Virtual surgical planning and patient specific osteosynthesis in Orthognathic Surgery

This thesis presents new research aimed at improving Virtual Surgical Planning (VSP) for orthognathic surgery, with a focus on enhancing accuracy and streamlining the virtual workflow. Orthognathic surgery corrects abnormalities in the jaw and facial bones for both functional and aesthetic purposes.

The research focuses on several key aspects:

AI-driven segmentation: A new AI algorithm, called SASeg, was developed to improve the segmentation of the mandible in CBCT scans. This addresses challenges like metal artifacts from braces and reduces the need for manual intervention, making VSP more accessible.

Patient-specific statistical shape models (SSM): These models predict anatomical variations, improving the accuracy and personalization of surgical plans and implants, as demonstrated in a case study on facial masculinization surgery.

Comparison of osteosynthesis techniques: A randomized study examined the accuracy of jaw surgery using patient-specific osteosynthesis (PSO). The results showed that the "maxilla-first" technique with PSO was more accurate than the traditional method.

Long-term evaluation of PSO plates: The study evaluated the stability of the maxilla after Le Fort I surgery, showing that PSO plates provided reliable long-term results.

This research contributes to the improvement of orthognathic surgery by integrating AI techniques and patient-specific models, and by investigating innovative surgical methods.