Submodelling Approach to Screw-to-Bone Interaction in Additively Manufactured Subperiosteal Implant Structures

Gabriel Castrillo, Alberto Carnicero, Ricardo Perera

Full text link: http://pubmed.ncbi.nlm.nih.gov/36541118/

Submodelling Approach to Screw-to-Bone Interaction in Additively Manufactured Subperiosteal Implant Structures

1. Scientific Reference

• Study Title: Submodelling Approach to Screw-to-Bone Interaction in Additively Manufactured Subperiosteal Implant Structures
• Authors: Gabriel Castrillo, Alberto Carnicero, Ricardo Perera
• Journal: Communications in Numerical Methods in Engineering
• Year of Publication: 2022
• DOI: 10.1002/cnm.3672

2. Scientific Background

The management of patients presenting with severe maxillary atrophy remains one of the greatest challenges in contemporary implant dentistry. Recent advances in digital workflows, computer-aided design (CAD), and additive manufacturing have enabled the development of patient-specific subperiosteal implant structures (AMSIS), providing an alternative treatment option for complex anatomical situations where conventional implant placement may be difficult. As these customized devices become increasingly sophisticated, understanding their biomechanical behavior has become essential for optimizing their design and long-term performance.

Most published investigations have primarily focused on the structural integrity of the implant framework itself, while comparatively little attention has been given to the mechanical interaction between fixation screws and the surrounding maxillary bone. Because screw fixation plays a fundamental role in primary stability and load transfer, accurately representing this interface is critical for realistic biomechanical simulations. Within this context, the present study explores an advanced finite element modelling strategy aimed at improving the analysis of bone–screw interactions while maintaining computational efficiency.

3. Study Objective

The purpose of this study was to develop and evaluate a finite element methodology capable of accurately assessing the mechanical interaction between fixation screws and the maxillary bone in additively manufactured subperiosteal implant structures. Specifically, the authors investigated how detailed modelling of the screw–bone interface influences displacement patterns, reaction forces, and stress distribution within both the implant and the surrounding bone. The study also examined the biomechanical effect of complete osseointegration within the proposed numerical model.

4. Methodology

This investigation is a preclinical computational study based on finite element analysis (FEA). The authors implemented a submodelling approach, combining a global finite element model of the customized subperiosteal implant with highly detailed local models representing the threaded interface between fixation screws and the maxillary bone. This strategy allows precise stress evaluation around the screw threads while significantly reducing computational requirements compared with a fully detailed model.

Different simulation scenarios were compared, including models that either incorporated or neglected detailed screw–bone interaction, as well as conditions representing complete osseointegration. The primary outcome measures included implant displacement, reaction forces, and stress distribution within the implant, cortical bone, and trabecular bone.

The abstract does not specify the number of models analysed, loading conditions, material properties, or additional computational parameters.

5. Main Findings

The numerical simulations demonstrated that explicitly modelling the interaction between fixation screws and the surrounding bone substantially alters the biomechanical response compared with simplified computational models. Both implant displacement and reaction forces differed significantly when the screw thread geometry and its interaction with bone were represented in greater detail.

The highest stress concentrations were observed within the cortical bone, where calculated values approached the maximum strength reported by the authors. In contrast, trabecular bone appeared to have only a limited influence on the overall mechanical behaviour of the system under the simulated conditions. Within the implant framework itself, calculated stresses remained below the fatigue stress limit considered in the study, suggesting favourable mechanical performance from a structural perspective.

Comparisons between models before and after simulated osseointegration showed a reduction in stress levels once complete osseointegration was assumed. Furthermore, the authors found that the initial geometry of the screw threads strongly influenced stress distribution within the cortical bone, highlighting the importance of accurately modelling thread geometry in biomechanical analyses of patient-specific subperiosteal implants.

6. Clinical Analysis

Rather than providing direct clinical evidence, this study offers valuable methodological insight into the biomechanical assessment of customized subperiosteal implant systems. The findings suggest that simplified representations of the screw–bone interface may underestimate or misrepresent mechanical behaviour, potentially affecting the interpretation of finite element analyses used during implant design and preoperative planning.

One of the most relevant observations is the predominant mechanical role of cortical bone during load transfer. Since stress concentrations were primarily located within this layer, accurate modelling of cortical engagement appears particularly important when evaluating fixation stability in severe maxillary atrophy. Conversely, the relatively limited contribution of trabecular bone observed in this computational model suggests that cortical support may have greater influence on overall mechanical performance under the simulated conditions. However, this conclusion should remain confined to the assumptions of the numerical model and should not be generalized to every clinical scenario.

The reduction in stress following simulated osseointegration also provides useful biomechanical insight into the evolution of implant loading over time. Although derived exclusively from computational simulations, these findings illustrate the potential value of incorporating biological healing stages into future finite element studies.

Finally, the study emphasizes that screw thread geometry should not be considered a minor modelling detail. The demonstrated influence of thread morphology on cortical stress distribution indicates that high-resolution numerical models may improve the biomechanical evaluation of patient-specific implants manufactured through digital workflows. Nevertheless, because this investigation is entirely computational, experimental validation and clinical studies remain necessary before translating these observations into routine clinical decision-making.

7. Clinical Applications

The methodology presented in this study may be particularly relevant for engineers, implant designers, and clinicians involved in the digital development of patient-specific subperiosteal implants for severe maxillary atrophy. By improving the representation of screw–bone interaction, the proposed modelling strategy may enhance biomechanical analyses performed during computer-aided implant design and additive manufacturing workflows.

The approach could also support future research aimed at optimizing implant geometry, fixation strategies, and digital planning protocols. However, the available data do not demonstrate a direct clinical benefit or justify modifications to current surgical protocols. Further experimental investigations and prospective clinical studies are required before this modelling strategy can be considered for routine clinical application.

8. Level of Evidence, Limitations and Transparency

This publication is a preclinical computational study based on finite element analysis (FEA). Accordingly, its level of evidence should be interpreted within the context of biomechanical modelling rather than clinical research. While the study provides valuable information regarding the mechanical behaviour of patient-specific subperiosteal implant structures, it does not establish clinical effectiveness or long-term treatment outcomes.

A major strength of the investigation lies in the proposed submodelling methodology, which enables highly detailed evaluation of the screw–bone interface while maintaining reasonable computational efficiency. This modelling strategy represents an important methodological contribution to the biomechanical assessment of customized implant designs.

However, the information provided does not indicate whether the numerical model was experimentally validated or correlated with clinical observations. Likewise, details regarding sensitivity analyses or additional validation procedures are not reported in the available abstract. As with all finite element simulations, the results remain dependent on the assumptions and boundary conditions incorporated into the computational model. No information regarding funding sources or conflicts of interest is available in the provided material and therefore cannot be assessed.

9. Key Study Highlights

• Study type: Preclinical computational finite element analysis (FEA).
• Level of evidence: Preclinical numerical simulation.
• Study population: Not specified in the available information.
• Number of patients or implants: Not specified in the available information.
• Follow-up duration: Not applicable / not specified.
• Primary outcome: Evaluation of the biomechanical influence of screw-to-bone interaction in additively manufactured subperiosteal implant structures.
• Main finding: Detailed modelling of the screw–bone interface significantly influences implant displacement, reaction forces, and stress distribution, with cortical bone showing the highest mechanical loading.
• Scientific conclusion: The proposed submodelling technique provides a more realistic assessment of the implant–bone interface while substantially reducing computational cost.
• Limitations: Computational study only; no clinical validation reported; several methodological details are not available in the provided abstract.

10. Scientific Impact

This study expands current knowledge on the biomechanical evaluation of patient-specific subperiosteal implants by shifting attention from the implant framework alone to the mechanical behaviour of the fixation interface. In doing so, it addresses an aspect that has received comparatively limited attention despite its importance for implant stability and load transmission.

Beyond its specific findings, the principal contribution of this work is methodological. The proposed submodelling strategy demonstrates that detailed representation of screw thread geometry can be achieved without the computational burden associated with fully refined finite element models. This balance between numerical accuracy and computational efficiency may facilitate more sophisticated biomechanical analyses during the design of customized implants manufactured through digital workflows.

Although the investigation does not provide clinical evidence supporting the superiority of a specific implant design or surgical protocol, it establishes a robust computational framework that may serve future experimental and clinical research. By highlighting the influence of thread geometry and cortical bone mechanics, the study also identifies variables that deserve further investigation before definitive clinical recommendations can be formulated.

11. Editorial Conclusion

This preclinical investigation introduces an innovative computational methodology for analysing the biomechanical interaction between fixation screws and bone in additively manufactured subperiosteal implant structures. The findings demonstrate that accurate representation of the screw–bone interface can substantially influence finite element predictions, particularly within cortical bone. While these results enhance the biomechanical understanding of customized implant systems and may contribute to future digital implant design, their clinical significance should be interpreted cautiously until validated through experimental research and well-designed clinical studies.