Explore the Dynamic Characteristics of Dental Structures: Modelling, Remodelling, Implantology and Optimisation

The properties of a structure can be both narrowly and broadly described. The mechanical properties, as a narrow sense of property, are those that are quantitative and can be directly measured through experiments. They can be used as a metric to compare the benefits of one material versus another. Examples include Young’s modulus, tensile strength, natural frequency, viscosity, etc. Those with a broader definition, can be hardly measured directly. This thesis aims to study the dynamic properties of dental complex through experiments, clinical trials and computational simulations, thereby bridging some gaps between the numerical study and clinical application. The natural frequency and mode shapes, of human maxilla model with different levels of integrities and properties of the periodontal ligament (PDL), are obtained through the complex modal analysis. It is shown that the comprehensiveness of a computational model significantly affects the characterisation of dynamic behaviours, with decreasing natural frequencies and changed mode shapes as a result of the models with higher extents of integrity and preciseness. It is also found that the PDL plays a very important role in quantifying natural frequencies. Meanwhile, damping properties and the heterogeneity of materials also have an influence on the dynamic properties of dental structures. The understanding of dynamic properties enables to further investigate how it can influence the response when applying an external stimulus. In a parallel preliminary clinical trial, 13 patients requiring bilateral maxillary premolar extractions were recruited and applied with mechanical vibrations of approximately 20 g and 50 Hz, using a split mouth design. It is found that both the space closure and canine distalisation of the vibration group are significantly faster and higher than those of the control group (p<0.05). The pressure within the PDL is computationally calculated to be higher with the vibration group for maxillary teeth for both linguo-buccal and mesial-distal directions. A further increased PDL response can be observed if increasing the frequency until reaching a local natural frequency. The vibration of 50 Hz or higher is thus approved to be a potential stimulus accelerating orthodontic treatment. The pivotal role of soft tissue the PDL is further studied by quantitatively establishing pressure thresholds regulating orthodontic tooth movement (OTM). The centre of resistance and moment to force ratio are also examined via simulation. Distally-directed tipping and translational forces, ranging from 7.5 g to 300 g, are exerted onto maxillary teeth. The hydrostatic stress is quantified from nonlinear finite element analysis (FEA) and compared with normal capillary and systolic blood pressure for driving the tissue remodelling. Localised and volume-averaged hydrostatic stress are introduced to describe OTM. By comparing with clinical results in past literature, the volume average of hydrostatic stress in PDL was proved to describe the process of OTM more indicatively. Global measurement of hydrostatic pressure in the PDL better characterised OTM, implying that OTM occurs only when the majority of PDL volume is critically stressed. The FEA results provide new insights into relevant orthodontic biomechanics and help establish optimal orthodontic force for a specific patient. Implant-supported fixed partial denture (FPD) with cantilever extension can transfer excessive load to the bone surrounding implants and stress/strain concentration which potentially leads to bone resorption. The immediate biomechanical response and long-term bone remodelling outcomes are examined. It is indicated that during the chewing cycles, the regions near implant necks and apexes experience high von Mises stress (VMS) and equivalent strain (EQS) than the middle regions in all configurations, with or without the cantilever. The patient-specific dynamic loading data and CT based mandibular model allow us to model the biomechanical responses more realistically. The results provide the data for clinical assessment of implant configuration to improve longevity and reliability of the implant-supported FPD restoration. On the other hand, the results show that the three-implant supported and distally cantilevered FPDs see noticeable and continuous bone apposition, mainly adjacent to the cervical and apical regions. The bridged and mesially cantilevered FPDs show bone resorption or no visible bone formation in some areas. Caution should be taken when selecting the FPD with cantilever due to the risk of overloading bone resorption. The position of FPD pontics plays a critical role in mechanobiological functionality and bone remodelling. As an important loading condition of dental biomechanics, the accurate assignment of masticatory loads has long been demanded. Methods involving different principles have been applied to acquire or assess the muscular co-activation during normal or unhealthy stomatognathic functioning. Their accuracy and capability of direct quantification, especially when using alone, are however questioned. We establish a clinically validated Sequential Kriging Optimisation (SKO) model, coupled with the FEM and in vivo occlusal records, to further the understanding of muscular functionality following a fibula free flap (FFF) surgery. The results, within the limitations of the current study, indicates the statistical advantage of agreeing occlusal measurements and hence the reliability of using the SKO model over the traditionally adopted optimality criteria. It is therefore speculated that mastication is not optimally controlled to a definite degree. It is also found that the maximum muscular capacity slightly decreases whereas the actual muscle forces fluctuate over the 28-month period

Computational Techniques to Predict Orthopaedic Implant Alignment and Fit in Bone

Among the broad palette of surgical techniques employed in the current orthopaedic practice, joint replacement represents one of the most difficult and costliest surgical procedures. While numerous recent advances suggest that computer assistance can dramatically improve the precision and long term outcomes of joint arthroplasty even in the hands of experienced surgeons, many of the joint replacement protocols continue to rely almost exclusively on an empirical basis that often entail a succession of trial and error maneuvers that can only be performed intraoperatively. Although the surgeon is generally unable to accurately and reliably predict a priori what the final malalignment will be or even what implant size should be used for a certain patient, the overarching goal of all arthroplastic procedures is to ensure that an appropriate match exists between the native and prosthetic axes of the articulation. To address this relative lack of knowledge, the main objective of this thesis was to develop a comprehensive library of numerical techniques capable to: 1) accurately reconstruct the outer and inner geometry of the bone to be implanted; 2) determine the location of the native articular axis to be replicated by the implant; 3) assess the insertability of a certain implant within the endosteal canal of the bone to be implanted; 4) propose customized implant geometries capable to ensure minimal malalignments between native and prosthetic axes. The accuracy of the developed algorithms was validated through comparisons performed against conventional methods involving either contact-acquired data or navigated implantation approaches, while various customized implant designs proposed were tested with an original numerical implantation method. It is anticipated that the proposed computer-based approaches will eliminate or at least diminish the need for undesirable trial and error implantation procedures in a sense that present error-prone intraoperative implant insertion decisions will be at least augmented if not even replaced by optimal computer-based solutions to offer reliable virtual “previews” of the future surgical procedure. While the entire thesis is focused on the elbow as the most challenging joint replacement surgery, many of the developed approaches are equally applicable to other upper or lower limb articulations

Artificial intelligence applications in implant dentistry: A systematic review

Statement of problem. Artificial intelligence (AI) applications are growing in dental implant procedures. The current expansion and performance of AI models in implant dentistry applications have not yet been systematically documented and analyzed. Purpose. The purposeof this systematic review was to assess the performance of Ai models in implant dentistry for implant type recognition, implant success prediction by using patient risk factors and ontology criteria, and implant design optimization combining finite element analysis (FEA) calculations and AI models. Material and methods. An electronic systematic review was completed in 5 databases: MEDLINE/ PubMed, EMBASE, World of Science, Cochrane, and Scopus. A manual search was also conducted. Peerreviewed studies that developed AI models for implant type recognition, implant success prediction, and implant design optimization were included. The search strategy included articles published until February 21, 2021. Two investigators independently evaluated the quality of the studies by applying the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Quasi-Experimental Studies (nonrandomized experimental studies). A third investigator was consulted to resolve lack of consensus. Results. Seventeen articles were included: 7 investigations analyzed AI models for implant type recognition, 7 studies included AI prediction models for implant success forecast, and 3 studies evaluated AI models for optimization of implant designs. The AI models developed to recognize implant type by using periapical and panoramic images obtained an overall accuracy outcome ranging from 93.8% to 98%. The models to predict osteointegration success or implant success by using different input data varied among the studies, ranging from 62.4% to 80.5%. Finally, the studies that developed AI models to optimize implant designs seem to agree on the applicability of AI models to improve the design of dental implants. This improvement includes minimizing the stress at the implant-bone interface by 36.6% compared with the finite element model; optimizing the implant design porosity, length, and diameter to improve the finite element calculations; or accurately determining the elastic modulus of the implant-bone interface. Conclusions. AI models for implant type recognition, implant success prediction, and implant design optimization have demonstrated great potential but are still in development. Additional studies are indispensable to the further development and assessment of the clinical performance of AI models for those implant dentistry applications reviewed

Shear-promoted drug encapsulation into red blood cells: a CFD model and μ-PIV analysis

The present work focuses on the main parameters that influence shear-promoted encapsulation of drugs into erythrocytes. A CFD model was built to investigate the fluid dynamics of a suspension of particles flowing in a commercial micro channel. Micro Particle Image Velocimetry (μ-PIV) allowed to take into account for the real properties of the red blood cell (RBC), thus having a deeper understanding of the process. Coupling these results with an analytical diffusion model, suitable working conditions were defined for different values of haematocrit

Craniofacial Growth Series Volume 56

https://deepblue.lib.umich.edu/bitstream/2027.42/153991/1/56th volume CF growth series FINAL 02262020.pdfDescription of 56th volume CF growth series FINAL 02262020.pdf : Proceedings of the 46th Annual Moyers Symposium and 44th Moyers Presymposiu

Computational Modeling of Fracture Failure in Mineralized and Prosthetic Biomaterials

Natural mineralized tissues, e.g., teeth and bone, have the capacity to tolerate the daily physiological loading. However, due to their high mineralized composition, they have been recognized as a class of relatively brittle biomaterials. The inherent brittle nature and fairly high susceptibility to mechanical failure present a more critical problem in biomedical research field. To replace such diseased or damaged mineralized tissues, prosthetic materials are largely applied in the areas of dental and osteo clinical treatments. Ceramic materials provide numerous favourable characteristics, including biocompatibility and chemical resistance. In addition to the dental industry, applications of osteofixation/osteosynthiesis devices are considered fundamental to stabilize various treatments of bone defects for promoting osteointegration and reconstruction. However, clinical observations and specialized literature have revealed that dental restorative materials and prosthetic fixation devices are often subject to high stress, leading to fracture either by catastrophic overloading or cyclic fatigue failure. The aim of this thesis is to develop a computational modelling framework on the basis of the extended finite element method (XFEM) to investigate the fracture behaviors of mineralised and synthetic biomaterials in various medical applications. The XFEM modelling results are validated by being compared with the in-vitro experiments and/or clinical observations. Through the research in this thesis studies, XFEM has been demonstrated to be a powerful tool to analyse fracture behaviors in the bio-structures subjected to not only static loadings but also cyclic loadings. The outcomes generated in this thesis help gain some insightful understanding failure of the native or prosthetic structures, which is anticipated to provide some clinical guidelines for the design optimisation of patient-specific prosthetic devices to ensure their reliability and longevity