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Updated: Oct 4, 2025
A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible
Published on: January 28, 2020
Machine-learning-based approach for predicting postoperative skeletal changes for orthognathic surgical planning
Qingchuan Ma1,2, Etsuko Kobayashi3, Bowen Fan3
1Department of Oral-Maxillofacial Surgery and Orthodontics, The University of Tokyo Hospital, Tokyo, Japan.
Background:
Manually surgical planning becomes an increasing workload of surgeons because of the fast-growing patient population. This study introduced a machine-learning-based approach to assist surgical planning in orthognathic surgery.
Methods:
Both preoperative and one-year-later postoperative computerised tomography images of 56 patients were collected. A 12-layers cascaded deep neural network structure with two successive models was proposed to yield an end-to-end solution, where the first model extracts landmarks from 2D patches of 3D volume and the second model predicts postoperative skeletal changes.
Results:
The experimental results showed that the model obtained a prediction accuracy of 5.4 mm at the landmark level in 42.9 s. It also represented 74.4% of 3D regions at volume level when compared with the ground truth of human surgeons.
Conclusions:
This study demonstrated the feasibility of predicting postoperative skeletal changes for orthognathic surgical planning by using machine learning, showing great potential for reducing the workload of surgeons.
