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Simple Summary: Spinal manipulation is recommended by clinical guidelines to treat low back pain. However, our understanding of how spinal manipulation alleviates pain and knowledge of the role that viscoelastic tissue properties play in the dampening of applied spinal manipulation forces in vivo is limited. Superficial and deep intramuscular pressures and applied forces were measured using miniature pressure catheters and a force transducer during low back spinal manipulation using a clinically available spinal manipulation device (Activator V) or a feedback motor in a deeply anesthetized animal model. Viscoelastic properties of muscle and other soft tissues greatly diminished applied spinal manipulative forces as well as intramuscular pressures in deep spinal tissues. These animal model findings may eventually have clinical implications or help to determine how spinal manipulation helps to alleviate muscular low back pain, particularly if it is determined that certain thresholds of deep tissue forces or intramuscular pressure changes are required to initiate a cascade of biological mechanisms resulting in a positive clinical treatment response. Current knowledge regarding biomechanical in vivo deep tissue measures related to spinal manipulation remain somewhat limited. More in vivo animal studies are needed to better understand the effects viscoelastic tissue properties (i.e., dampening) have on applied spinal manipulation forces. This new knowledge may eventually help to determine whether positive clinical outcomes are associated with particular force thresholds reaching superficial and/or deep spinal tissues. A computer-controlled feedback motor and a modified Activator V device with a dynamic load cell attached were used to deliver thrust spinal manipulations at various magnitudes to the L7 spinous process in deeply anesthetized animals. Miniature pressure catheters (Millar SPR-1000) were inserted unilaterally into superficial and deep multifidi muscles. Measurements of applied mechanical forces and superficial/deep multifidi intramuscular pressure changes were recorded during spinal manipulations delivered in vivo. Manipulative forces and net changes in intramuscular pressures reaching deep spinal tissues are greatly diminished by viscoelastic properties of in vivo tissues, which could have possible clinical safety and/or mechanistic implications. [ABSTRACT FROM AUTHOR]

Study Design: The effects of bone cement placement, volume, and bone density on the degree of biomechanical reinforcement on cadaveric vertebral bodies were studied using experimentally calibrated detailed finite element models.Objectives: To investigate the efficacy of prophylactic vertebroplasty on intact vertebral bodies with respect to biomechanical recovery and fracture risk reduction.Summary Of Background Data: Vertebroplasty is a potentially effective fracture prevention treatment, but the risk of complications due to cement leakage must be minimized. Therefore, the least amount of bone cement required to improve vertebral strengths to low fracture risk levels need to be determined.Methods: Six different polymethyl methacrylate volumes--1, 2.5, 3.5, 5, 7.5 and 9 cm--were virtually implanted into previously validated vertebral body finite element models, following bipedicular and posterolateral vertebroplasty approaches. Stiffness and fracture load of the treated and untreated vertebral body models under uniaxial compression were determined.Results: Greater augmentation effects were observed for vertebral bodies with average quantitative computed tomography densities below 0.1 g/cm injected with polymethyl methacrylate volumes higher than 20% compared to lower injection volumes and higher bone densities, as well as for the bipedicular approach versus posterolateral. Vertebral bodies at high risk of fracture required at least 20% fill of polymethyl methacrylate to improve the mechanical integrity of vertebral bodies to low fracture risk levels, whereas 5% to 15% polymethyl methacrylate volumes were needed for the medium-risk vertebral bodies.Conclusion: Prophylactic vertebroplasty can be effective in reducing fracture risk. However, for the polymethyl methacrylate volume (20%) required for the successful reinforcement of high-risk vertebral bodies, the risk of complications will be as high as that for current vertebroplasty procedure for fracture repair. Therefore, alternative materials have to be investigated for prophylactic vertebroplasty. Furthermore, bipedicular vertebroplasty is the recommended approach due to its higher strengthening effect and easier surgical access than the posterolateral case. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions Surgical planning for orthognathic procedures demands swift and accurate biomechanical modeling of facial soft tissues. Efficient simulations are vital in the clinical pipeline, as surgeons may iterate through multiple plans. Biomechanical simulations typically use the finite element method (FEM). Prior works divide FEM simulations into increments to enhance convergence and accuracy. However, this practice elongates simulation time, thereby impeding clinical integration. To accelerate simulations, deep learning (DL) models have been explored. Yet, previous efforts either perform simulations in a single step or neglect the temporal aspects in incremental simulations.This study investigates the use of spatiotemporal incremental modeling for biomechanics simulations of facial soft tissue.We implement the method using a graph neural network. Our method synergizes spatial features with temporal aggregation using DL networks trained on incremental FEM simulations from 17 subjects that underwent orthognathic surgery.Our proposed spatiotemporal incremental method achieved a mean accuracy of 0.37 mm with a mean computation time of 1.52 s. In comparison, a spatial‐only incremental method yielded a mean accuracy of 0.44 mm and a mean computation time of 1.60 s, while a spatial‐only single‐step method yielded a mean accuracy of 0.41 mm and a mean computation time of 0.05 s.Statistical analysis demonstrated that the spatiotemporal incremental method reduced mean errors compared to the spatial‐only incremental method, emphasizing the importance of incorporating temporal information in incremental simulations. Overall, we successfully implemented spatiotemporal incremental learning tailored to simulate soft tissue deformation while substantially reducing simulation time compared to FEM. [ABSTRACT FROM AUTHOR]

An introduction to the journal is presented in which the editor discusses developments on the use of computational methods in orthopaedic biomechanics.

2021, 10, 865 37 of 99 Hudson, Geoffrey Iacono, Pierluigi Huebner, Hanna Iacopi, Elisabetta Hueso, Miguel Iaculli, Flavia Hugelshofer, Michael Ianiro, Gianluca Hughes, Christopher V. Iannaccone, Andrea Hughes, David T. Iannuzzi, Clara Hughes, Jennifer Iasevoli, Felice Huh, Joo Young Iatí, Giuseppe Huhn, Ragnar Ibáñez-Costa, Alejandro Huling, Jennifer Ichibori, Yasuhiro Hull, Chris Ichida, Fukiko Hull, Louise Ichikawa, Hajime Hüls, Anke Ichikawa, Tetsuo Hulsebosch, Claire E. Ickmans, Kelly Hultcrantz, Rolf Idicula, Titto T Hultdin, Johan Idolazzi, Luca Hultin, Magnus Idrovo, Juan-Pablo Hultqvist, Jenny Ierardi, Enzo Humenberger, Michael Ifantides, Cristos Humpel, Christian Iga, Junichi Humrich, Jens Y. Iglesias-Osma, Maria Carmen Hung, Kenneth Iglseder, Bernhard Hung, Li-Fang Ignee, Andre Hunt, Beverley Jane Ignjatovic, Vera Hunt, William R. Igoe, Ann Hunter, Elizabeth G. Ihemelandu, Chukwuemeka Huotari, Matti Iijima, Kazutoshi Hupin, David Iijima, Takehiko Huppler, Anna R. Ijare, Omkar B. Hurford, William Ikada, Yoshito Hurtado-Fernández, Elena Ikeda, Keigo Hurtubise, Karen Ikeda, Youko Husby, Paul J. A. Ikeda, Yuki Huselstein, Céline Ikegaya, Naoki Hussain, Mulazim Ikezawa, Kenji Hussain, Rashad Ikuta, Toshikazu Hussain, Sultana Monira Ilatovskaya, Daria Husson, Olga Ildefonso, Cristhian J. Hutt, Axel Iliadi, Anna Huttner, Inken Iliescu, Cezar A. Hutton, George Joseph Iliou, Marie Christine Huynh-Do, Uyen Illek, Beate Hwang, Hun-Gyu Illuminati, Giulio Hwang, Hyeon Seok Ilves, Outi Hwang, Jae-Joon Imai, Chihaya Hwang, Ki-Chul Imai, Hisao Hwang, Mark Imai, Yoichi Hwang, Seung Rim Imamura, Masahiro Hwang, Yong Il Imamura, Teruhiko Hwu, Ruth S. Imamura, Yoshiki Hyatt, Hayden Imataki, Osamu Hyer, Steve Imbert, Véronique Hyink, Deborah Pinson Imbery, Terence A. Hynnekleiv, Torfinn Immins, Tikki Hytti, Maria Impellizzeri, Alessandra Iaccarino, Guido Impellizzeri, Pietro Iaccino, Enrico Inaki, Noriyuki J. Clin. [Extracted from the article]