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.

(1) Background: Our aim is to reveal the influence of the geometry designs on biophysical stimuli and healing patterns. The design guidelines for dental implants can then be provided. (2) Methods: A two-dimensional axisymmetric finite element model was developed based on mechano-regulatory algorithm. The history of tissue differentiation around eight selected implants can be predicted. The performance of the implants was evaluated by bone area (BA), bone-implant contact (BIC); (3) Results: The predicted healing patterns have very good agreement with the experimental observation. Many features observed in literature, such as soft tissues covering on the bone-implant interface; crestal bone loss; the location of bone resorption bumps, were reproduced by the model and explained by analyzing the solid and fluid biophysical stimuli and (4) Conclusions: The results suggested the suitable depth, the steeper slope of the upper flanks, and flat roots of healing chambers can improve the bone ingrowth and osseointegration. The mechanism related to solid and fluid biophysical stimuli were revealed. In addition, the model developed here is efficient, accurate and ready to extend to any geometry of dental implants. It has potential to be used as a clinical application for instant prediction/evaluation of the performance of dental implants.

In this study, we optimized the geometry and composition of additive-manufactured pedicle screws. Metal powders of titanium-aluminum-vanadium (Ti-6Al-4V) were mixed with reactive glass-ceramic biomaterials of bioactive glass (BG) powders. To optimize the geometry of pedicle screws, we applied a novel numerical approach to proposing the optimal shape of the healing chamber to promote biological healing. We examined the geometry and composition effects of pedicle screw implants on the interfacial autologous bone attachment and bone graft incorporation through in vivo studies. The addition of an optimal amount of BG to Ti-6Al-4V leads to a lower elastic modulus of the ceramic-metal composite material, effectively reducing the stress-shielding effects. Pedicle screw implants with optimal shape design and made of the composite material of Ti-6Al-4V doped with BG fabricated through additive manufacturing exhibit greater osseointegration and a more rapid bone volume fraction during the fracture healing process 120 days after implantation, per in vivo studies.

Effective osseointegration is a fundamental requirement in biomedical implant applications. Additive manufacturing allows precise control over implant geometry and material composition, enhancing implant design flexibility. Bioactive glass (BG) can substantially enhance bone binding and bioactivity; however, limited research has been conducted on its incorporation into additively manufactured implants. The performance of BG varies depending on the incorporation method, and the spatial and temporal evolution of its integration remains unclear. In this study, we synthesized Ti‐6Al‐4V/58S BG composites by using the selective laser melting method and systematically compared the effects of BG coating and doping in additively manufactured implants. In vivo histological results from animal tests were statistically analyzed and discussed in terms of osseointegration over 4‐ and 12‐week periods. Bone‐to‐implant contact (BIC) and bone density (BD) were used as quantitative metrics to evaluate interactions between the implants and surrounding bone. Our findings indicate that both BG‐doped and BG‐coated implants accelerated bone ingrowth during the early stages of healing. BG‐coated implants demonstrated a greater improvement than did pure 3D‐printed Ti‐6Al‐4V implants. However, the effects of BG became nonsignificant during the later healing stage (12 weeks). This study provides a foundation for systematically investigating BG incorporation methods in 3D‐printed biomedical implants and their effect on osseointegration.