A Hybrid Model for Predicting Bone Healing around Dental Implants

Kung, Pei-Ching; Chien, Shih-Shun; Tsou, Nien‐Ti

doi:10.3390/ma13122858

Cited by 5 publications

(3 citation statements)

References 52 publications

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“…First, in this study, we assumed that the mechanism of bone healing can be well described by the current mechano-regulation theory. There were some more factors that affect bone healing that can be considered, for example, random-walk stem cell migration [28], extending the model from two-dimensional axis-symmetry to three-dimensional [29], toxicological concerns [30], chemical compounds application such as nano-antioxidants [31] for enhancing bioactivity, and health concerns such as infection [32] and allergic reaction [33]. Second, the prediction accuracy can be further improved by optimizing all the hyperparameters and network architecture.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Bone Healing around Dental Implants in Various Boundary Conditions by Deep Learning Network

Kung

Hsu

Yang

et al. 2023

IJMS

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Tissue differentiation varies based on patients’ conditions, such as occlusal force and bone properties. Thus, the design of the implants needs to take these conditions into account to improve osseointegration. However, the efficiency of the design procedure is typically not satisfactory and needs to be significantly improved. Thus, a deep learning network (DLN) is proposed in this study. A data-driven DLN consisting of U-net, ANN, and random forest models was implemented. It serves as a surrogate for finite element analysis and the mechano-regulation algorithm. The datasets include the history of tissue differentiation throughout 35 days with various levels of occlusal force and bone properties. The accuracy of day-by-day tissue differentiation prediction in the testing dataset was 82%, and the AUC value of the five tissue phenotypes (fibrous tissue, cartilage, immature bone, mature bone, and resorption) was above 0.86, showing a high prediction accuracy. The proposed DLN model showed the robustness for surrogating the complex, time-dependent calculations. The results can serve as a design guideline for dental implants.

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Section: Discussionmentioning

confidence: 99%

Prediction of Bone Healing around Dental Implants in Various Boundary Conditions by Deep Learning Network

Kung

Hsu

Yang

et al. 2023

IJMS

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“…In addition, since the theory of SED based bone remodelling approach 64,[125][126][127][128][129] came into existence, there has not been much significant development in the remodelling approaches. However, most of the models developed henceforth are mostly subtle need-based modifications 93,95,125,[130][131][132][133][134] of the existing formulation of Huiskes and co-workers 64,85 and the Stanford model 84,92,135 of bone adaptation.…”

Section: Studies On Internal Bone Remodelling and External Bone Remod...mentioning

confidence: 99%

Application of finite element analysis to tissue differentiation and bone remodelling approaches and their use in design optimization of orthopaedic implants: A review

Ghosh

Chanda

Chakraborty

2022

Numer Methods Biomed Eng

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Post-operative bone growth and long-term bone adaptation around the orthopaedic implants are simulated using the mechanoregulation based tissuedifferentiation and adaptive bone remodelling algorithms, respectively. The primary objective of these algorithms was to assess biomechanical feasibility and reliability of orthopaedic implants. This article aims to offer a comprehensive review of the developments in mathematical models of tissuedifferentiation and bone adaptation and their applications in studies involving design optimization of orthopaedic implants over three decades. Despite the different mechanoregulatory models developed, existing literature confirm that none of the models can be highly regarded or completely disregarded over each other. Not much development in mathematical formulations has been observed from the current state of knowledge due to the lack of in vivo studies involving clinically relevant animal models, which further retarded the development of such models to use in translational research at a fast pace. Future investigations involving artificial intelligence (AI), soft-computing techniques and combined tissue-differentiation and bone-adaptation studies involving animal subjects for model verification are needed to formulate more sophisticated mathematical models to enhance the accuracy of pre-clinical testing of orthopaedic implants.

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“…Masticatory forces are transferred from superstructures or artificial teeth to bone via implants. A biomechanical model was introduced in the study of Kung et al for the prediction of bone healing around a dental implant system composed of an artificial crown cemented to a one-body implant, where an abutment and an implant are fused together [ 10 ]. Various materials are being developed for superstructures that are usually cemented to abutments.…”

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confidence: 99%