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Developing Advanced Patient-Specific In Silico Models: A New Era in Biomechanical Analysis of Tooth
Pierre Lahoud1, Reinhilde Jacobs2, Seyed Ali Elahi3
1Department of Oral and Maxillofacial Surgery & Imaging and Pathology, OMFS-IMPATH Research Group, University Hospitals Leuven, KU Leuven, Belgium; Division of Periodontology & Oral Microbiology, Department of Oral Health Sciences-University Hospitals Leuven, KU Leuven, Belgium.
Introduction:
As personalized medicine advances, there is an escalating need for sophisticated tools to understand complex biomechanical phenomena in clinical research. Recognizing a significant gap, this study pioneers the development of patient-specific in silico models for tooth autotransplantation (TAT), setting a new standard for predictive accuracy and reliability in evaluating TAT outcomes.
Methods:
Development of the models relied on 6 consecutive cases of young patients (mean age 11.66 years ± 0.79), all undergoing TAT procedures. The development process involved creating detailed in silico replicas of patient oral structures, focusing on transplanting upper premolars to central incisors. These models underpinned finite element analysis simulations, testing various masticatory and traumatic scenarios.
Results:
The models highlighted critical biomechanical insights. The finite element models indicated homogeneous stress distribution in control teeth, contrasted by shape-dependent stress patterns in transplanted teeth. The surface deviation in the postoperative year for the transplanted elements showed a mean deviation of 0.33 mm (±0.28), significantly higher than their contralateral counterparts at 0.05 mm (±0.04).
Conclusions:
By developing advanced patient-specific in silico models, we are ushering in a transformative era in TAT research and practice. These models are not just analytical tools; they are predictive instruments capturing patient uniqueness, including anatomical, masticatory, and tissue variables, essential for understanding biomechanical responses in TAT. This foundational work paves the way for future studies, where applying these models to larger cohorts will further validate their predictive capabilities and influence on TAT success parameters.
