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.
Keywords: 3D cephalometry; automatic landmarking; machine learning; orthognathic surgery; surgical planning.
© 2022 John Wiley & Sons Ltd.