MultiSim has integrated the ongoing musculoskeletal research within the University of Sheffield into the grand vision of the grant. This has resulted in the development of models at all scales and integration into a multi-scale platform, with three application workflows:
- A workflow for the prediction of femoral fractures in osteoporotic patients. We have been able to show that patient-specific predictions of the risk of fracture in post-menopausal woman were more accurate with computer simulations than current clinical standard.
- The development of computational tools for children musculoskeletal diseases such as the diagnosis of unexplained fractures in the child.
- The development of a murine platform for the complete study of multiscale modelling and the possibility to validate the tools developed at each scale and their integration from the cell to the organ levels.
A general hypermodelling framework concept was constructed to enable multiscale modelling across space and time by defining inputs and outputs between scales within each application. In addition, a general IT infrastructure is being developed to provide successful applications as an online service to external users by establishing a workflow of software applications using Taverna.
The diagram below shows how the original MultiSim work packages feed into the application workflows developed.Workflow and work packages relationship revised
Application 1 Prediction of Osteoporotic Fractures and Application 3 Murine Model, have subsequently been developed into mature workflows illustrated in the two videos below.
Application 1 workflow: Prediction of osteoporotic fracture
This video shows how movement data and image data from image data from MRI and CT scans are be combined to develop musculoskeletal kinematic models and finite element models to predict the risk of fracture.
Publications related to Application 1
Di Marco, R., Scalona, E., Pacilli, A., Cappa, P., Mazzà, C., Rossi, S. (2018), “How to choose and interpret similarity indices to quantify the variability in gait joint kinematics”, International Biomechanics, 5 (1), pp 1-8, URL: https://doi.org/10.1080/23335432.2018.1426496
Modenese, L., Montefiori, E., Wang, A., Wesarg, S., Viceconti, M., Mazzà, C. (2018), “Investigation of the dependence of joint contact forces on musculotendon parameters using a codified workflow for image-based modelling”, Journal of Biomechanics, 16 (3), pp216-223, URL: https://doi.org/10.1016/j.jbiomech.2018.03.039
Tamburini, P., Storm, F., Buckley, C., Bisi, M. C., Stagni, R., Mazzà, C (2018), “Moving from laboratory to real life conditions: influence on the assessment of variability and stability of gait”, Gait and Posture, 59, pp 248-252, URL: https://doi.org/10.1016/j.gaitpost.2017.10.024
Viceconti, M., Qasim, M., Bhattacharya, P., Li. X. (2018), “Are CT-Based Finite Element Model Predictions of Femoral Bone Strengthening Clinically Useful?”, Current Osteoporosis Reports, URL: https://doi.org/10.1007/s11914-018-0438-8
Shahabpoor, E., Pavic, A. (2018), “Estimation of Vertical Walking Ground Reaction Force in Real-life Environment from Single IMU Sensor”, Journal of Biomechanics, URL: https://doi.org/10.1016/j.jbiomech.2018.08.015
Shahapoor, E., Pavic, A., Brownjohn, J. M. W., Billings, S. A., Guo, L., Bocian, M. (Published), “Real-Life Measurement of Tri-Axial Walking Ground Reaction Forces Using Optimal Network of Wearable Inertial Measurement Units”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26(6), URL: https://doi.org/10.1109/TNSRE.2018.2830976 https://purehost.bath.ac.uk/ws/portalfiles/portal/183734057/JP15_draft_IEEE_20180207.pdf
Dall’Ara, E., Eastell, R., Viceconti, M., Pahr, D., Yang, L. (2016), “Experimental Validation of DXA-based Finite Element models for prediction of femoral strength”, Journal of the Mechanical Behavior of Biomedical Materials, 63, pp 17-25, URL: http://dx.doi.org/10.1016/j.jmbbm.2016.06.004
Guo, Y., Storm, F., Zhao, Y., Billings, S. A., Pavic, A., Mazzà, C., Guo, L. (2017), “A New Proxy Measurement Algorithm with Applications to Vertical Ground Reaction Forces with Wearable Sensors”, Sensors, 17 (10), pp 2181-2195, URL: https://doi.org/10.3390/s17102181
Hannah I., Montefiori E., Modenese L., Prinold, J., Viceconti M., Mazzà C. (2017), “Sensitivity of a juvenile subject-specific musculoskeletal model of the ankle joint to the variability of operator dependent input”, Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, 231 (5), pp415-422, URL: http://dx.doi.org/10.1177/0954411917701167
Moissenet, F., Modenese, L., Dumas, R. (2017), “Alterations of musculoskeletal models for a more accurate estimation of lower limb joint contact forces during normal gait: A systematic review”, Journal of Biomechanics, 63, pp 8-20, URL: http://doi.org/10.1016/j.jbiomech.2017.08.025
Shahabpoor, E., Pavic, A. (2017), “Measurement of Walking Ground Reactions in Real-Life Environments: A Systematic Review of Techniques and Technologies”, Sensors, 17 (9), 2085, URL: https://doi.org/10.3390/s17092085
Hannah, I., Sawacha, Z., Guiotto, A., Mazzà, C. (2016), “Relationship between sagittal plane kinematics, foot morphology and vertical forces applied to three regions of the foot”, International Biomechanics, 3 (1), pp 50-56, URL: http://dx.doi.org/10.1080/23335432.2016.1229135
Lamberto, G., Martelli, S., Cappozzo, A., Mazzà, C. (2016), “To what extent is joint and muscle mechanics predicted by musculoskeletal models sensitive to soft tissue artefacts?”, Journal of Biomechanics, Available online 24 August 2016, URL: http://dx.doi.org/10.1016/j.jbiomech.2016.07.042
Qasim, M., Farinella, G., Zhang, J., Li, X., Yang, L., Eastell, R., Viceconti, M. (2016), “Patient-Specific Finite Element Estimated Femur Strength as a Predictor of the Risk of Hip Fracture: The Effect of Methodological Determinants”, Osteoporosis International, 27 (9), pp 2815-2822, URL: https://dx.doi.org/10.1007/s00198-016-3597-4
Storm, F., Buckley, C., Mazzà, C. (2016), “Gait event detection in indoor and outdoor settings: accuracy of two inertial sensors based methods”, Gait and Posture, 50, pp 42-46, URL: http://dx.doi.org/10.1016/j.gaitpost.2016.08.012
Application 3 workflow: Murine model
This video shows how the murine models developed in MultiSim can be used to improve the current preclinical assessment of new interventions through better experiments, better endpoints and validated multiscale models.
Dall’Ara, E., Peña-Fernández, M., Palanca, M., Giorgi, M., Cristofolini, L., Tozzi, G. (2017), “Precision of DVC approaches for strain analysis 1 in bone imaged with μCT at different dimensional levels”, Frontiers in Materials: Mechanics of Materials, 4, Article 31, URL: https://doi.org/10.3389/fmats.2017.00031
Oliviero, S., Y., Lu, Y., Viceconti, M., Dall’Ara, E. (2017), “Effect of integration time on the morphometric, densitometric and mechanical properties of the mouse tibia”, Journal of Biomechanics, 65, pp 203-211, URL: https://doi.org/10.1016/j.jbiomech.2017.10.026Lu, Y., Boudiffa, M., Dall’Ara, E., Liu, Y., Bellantuono, I., Viceconti, M. (2017), “Longitudinal effects of Parathyroid Hormone treatment on morphological, densitometric and mechanical properties of mouse tibia”, Journal of the Mechanical Behavior of Biomedical Materials, 75, pp 244-251,
Giorgi, M., Verbruggen, S. W., Lacroix, D. (2016), “In silico bone mechanobiology: Modelling a multi-faceted biological system”, WIREs Systems Biology and Medicine, 8 (6), pp 485-505, URL: http://dx.doi.org/10.1002/wsbm.1356
Lu, Y., Boudiffa, M., Dall’Ara, E., Bellantuono, I., Viceconti, M. (2016), “Development of a protocol to quantify local bone adaptation over space and time: Quantification of reproducibility”, Journal of Biomechanics, 49 (10), pp 2095-2099, URL: http://dx.doi.org/10.1016/j.jbiomech.2016.05.022
Wittkowske, C., Reilly, G. C., Lacroix, D., Perrault, C. M. (2016), “In vitro bone cell models: Impact of fluid shear stress on bone formation”, Frontiers in Bioengineering and Biotechnology, 4 (87), 22 pages, URL: https://doi.org/10.3389/fbioe.2016.00087