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Prediction of Three-Dimensional Soft Tissue Changes Following Mandibular PEEK Implantation (Proof of Concept)
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DOI：10.3390/jpm16080427.png)
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Abstract
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Background/Objectives: Polyetheretherketone (PEEK) implants are increasingly being used for mandibular reconstruction and augmentation, yet predicting the resulting facial soft tissue changes remains challenging. Accurate pre-operative prediction of soft tissue deformation is essential for surgical planning and patient counselling. To the best of our knowledge, this study presents the first application of a computational model for predicting three-dimensional facial soft tissue deformation induced by PEEK implants. Methods: Four patient cases with pre-operative and post-operative cone-beam computed tomography scans were analysed. A mass tensor model was employed to predict soft tissue changes based on the pre-operative soft tissue shape, mandibular anatomy, and the designed PEEK implant. Model accuracy was quantified by comparing predicted outcomes to post-operative scans using landmark-based analysis of chin and jaw positions, as well as surface distance mapping, with a mean error below 2 mm adopted as the criterion for clinically acceptable accuracy. Results: Landmark-based analysis revealed a mean absolute error of 0.9 ± 0.8 mm between the predicted and actual positions. The mean surface distances were 1.3 mm for the jaw–chin region and 1.0 mm for the entire facial region. Conclusions: These results demonstrate the potential of a computational approach for real-time prediction of facial soft tissue changes following PEEK implant placement, achieving clinically relevant accuracy at the cohort level, with a tendency towards under-prediction and one case exceeding the threshold.
Keywords:
PEEK implants
soft tissue prediction
mass tensor model
craniofacial surgery
mandibular reconstruction
computational modelling
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