ARTIFICIAL INTELLIGENCE IN ORTHOGNATHIC SURGERY: A REVIEW OF DIAGNOSTIC SUPPORT, VIRTUAL PLANNING, AND SOFT TISSUE OUTCOME PREDICTION
DOI:
https://doi.org/10.31435/ijitss.3(51).2026.6581Keywords:
Artificial Intelligence, Deep Learning, Orthognathic Surgery, Cephalometric Analysis, Soft Tissue Prediction, Virtual Surgical PlanningAbstract
Objectives. Orthognathic treatment of dentofacial deformities is highly experience-dependent, and the prediction of post-operative facial soft tissue change has historically been the least reliable step of surgical planning. This review synthesises the current applications, reported performance, and limitations of artificial intelligence (AI) across the orthognathic care pathway, with particular emphasis on diagnostic support, virtual surgical planning, and soft tissue outcome prediction.
Methods. A narrative review of peer-reviewed studies applying machine learning (ML), deep learning (DL), and related methods to diagnosis, patient qualification, cephalometric analysis, treatment planning, soft tissue prediction, and outcome evaluation in orthognathic surgery was conducted. Findings were organised thematically and appraised against commonly reported metrics.
Results. AI is most mature for automated cephalometric landmarking and for screening the need for surgery, where convolutional neural networks and ensemble classifiers approach expert-level agreement. For soft tissue prediction, DL methods that learn nonlinear bone-to-soft-tissue mappings achieve clinically relevant accuracy that exceeds traditional proportional and software-based simulations, with region-specific surface errors typically in the sub-millimetre to low-millimetre range. Applications in planning, outcome verification, and patient communication are emerging but less validated.
Conclusions. AI provides meaningful decision support across the orthognathic pathway and improves soft tissue simulation, yet evidence remains limited by small single-centre datasets, weak external validation, and restricted interpretability. Multicentre data, prospective validation, and explainable, clinician-supervised models are required before routine clinical adoption.
References
Dot, G., Schouman, T., Chang, S., Rafflenbeul, F., Kerbrat, A., Rouch, P., et al. (2022). Automatic 3-dimensional cephalometric landmarking via deep learning. Journal of Dental Research, 101(11), 1380–1387.
Elshebiny, T., Morcos, S., Mohammad, A., Quereshy, F., & Valiathan, M. (2019). Accuracy of three-dimensional soft tissue prediction in orthognathic cases using Dolphin three-dimensional software. Journal of Craniofacial Surgery, 30(2), 525–528.
Hwang, H.-W., Moon, J.-H., Kim, M.-G., Donatelli, R. E., & Lee, S.-J. (2021). Evaluation of automated cephalometric analysis based on the latest deep learning method. The Angle Orthodontist, 91(3), 329–335.
Kim, H., Shim, E., Park, J., Kim, Y.-J., Lee, U., & Kim, Y. (2020). Web-based fully automated cephalometric analysis by deep learning. Computer Methods and Programs in Biomedicine, 194, 105513.
Kim, I. H., Jeong, J., Kim, J. S., Lim, J., Cho, J. H., Hong, M., et al. (2025). Predicting orthognathic surgery results as postoperative lateral cephalograms using graph neural networks and diffusion models. Nature Communications, 16(1), 2586.
Knoops, P. G. M., Papaioannou, A., Borghi, A., Breakey, R. W. F., Wilson, A. T., Jeelani, O., et al. (2019). A machine learning framework for automated diagnosis and computer-assisted planning in plastic and reconstructive surgery. Scientific Reports, 9, 13597. https://doi.org/10.1038/s41598-019-49506-1
Lampen, N., Kim, D., Fang, X., Xu, X., Kuang, T., Deng, H. H., et al. (2022). Deep learning for biomechanical modeling of facial tissue deformation in orthognathic surgical planning. International Journal of Computer Assisted Radiology and Surgery, 17(5), 945–952.
Lee, H., Ahmad, S., Frazier, M., Dundar, M. M., & Turkkahraman, H. (2022). A novel machine learning model for class III surgery decision. Journal of Orofacial Orthopedics / Fortschritte der Kieferorthopädie. Advance online publication. https://doi.org/10.1007/s00056-022-00421-7
Lin, H.-H., Chiang, W.-C., Yang, C.-T., Cheng, C.-T., Zhang, T., & Lo, L.-J. (2021). On construction of transfer learning for facial symmetry assessment before and after orthognathic surgery. Computer Methods and Programs in Biomedicine, 200, 105928. https://doi.org/10.1016/j.cmpb.2021.105928
Ma, L., Lian, C., Kim, D., Xiao, D., Wei, D., Liu, Q., et al. (2023). Bidirectional prediction of facial and bony shapes for orthognathic surgical planning. Medical Image Analysis, 83, 102644.
Ma, L., Xiao, D., Kim, D., Lian, C., Kuang, T., & Liu, Q. (2023). Simulation of postoperative facial appearances via geometric deep learning for efficient orthognathic surgical planning. IEEE Transactions on Medical Imaging, 42(2), 336–345. https://doi.org/10.1109/TMI.2022.3180078
Motamedian, S. R., Mohaghegh, S., Niazmand, M., Mohammad-Rahimi, H., Ahmadi, N., Yaseri, M., & Haeri Boroojeni, H. S. (2025). Application of artificial intelligence in orthognathic surgery: A scoping review. BioMed Research International, 2025, 8284581. https://doi.org/10.1155/bmri/8284581
Resnick, C. M., Dang, R. R., Glick, S. J., & Padwa, B. L. (2017). Accuracy of three-dimensional soft tissue prediction for Le Fort I osteotomy using Dolphin 3D software: A pilot study. International Journal of Oral and Maxillofacial Surgery, 46(3), 289–295. https://doi.org/10.1016/j.ijom.2016.10.016
Schwendicke, F., Chaurasia, A., Arsiwala, L., Lee, J.-H., Elhennawy, K., Jost-Brinkmann, P.-G., Demarco, F., & Krois, J. (2021). Deep learning for cephalometric landmark detection: Systematic review and meta-analysis. Clinical Oral Investigations, 25(7), 4299–4309. https://doi.org/10.1007/s00784-021-03990-w
Tanikawa, C., & Yamashiro, T. (2021). Development of novel artificial intelligence systems to predict facial morphology after orthognathic surgery and orthodontic treatment in Japanese patients. Scientific Reports, 11(1), 15853. https://doi.org/10.1038/s41598-021-95002-w
ter Horst, R., van Weert, H., Loonen, T., Bergé, S., Vinayahalingam, S., & Baan, F. (2021). Three-dimensional virtual planning in mandibular advancement surgery: Soft tissue prediction based on deep learning. Journal of Cranio-Maxillofacial Surgery, 49(9), 775–782. https://doi.org/10.1016/j.jcms.2021.04.001
Zhang, J., Liu, M., Wang, L., Chen, S., Yuan, P., Li, J., et al. (2020). Context-guided fully convolutional networks for joint craniomaxillofacial bone segmentation and landmark digitization. Medical Image Analysis, 60, 101621.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Kinga Bukała, Szymon Klimaszewski, Natalia Dejewska, Urszula Gadomska, Jowita Wiktoria Maksymiuk, Mateusz Onopiuk, Zuzanna Wiktoria Szumska, Mikołaj Daniluk, Jonasz Żuk
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles are published in open-access and licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Hence, authors retain copyright to the content of the articles.
CC BY 4.0 License allows content to be copied, adapted, displayed, distributed, re-published or otherwise re-used for any purpose including for adaptation and commercial use provided the content is attributed.
