Date: April 2021 (Online).
Source: Journal of Cranio-Maxillofacial Surgery, ISSN 1010-5182, https://doi.org/10.1016/j.jcms.2021.04.001.
Objective: To develop a novel deep-learning (DL)-based algorithm to predict the virtual soft tissue profile after mandibular advancement surgery, and to compare its accuracy with the mass tensor model (MTM).
Materials and Methods: Subjects who underwent mandibular advancement surgery were enrolled and divided into a training group and a test group. The DL model was trained using 3D photographs and CBCT data based on surgically achieved mandibular displacements (training group). Soft tissue simulations generated by DL and MTM based on the actual surgical jaw movements (test group) were compared with soft-tissue profiles on postoperative 3D photographs using distance mapping in terms of mean absolute error in the lower face, lower lip, and chin regions.
Results: 133 subjects were included — 119 in the training group and 14 in the test group. The mean absolute error for DL-based simulations of the lower face region was 1.0 ± 0.6 mm and was significantly lower (p = 0.02) compared with MTM-based simulations (1.5 ± 0.5 mm).
Conclusions: The DL-based algorithm can accurately simulate 3D soft tissue profiles following mandibular advancement surgery, with a clinically acceptable mean absolute error and a higher precision compared with the MTM algorithm.
Article: Three-dimensional virtual planning in mandibular advancement surgery: soft tissue prediction based on deep learning.
Authors: Rutger ter Horst, Hanneke van Weert, Tom Loonen, Stefaan Bergé, Shank Vinayahalingam, Frank Baan, Thomas Maal, Guido de Jong, Tong Xi. Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands.