Mathematical and Computational Models for Osseointegration in Titanium Dental Implants: A Systematic Review

Abstract

Purpose: Mathematical and computational models play a fundamental role in understanding osseointegration in titanium dental implants, offering valuable insights into biomechanical behaviour, stress distribution, and implant stability. Materials and Methods: This systematic review examines current modelling approaches, including finite element analysis (FEA), mechanobiological frameworks, and reaction-diffusion models, assessing their effectiveness in predicting osseointegration dynamics. Despite these advancements, challenges persist in translating computational findings into clinical practice. Results: The lack of integrated models that simultaneously incorporate mechanical, biological, and biochemical factors limits the predictive accuracy of simulations. Additionally, variations in bone quality classification, implant designs, and loading conditions complicate reproducibility and standardization across studies. Personalized computational simulations, incorporating patient-specific bone properties and implant parameters, hold promise for optimizing treatment planning and improving clinical outcomes. Future research should prioritize hybrid modelling approaches that merge FEA with mechanobiological and reaction-diffusion frameworks, providing a more holistic perspective on osseointegration. Furthermore, integrating artificial intelligence and machine learning could enhance predictive accuracy by analysing large datasets and identifying complex patterns in implant behaviour. Conclusion: mathematical and computational models have significantly advanced our understanding of osseointegration in titanium dental implants. However, further refinements are needed to bridge the gap between theoretical predictions and real-world applications. By standardizing key parameters and integrating multidisciplinary modelling techniques, these tools can become more accessible and impactful in dental implantology, ultimately improving implant design, surgical planning, and patient-specific treatment strategies.