Automatic Reproduction of Natural Head Position in Orthognathic Surgery Using a Geometric Deep Learning Network
Ji-Yong Yoo,
Su Yang,
Sang-Heon Lim,
Ji Yong Han,
Jun-Min Kim,
Jo-Eun Kim,
Kyung-Hoe Huh,
Sam-Sun Lee,
Min-Suk Heo,
Hoon Joo Yang,
Won-Jin Yi
Affiliations
Ji-Yong Yoo
Department of Biomedical Radiation Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul 08826, Republic of Korea
Su Yang
Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul 08826, Republic of Korea
Sang-Heon Lim
Interdisciplinary Program in Bioengineering, Graduate School of Engineering, Seoul National University, Seoul 08826, Republic of Korea
Ji Yong Han
Interdisciplinary Program in Bioengineering, Graduate School of Engineering, Seoul National University, Seoul 08826, Republic of Korea
Jun-Min Kim
Department of Electronics and Information Engineering, Hansung University, Seoul 02876, Republic of Korea
Jo-Eun Kim
Department of Oral and Maxillofacial Radiology, Dental Research Institute, School of Dentistry, Seoul National University, Seoul 03080, Republic of Korea
Kyung-Hoe Huh
Department of Oral and Maxillofacial Radiology, Dental Research Institute, School of Dentistry, Seoul National University, Seoul 03080, Republic of Korea
Sam-Sun Lee
Department of Oral and Maxillofacial Radiology, Dental Research Institute, School of Dentistry, Seoul National University, Seoul 03080, Republic of Korea
Min-Suk Heo
Department of Oral and Maxillofacial Radiology, Dental Research Institute, School of Dentistry, Seoul National University, Seoul 03080, Republic of Korea
Hoon Joo Yang
Department of Oral and Maxillofacial Surgery, Dental Research Institute, School of Dentistry, Seoul National University, Seoul 03080, Republic of Korea
Won-Jin Yi
Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul 08826, Republic of Korea
Background: Accurate determination of the natural head position (NHP) is essential in orthognathic surgery for optimal surgical planning and improved patient outcomes. However, traditional methods encounter reproducibility issues and rely on external devices or patient cooperation, potentially leading to inaccuracies in the surgical plan. Methods: To address these limitations, we developed a geometric deep learning network (NHP-Net) to automatically reproduce NHP from CT scans. A dataset of 150 orthognathic surgery patients was utilized. Three-dimensional skull meshes were converted into point clouds and normalized to fit within a unit sphere. NHP-Net was trained to predict a 3 × 3 rotation matrix to align the CT-acquired posture with the NHP. Experiments were conducted to determine optimal point cloud sizes and loss functions. Performance was evaluated using mean absolute error (MAE) for roll, pitch, and yaw angles, as well as a rotation error (RE) metric. Results: NHP-Net achieved the lowest RE of 1.918° ± 1.099° and demonstrated significantly lower MAEs in roll and pitch angles compared to other deep learning models (p Conclusions: By effectively improving the accuracy and efficiency of NHP reproduction, NHP-Net reduces the workload of surgeons, supports more precise orthognathic surgical interventions, and ultimately contributes to better patient outcomes.
