Machine‐learning‐based approach for predicting postoperative skeletal changes for orthognathic surgical planning
Qingchuan Ma, Etsuko Kobayashi, Bowen Fan, Kazuaki Hara, Keiichi Nakagawa, Ken Masamune, Ichiro Sakuma, Hideyuki Suenaga
Abstract
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.