Toward Digital Twin Development for Implant Placement Planning Using a Parametric Reduced-Order Model.

Area of Science:

• Biomaterials Engineering
• Computational Mechanics
• Dental Implantology

Background:

• Accurate dental implant placement is crucial for success.
• Real-time stress analysis can optimize implant positioning and outcomes.
• Current methods may lack objectivity and real-time feedback.

Purpose of the Study:

• To develop a parametric reduced-order model (ROM) for real-time stress distribution analysis in dental implants and bone.
• To enhance the objectivity and success rate of implant placement plans.
• To create a digital twin for real-time evaluation of implant placement adequacy.

Main Methods:

• Utilized finite element analysis (FEA) to obtain stress distribution data.
• Employed design of experiments and sensitivity analysis to determine key design variables.
• Developed a parametric reduced-order model (ROM) for a 1-D computational-aided engineering (CAE) solver.
• Integrated the 1-D CAE solver into the Ondemand3D program to create a digital twin.

Main Results:

• Identified the order of influence for six design variables on stress distribution: Young's modulus of cancellous bone > implant thickness > front-rear angle > left-right angle > implant length.
• Achieved high coefficient of determination and prognosis accuracy with the developed ROM.
• Demonstrated the capability of the digital twin for real-time evaluation and visualization of implant placement plans.

Conclusions:

• The parametric ROM method provides an objective approach to analyzing stress distribution for dental implants.
• The developed digital twin aids dentists in decision-making by providing real-time feedback on implant placement.
• This approach has the potential to improve post-placement success rates and reduce reliance on subjective judgment.