cyanothece ATCC 51142 is capable of oxygenic photosynthesis and biological N2 fixation (BNF), a process highly sensitive to oxygen. Previous work has focused on determining protein expression levels under different growth conditions. A major gap of our knowledge is an understanding on how these expressed proteins are assembled into complexes and organized into metabolic pathways, an area that has not been thoroughly investigated. Here, we combined size-exclusion chromatography (SEC) with label-free quantitative mass spectrometry (MS) and bioinformatics to characterize many protein complexes from Cyanothece 51142 cells grown under a 12 h light-dark cycle. We identified 1386 proteins in duplicate biological replicates, and 64% of those proteins were identified as putative complexes. Pairwise computational prediction of protein-protein interaction (PPI) identified 74 822 putative interactions, of which 2337 interactions were highly correlated with published protein coexpressions. Many sequential glycolytic and TCA cycle enzymes were identified as putative complexes. We also identified many membrane complexes that contain cytoplasmic domains. Subunits of NDH-1 complex eluted in a fraction with an approximate mass of ∼669 kDa, and subunits composition revealed coexistence of distinct forms of NDH-1 complex subunits responsible for respiration, electron flow, and CO2 uptake. The complex form of the phycocyanin beta subunit was nonphosphorylated, and the monomer form was phosphorylated at Ser20, suggesting phosphorylation-dependent deoligomerization of the phycocyanin beta subunit. This study provides an analytical platform for future studies to reveal how these complexes assemble and disassemble as a function of diurnal and circadian rhythms.

Computing and Visualizing Gene Function Similarity and Coherence with NaviGO.

Gene ontology (GO) is a controlled vocabulary of gene functions across all species, which is widely used for functional analyses of individual genes and large-scale proteomic studies. NaviGO is a webserver for visualizing and quantifying the relationship and similarity of GO annotations. Here, we walk through functionality of the NaviGO webserver ( http://kiharalab.org/web/navigo/ ) using an example input and explain what can be learned from analysis results. NaviGO has four main functions, accessed from each page of the webserver: "GO Parents," "GO Set", "GO Enrichment", and "Protein Set." For a given list of GO terms, the "GO Parents" tab visualizes the hierarchical relationship of GO terms, and the "GO Set" tab calculates six functional similarity and association scores and presents results in a network and a multidimensional scaling plot. For a set of proteins and their associated GO terms, the "GO Enrichment" tab calculates protein GO functional enrichment, while the "Protein Set" tab calculates functional association between proteins. The NaviGO source code can be also downloaded and used locally or integrated into other software pipelines.

Computational Methods for Predicting Protein-Protein Interactions Using Various Protein Features.

Understanding protein-protein interactions (PPIs) in a cell is essential for learning protein functions, pathways, and mechanism of diseases. PPIs are also important targets for developing drugs. Experimental methods, both small-scale and large-scale, have identified PPIs in several model organisms. However, results cover only a part of PPIs of organisms; moreover, there are many organisms whose PPIs have not yet been investigated. To complement experimental methods, many computational methods have been developed that predict PPIs from various characteristics of proteins. Here we provide an overview of literature reports to classify computational PPI prediction methods that consider different features of proteins, including protein sequence, genomes, protein structure, function, PPI network topology, and those which integrate multiple methods. © 2018 by John Wiley & Sons, Inc.

Activation of biceps femoris long head reduces tibiofemoral anterior shear force and tibial internal rotation torque in healthy subjects.

The anterior cruciate ligament (ACL) provides resistance to tibial internal rotation torque and anterior shear at the knee. ACL deficiency results in knee instability. Optimisation of muscle contraction through functional electrical stimulation (FES) offers the prospect of mitigating the destabilising effects of ACL deficiency. The hypothesis of this study is that activation of the biceps femoris long head (BFLH) reduces the tibial internal rotation torque and the anterior shear force at the knee. Gait data of twelve healthy subjects were measured with and without the application of FES and taken as inputs to a computational musculoskeletal model. The model was used to investigate the optimum levels of BFLH activation during FES gait in reducing the anterior shear force to zero. This study found that FES significantly reduced the tibial internal rotation torque at the knee during the stance phase of gait (p = 0.0322) and the computational musculoskeletal modelling revealed that a mean BFLH activation of 20.8% (±8.4%) could reduce the anterior shear force to zero. At the time frame when the anterior shear force was zero, the internal rotation torque was reduced by 0.023 ± 0.0167 Nm/BW, with a mean 188% reduction across subjects (p = 0.0002). In conclusion, activation of the BFLH is able to reduce the tibial internal rotation torque and the anterior shear force at the knee in healthy control subjects. This should be tested on ACL deficient subject to consider its effect in mitigating instability due to ligament deficiency. In future clinical practice, activating the BFLH may be used to protect ACL reconstructions during post-operative rehabilitation, assist with residual instabilities post reconstruction, and reduce the need for ACL reconstruction surgery in some cases.

NaviGO: interactive tool for visualization and functional similarity and coherence analysis with gene ontology.

BACKGROUND: The number of genomics and proteomics experiments is growing rapidly, producing an ever-increasing amount of data that are awaiting functional interpretation. A number of function prediction algorithms were developed and improved to enable fast and automatic function annotation. With the well-defined structure and manual curation, Gene Ontology (GO) is the most frequently used vocabulary for representing gene functions. To understand relationship and similarity between GO annotations of genes, it is important to have a convenient pipeline that quantifies and visualizes the GO function analyses in a systematic fashion. RESULTS: NaviGO is a web-based tool for interactive visualization, retrieval, and computation of functional similarity and associations of GO terms and genes. Similarity of GO terms and gene functions is quantified with six different scores including protein-protein interaction and context based association scores we have developed in our previous works. Interactive navigation of the GO function space provides intuitive and effective real-time visualization of functional groupings of GO terms and genes as well as statistical analysis of enriched functions. CONCLUSIONS: We developed NaviGO, which visualizes and analyses functional similarity and associations of GO terms and genes. The NaviGO webserver is freely available at: http://kiharalab.org/web/navigo .

Non-linear scaling of a musculoskeletal model of the lower limb using statistical shape models.

Accurate muscle geometry for musculoskeletal models is important to enable accurate subject-specific simulations. Commonly, linear scaling is used to obtain individualised muscle geometry. More advanced methods include non-linear scaling using segmented bone surfaces and manual or semi-automatic digitisation of muscle paths from medical images. In this study, a new scaling method combining non-linear scaling with reconstructions of bone surfaces using statistical shape modelling is presented. Statistical Shape Models (SSMs) of femur and tibia/fibula were used to reconstruct bone surfaces of nine subjects. Reference models were created by morphing manually digitised muscle paths to mean shapes of the SSMs using non-linear transformations and inter-subject variability was calculated. Subject-specific models of muscle attachment and via points were created from three reference models. The accuracy was evaluated by calculating the differences between the scaled and manually digitised models. The points defining the muscle paths showed large inter-subject variability at the thigh and shank - up to 26mm; this was found to limit the accuracy of all studied scaling methods. Errors for the subject-specific muscle point reconstructions of the thigh could be decreased by 9% to 20% by using the non-linear scaling compared to a typical linear scaling method. We conclude that the proposed non-linear scaling method is more accurate than linear scaling methods. Thus, when combined with the ability to reconstruct bone surfaces from incomplete or scattered geometry data using statistical shape models our proposed method is an alternative to linear scaling methods.