A comparative study of surrogate musculoskeletal models using various neural network configurations

Title from PDF of title page, viewed on August 13, 2013Thesis advisor: Reza R. DerakhshaniVitaIncludes bibliographic references (pages 85-88)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2013The central idea in musculoskeletal modeling is to be able to predict body-level (e.g. muscle forces) as well as tissue-level information (tissue-level stress, strain, etc.). To develop computationally efficient techniques to analyze such models, surrogate models have been introduced which concurrently predict both body-level and tissue-level information using multi-body and finite-element analysis, respectively. However, this kind of surrogate model is not an optimum solution as it involves the usage of finite element models which are computation intensive and involve complex meshing methods especially during real-time movement simulations. An alternative surrogate modeling method is the use of artificial neural networks in place of finite-element models. The ultimate objective of this research is to predict tissue-level stresses experienced by the cartilage and ligaments during movement and achieve concurrent simulation of muscle force and tissue stress using various surrogate neural network models, where stresses obtained from finite-element models provide the frame of reference. Over the last decade, neural networks have been successfully implemented in several biomechanical modeling applications. Their adaptive ability to learn from examples, simple implementation techniques, and fast simulation times make neural networks versatile and robust when compared to other techniques. The neural network models are trained with reaction forces from multi-body models and stresses from finite element models obtained at the interested elements. Several configurations of static and dynamic neural networks are modeled, and accuracies close to 93% were achieved, where the correlation coefficient is the chosen measure of goodness. Using neural networks, the simulation time was reduced nearly 40,000 times when compared to the finite-element models. This study also confirms theoretical concepts that special network configurations--including average committee, stacked generalization, and negative correlation learning--provide considerably better results when compared to individual networks themselves.Introduction -- Methods -- Results -- Conclusion -- Future work -- Appendix A. Various linear and non-linear modeling techniques -- Appendix B. Error analysi

Predictions of Knee Joint Contact Forces Using Only Kinematic Inputs with a Recurrent Neural Network

BACKGROUND: Knee joint contact (bone on bone) forces are commonly estimated using surrogate measures such as external knee adduction moments (with limited success) or musculoskeletal modeling (more successful). Despite its capabilities, modeling is not optimal for clinicians or persons with limited experience and knowledge. Therefore, the purpose of this study was to design a novel prediction method for knee joint contact forces that is equal or more accurate than modeling, yet simplistic in terms of required inputs. METHODS: This study included all six subjects’ (71.3±6.5kg, 1.7±0.1m) data from the opensource “Grand Challenge” datasets (simtk.org) and two subjects from the CAMS datasets, consisting of motion capture and in-vivo instrumented knee prosthesis data (e.g. true knee joint contact forces). Inverse kinematics were used to derive three-dimensional hip, two-dimensional knee (sagittal & frontal), and one-dimensional ankle (sagittal) kinematics during the stance phase of normal walking for all subjects. Medial and lateral knee joint contact forces (normalized to body weight) and inverse kinematics were imported into MATLAB and normalized to 101 data points. A long-short term memory network (LSTM) was created to predict knee forces using combinations of the kinematics inputs. The Grand Challenge data were used for training, while the CAMS data were used for testing. Waveform accuracy was explained by the proportion of variance and root mean square error between network predictions and in-vivo knee joint contact forces data. RESULTS: The top five networks demonstrated excellent fit with the training data, achieving RMSE \u3c 0.26BW for medial and lateral forces, R2 \u3e 0.69 for medial forces, but only R2 \u3e 0.15 for lateral forces. The overall best-selected network contained frontal hip and knee, and sagittal hip and ankle input variables and presented the finest visual waveform agreement with the in vivo data (R2=0.77, RMSE=0.27). CONCLUSIONS: The LSTM network designed in this study revealed knee joint forces could accurately be predicted by using only kinematic input variables. The network’s results outperformed most reports of root mean squared errors and correlation coefficients attained by musculoskeletal modeling and surrogate measures of KAMs

Predictive Behavior of a Computational Foot/Ankle Model through Artificial Neural Networks

Computational models are useful tools to study the biomechanics of human joints. Their predictive performance is heavily dependent on bony anatomy and soft tissue properties. Imaging data provides anatomical requirements while approximate tissue properties are implemented from literature data, when available. We sought to improve the predictive capability of a computational foot/ankle model by optimizing its ligament stiffness inputs using feedforward and radial basis function neural networks. While the former demonstrated better performance than the latter per mean square error, both networks provided reasonable stiffness predictions for implementation into the computational model

Cancellous bone and theropod dinosaur locomotion. Part II—a new approach to inferring posture and locomotor biomechanics in extinct tetrapod vertebrates

This paper is the second of a three-part series that investigates the architecture of cancellous bone in the main hindlimb bones of theropod dinosaurs, and uses cancellous bone architectural patterns to infer locomotor biomechanics in extinct non-avian species. Cancellous bone is widely known to be highly sensitive to its mechanical environment, and therefore has the potential to provide insight into locomotor biomechanics in extinct tetrapod vertebrates such as dinosaurs. Here in Part II, a new biomechanical modelling approach is outlined, one which mechanistically links cancellous bone architectural patterns with three-dimensional musculoskeletal and finite element modelling of the hindlimb. In particular, the architecture of cancellous bone is used to derive a single ‘characteristic posture’ for a given species—one in which bone continuum-level principal stresses best align with cancellous bone fabric—and thereby clarify hindlimb locomotor biomechanics. The quasi-static approach was validated for an extant theropod, the chicken, and is shown to provide a good estimate of limb posture at around mid-stance. It also provides reasonable predictions of bone loading mechanics, especially for the proximal hindlimb, and also provides a broadly accurate assessment of muscle recruitment insofar as limb stabilization is concerned. In addition to being useful for better understanding locomotor biomechanics in extant species, the approach hence provides a new avenue by which to analyse, test and refine palaeobiomechanical hypotheses, not just for extinct theropods, but potentially many other extinct tetrapod groups as well

Quantifying the Effects of Knee Joint Biomechanics on Acoustical Emissions

The knee is one of the most injured body parts, causing 18 million patients to be seen in clinics every year. Because the knee is a weight-bearing joint, it is prone to pathologies such as osteoarthritis and ligamentous injuries. Existing technologies for monitoring knee health can provide accurate assessment and diagnosis for acute injuries. However, they are mainly confined to clinical or laboratory settings only, time-consuming, expensive, and not well-suited for longitudinal monitoring. Developing a novel technology for joint health assessment beyond the clinic can further provide insights on the rehabilitation process and quantitative usage of the knee joint. To better understand the underlying properties and fundamentals of joint sounds, this research will investigate the relationship between the changes in the knee joint structure (i.e. structural damage and joint contact force) and the JAEs while developing novel techniques for analyzing these sounds. We envision that the possibility of quantifying joint structure and joint load usage from these acoustic sensors would advance the potential of JAE as the next biomarker of joint health that can be captured with wearable technology. First, we developed a novel processing technique for JAEs that quantify on the structural change of the knee from injured athletes and human lower-limb cadaver models. Second, we quantified whether JAEs can detect the increase in the mechanical stress on the knee joint using an unsupervised graph mining algorithm. Lastly, we quantified the directional bias of the load distribution between medial and lateral compartment using JAEs. Understanding and monitoring the quantitative usage of knee loads in daily activities can broaden the implications for longitudinal joint health monitoring.Ph.D

Towards Functional Preoperative Planning in Orthopaedic Surgery

Las cirugíıas del aparato locomotor suponen más de 20 millones de intervencionesanuales para la corrección de lesiones que afectan a músculos, articulaciones,ligamentos, tendones, huesos o nervios; elementos que conforman el sistema musculoesquelético. Este tipo de afecciones de la biomecánica pueden tener diversos orígenes; siendo los principales los traumatismos, las lesiones degenerativas en huesos y tejidos blandos, los malos hábitos posturales o motores, y los de origen congénito.El uso de las tecnologías actuales en los procesos de corrección de estas afecciones forma parte del día a día en los quirófanos y en la monitorización de los pacientes.Sin embargo, el uso de técnicas computacionales que permitan la preparación de las intervenciones quirúrgicas antes de proceder con la cirugía están todavía lejos de formar parte del proceso de evaluación preoperatoria en este tipo de lesiones. Por este motivo, el objetivo principal de esta tesis consiste en demostrar la viabilidad del uso de herramientas computacionales en la planificación preoperatoria de diferentes cirugías ortopédicas.Entre los tipos de cirugías más comunes, la mayor parte de ellas se centran en las articulaciones del tren inferior de la anatomía humana. Por este motivo, este trabajo se centraría en el análisis de diferentes cirugías cuya finalidad es solucionar lesiones en las principales articulaciones del tren inferior: región sacrolumbar, cadera, rodilla y tobillo.Para poder realizar el análisis de estas cirugías se hizo uso de algunas de lasherramientas computacionales más usadas habitualmente y cuya capacidad en diversos ámbitos ha sido comprobada. Se ha utilizado la reconstrucción 3D para la obtención de modelos anatómicos sobre los que comprobar la viabilidad de las cirugías. Estas reconstrucciones se basan en las imágenes médicas obtenidas mediante Tomografia Axial Computerizada (TAC) o Resonancia Magnética (RM). Las imágenes procedentes de RM permiten diferenciar todos los tejidos de la anatomía, incluyendo los blandos tales como tendones o cartílagos; mientras que los TAC facilitan la diferenciación de los huesos. Esta última es la prueba más habitual en los diagnósticos.Para su análisis y reconstrucción se hizo uso de los software Mimics v 20.0 y3-matic 11.0 (Materialise NV, Leuven, Belgium). Como alternativa para la generación de los modelos cuando no se dispone de las imágenes necesarias para realizar la reconstrucción o cuando se requiere dotar de flexibilidad a estos modelos, se recurrió al modelado en el software de análisis por elementos finitos Abaqus/CAE v.6.14 (Dassault Syst`emes, Suresnes, France). Dicho software fue además utilizado para la simulación del efecto de las diferentes cirugías sobre la región de interés. Para resalizar las simulaciones, se incluyeron en los modelos aquellos parámetros, elementos y condiciones necesarios para poder representar las caraterísticas propias de cada cirugía. Finalmente, para aquellas situaciones que requerían del análisis de datos se hizo uso de tecnologías de machine learning. La solución seleccionada para estos casos fueron las redes neuronales artificiales (ANN). Dichas redes se desarrollaronhaciendo uso del software MATLAB R2018b (MathWorks, Massachusetts, USA).El estudio de la rodilla se centra en uno de los ligamentos clave en la estabilidad de la rótula y que, sin embargo, es uno de los menos analizados hasta ahora, el ligamento medial patelofemoral. La reconstrucción de este ligamento es la principal solución clínica para solventar esta inestabilidad y diferentes cirugías utilizadas para dicho fin han sido analizadas mediante el desarrollo de un modelo paramétrico en elementos finitos que permita su simulación. En este modelo es posible adaptar la geometría de la rodilla de forma que se puedan simular diferentes condiciones que pueden afectar a la estabilidad de la rótula, tales como la displasia troclear y la patella alta.El estudio de la región sacrolumbar se centra en el análisis de diferentes posibles configuraciones para las cirugías de fusión vertebral. El análisis se centró en la fijación con tornillos y la influencia del Polimetimetacrilato (PMMA) como elemento de fijación en las vértebras. Para ello, se reconstruyó el modelo óseo de diferentes pacientes que necesitaron este tipo de intervención. Sobre estos modelos se simularon mediante elementos finitos las diferentes configuraciones consideradas de forma que se pudiera comparar su comportamiento en diferentes casos.En el caso de la cadera, el estudio se centra en el análisis de la artroplastia total de cadera, que implica el reemplazo de la articulación anatómica por una prótesis habitualmente de titanio. Cuando este tipo de cirugías es realizado, es común que surjan posteriormente problemas derivados de la disposición de la prótesis y que pueden llevar al pinzamiento entre sus componentes y, en algunas ocasiones, su dislocación.Esto ocurre cuando el rango de movimiento de la articulación es reducido. Este tipo de sucesos son más comunes cuando se realizan los movimientos de extensión externa (EE) o de rotación interna (RI) de la extremidad. El estudio se desarrolló con el objetivo de elaborar una herramienta computacional capaz de predecir este choque y dislocación basándose en el diámetro de la cabeza del femur y de los ángulos de abducción y anteversión. Para ello, se recurrió al uso de redes neuronales artificales(ANN). Se configuró una red independiente para cada movimiento (EE y RI) y cada posible evento (pinzamiento y dislocación), de forma que se obtuvieron cuatro redes completamente independientes. Para el entrenamiento y primer testeo de las redes se recurrió a un modelo paramétrico en elementos finitos de la prótesis con el que se realizaron diferentes simulaciones determinando el rango de movimiento para cada caso. Finalmente, las redes fueron de nuevo validadas con el uso de datos procedentes de pacientes que sufrieron dislocación tras ser sometidos a este tipo de cirugías.Finalmente, el estudio de la región del tobillo se centró en la lesión de la sindesmosis del tobillo. Este tipo de lesiones implica la rotura de algunos de los ligamentos que unen los principales huesos de esta articulación (tibia, peroné y astrágalo) junto con parte de la membrana intraósea, que se extiende a lo largo de la tibia y el peroné ligando ambos huesos. Cuando se produce este tipo de lesiones, es necesario recurrir a la inclusión de elementos que fijen la articulación y prevengan la separación de los huesos. Los métodos más comunes y que centran este análisis comprenden la fijación con tornillos y la fijación mediante botón de sutura. Para poder realizar un análisis que permita comparar la efectividad y incidencia de este tipo de cirugías se recurrióa la reconstruccción 3D de la articulación de un paciente que sufrió este tipo de lesión. Con este modelo geométrico, se procedió al desarrollo de diferentes modelos en elementos finitos que incluyeran cada una de las alternativas consideradas. Las simulaciones de estos modelos junto a las situaciones anatómicas y lesionadas, permitió hacer una aproximación sobre la solución quirúrgica que mejor restablece el estado incial sano de la región afectada.Locomotor system surgeries represents more the 20 million interventions per year for the correction of injuries that affect muscles, joints, ligaments, tendons, bones or nerves; elements that form themusculoskeletal system. This kind of biomechanical affections may have several sources, being the main ones traumas, bones and soft tissues degenerative injuries, poor postural or motor habits and those of congenital source. The use of current technologies in the correction process for these injuries is part of the day-to-day in the operating rooms and the monitoring of patients. However, the use of computational tools that allow preoperative planning is still far from being part of the preoperative evaluation process in this kind of injuries. For this reason, the main goal of this thesis consists in demonstrating the viability of the use of computational tools in the preoperative planning of different orthopaedic surgeries. Among the most common surgeries, most of them focus in the lower body joints of the human anatomy. For this reason, this work will focus in the analysis of different surgeries whose purpose is to solve injuries in the main joints of the lower body: lumbosacral region, hip, knee and ankle. Some of the most commonly used computational tools, and whose capability in different fields has been widely proven, were used in order to be able of performing the analysis of these surgeries. 3D reconstruction has been used for obtaining anatomical models in which the viability of the surgeries could be verified. These reconstructions are based on the medical images obtained through Computerized Tomography (CT) or Magnetic Resonance Imaging (RMI). Images from RMI allow differentiating all the tissues of the anatomy, including soft ones such as tendons and cartilages; while CT scans make easier the bones differentiation. This last procedure is the most commonly used in diagnoses. For their analysis and reconstruction software Mimics v 20.0 and 3-Matic 11.0 (Materialise NV, Leuven, Belgium) were used. As alternative for the models generation when the necessary images for the reconstruction are not available or when flexibility is required for these models, modelling in the Finite Element Analysis software Abaqus/CAE v.6.14 (Dassault Syst‘emes, Suresnes, France) was used. This software was also used for the simulation of the effects of the different surgeries in the interest region. In order to perform the simulations, those parameters, elements and conditions necessary to represent the characteristics of each surgery were included. Finally, for those situations requiring data analysis, machine learning technologies were used. The selected solution for these cases were Artificial Neural Networks (ANN). These networks were developed using the software MATLAB R2018b (MathWorks, Massachusetts, USA). The study of the knee joint focuses in one of the key ligaments for the patellar stability and which, however, is one of the least analysed so far, the medial patellofemoral ligament. The reconstruction of this ligament is the main clinical solution for solving this instability and different surgeries used for that purpose have been analysed through the development of a finite element parametric model that allows their simulation. In this model adapting knee geometry is possible so that those conditions that can affect the stability of the patella, such as trochlear dysplasia or patella alta, can be simulated. The study of the lumbosacral region focuses in the analysis of different possible configurations for spine fusion surgeries. The analyses focused in the pedicle screws fixation and the influence of polymethyl methacrylate (PMMA) as fixation element in the vertebrae. To do this, osseous models for different patients that required this kind of intervention were reconstructed. The different configurations considered were simulated on these models through finite element analysis comparing their behaviour. In the case of the hip, the study focuses in the analysis of the total hip arthroplasty, which implies replacing the anatomical joint by a prosthesis, usually made of titanium. When this kind of surgery is performed, it is common for later issues arising from the arrangement of the prosthesis and which can lead to impingement between its components and, on some occasions, their dislocation. This happens when the range of movement of the joint is limited. This kind of events are more common when the external extension (EE) or internal rotation (IR) movements of the leg are performed. The study was developed with the goal of elaborating a computational tool able to predict the impingement and dislocation based on the diameter of the head of the femur and the anteversion and abduction angles. To do this, artificial neural networks (ANN) were used. An independent network was configured for each movement (EE and IR) and for each possible event (impingement and dislocation), so that four completely independent networks. For the training and the first testing of the networks, a parametric finite element model of the hip was used; with which different simulations were performed determining the range of movement for each case. Finally, the networks were validated again with the use of data proceeding from patients that suffered dislocation after going through this kind of surgery. Finally, the study of the ankle region focused in the ankle syndesmosis injury. This kind of injuries implies the tear of some ligaments that connect the main bones of the joint (tibia, fibula and talus) together with part of the intraosseous membrane, which extends along the tibia and fibula linking both bones. When this kind of injuries happens, it is necessary to resort to the inclusion of elements that fix the joint and prevent the bones distance. The most common methods, which focus this analysis, include the screws fixation and the suture button fixation. In order to carry out an analysis that allows comparing the effectiveness and incidence of this kind of surgeries, a 3D reconstruction of the joint from a patient that suffered this kind of injury was used. With this geometrical model, different finite element models including each of the considered alternatives were developed. The simulations of these models, together with the injured and anatomical situations, allowed an approximation of the surgical solution that better restores the initial healthy state of the affected region.<br /

Integrated Neuromusculoskeletal Modeling within a Finite Element Framework to Investigate Mechanisms and Treatment of Neurodegenerative Conditions

Neurodegenerative and neurodevelopmental disorders are a group of conditions that stem from irregularities in the nervous system that lead to complications in function and movement. The goal of this work is to develop computational tools that: (1) measure the accuracy of surgical interventions in neurodegenerative and neurodevelopmental conditions, and (2) integrate neural and musculoskeletal frameworks to provide a platform to better investigate neurodegenerative and neurodevelopmental disorders. Parkinson’s disease (PD) is a neurodegenerative condition projected to affect over 1.2 million people by 2030 in the US. It is caused by atypical firing patterns in the basal ganglia region of the brain that leads to primary motor symptoms of tremor, slowness of movement, and rigidity. A potential treatment for PD is deep brain stimulation (DBS). DBS involves implanting electrodes into central brain structures to regulate the pathological signaling. Electrode placement accuracy is a key metric that helps to determine patient outcomes postoperatively. An automated measurement system was developed to quantify electrode placement accuracy in robot-assisted asleep DBS procedures (Chapter 2). This measurement system allows for precise metrics without human bias in large cohorts of patients. This measurement system was later modified to measure screw placement accuracy in spinal fusion procedures for the treatment of degenerative musculoskeletal conditions (Chapter 3). DBS is an effective treatment for PD, but it is not a cure for the cause of the disease itself. To cure neurodegenerative and neurodevelopmental diseases, the underlying disease mechanisms must be better understood. A major limitation in studying neural conditions is the infeasibility of performing in vivo experiments, particularly in humans due to ethical considerations. Computational modeling, specifically fully predictive neuromusculoskeletal (NMS) models, can help to accumulate additional knowledge about neural pathways that cannot be determined experimentally. NMS models typically include complexity in either the neuromuscular or musculoskeletal system, but not both, making it difficult or infeasible to investigate the relationship between neural signaling and musculoskeletal function. To overcome this, a fully predictive NMS model was developed by integrating NEURON software within Abaqus, a finite element (FE) environment (Chapter 4). The neural model consisted of a pool of motor neurons innervating the soleus muscle in a FE human ankle model. Software integration was verified against previously published data, and the neuronal network was verified for motor unit recruitment and rate coding, which are the two principles required for in vivo muscle generation. To demonstrate the applicability of the model to study neurodegenerative and neurodevelopmental diseases, a fully predictive mouse hindlimb NMS model was developed using the integrated framework to investigate Rett syndrome (RS) (Chapter 5). RS is a neurodevelopmental disorder caused by a mutation of the Mecp2 gene with hallmark motor symptoms of a loss of purposeful hand movement, changes in muscle tone, and a loss of speech. Recent experimental analysis has found that the axon initial segment (AIS) in mice that model RS has torsional morphology compared to wildtype littermate controls. The effects these neural morphological changes have on joint motion will be studied using the mouse NMS model. This work encompasses a range of research that uses computational models to study the underlying mechanisms and design targeted treatment options for neurodegenerative and neurodevelopmental disorders. The outcomes of this work have quantified the accuracy at which surgical interventions for these conditions can be performed and have resulted in a neuromusculoskeletal model that can be applied to understand how neural morphology, and associated changes due to these disorders, affects musculoskeletal function