ArticleInternational journal of molecular sciences2023
Prediction of Bone Healing around Dental Implants in Various Boundary Conditions by Deep Learning Network.
Article in International journal of molecular sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- In Silico possibilities to understand peri-implant bone healing- state of the Art.International journal of implant dentistry · 2025Review
- Comparing Manual and ChatGPT Deep Research on Systematic Search and Selection in the PubMed Database on the Topic of Dental Implantology.International journal of dentistry · 2025Article
- A macro-micro FE and ANN framework to assess site-specific bone ingrowth around the porous beaded-coated implant: an example with BOX® tibial implant for total ankle replacement.Medical & biological engineering & computing · 2024Article
- Radiographic and histologic assessment of osseointegration for surface-treated titanium dental implants: An experimental study in dogs.Journal of dental research, dental clinics, dental prospects · 2024Article
- Multiscale morphological analysis of bone microarchitecture around Mg-10Gd implants.Bioactive materials · 2023Article
- Synergistic Integration of Deep Neural Networks and Finite Element Method with Applications of Nonlinear Large Deformation Biomechanics.Computer methods in applied mechanics and engineering · 2023Article
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Authors and funding
5 authors.
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Abstract
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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Registered trials
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