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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]

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.