How to predict the future face? A 3D methodology to forecast the aspect of patients after orthognathic surgeries

Abstract

Background and objective

Despite the availability of several commercial solutions for predicting the soft tissue outcomes of maxillofacial surgeries, none have proven sufficiently reliable for routine clinical use. This study proposes a 3D methodology for predicting soft tissue displacement following maxillofacial surgery without relying on mechanical modeling, unlike most existing approaches.

Methods

Pre- and post-operative Cone Beam Computed Tomography scans of patients with class III malocclusion were collected. Tailored image processing and volume reconstruction techniques were applied to semi-automatically generate 3D soft tissue models. Cephalometric landmarks were identified to perform a geometrical similarity analysis among patients with the same malocclusion class undergoing the same surgical procedure. Vectorial displacement maps were generated to capture the soft tissue changes from pre- to post-operative and were then applied to the pre-operative of test patients to predict soft tissue outcomes. Euclidean distances were calculated between predicted and real post-operative positions, and the Wilcoxon signed-rank test was conducted to assess statistical differences between predicted and real landmark coordinates.

Results

Error maps indicated that approximately 70 % of predicted facial points had errors below 2.5 mm, while around 10 % ranged between 2.5 mm and 3 mm. Statistically significant differences (p < 0.05) were observed only for the gonion and cheilion.

Conclusion

. The findings support the validity of the geometrical similarity analysis and the vectorial displacement map approach. The simplicity and promising accuracy of the proposed method encourage further investigations across different surgical procedures. Additionally, integrating this methodology into surgical planning could offer a viable alternative to commercial solutions. This low-cost, computationally efficient prediction method is designed to improve as more patient data become available. The proposed method is patent pending.