Three-dimensional virtual planning in mandibular advancement surgery: soft tissue prediction based on deep learning. R ter Horst, H van Weert, T Loonen, S Bergé, S Vinayahalingam, F Baan, T Maal, G de Jong, T Xi.

3dMD Customer Research, Computer Science | Computer Vision, Medical, 3dMDface, Oral and Maxillofacial, Orthognathic

This study focused on developing a novel deep-learning (DL)-based algorithm to predict the virtual soft tissue profile after mandibular advancement surgery and comparing its accuracy with the mass tensor model (MTM).

Evaluating effects of focal length and viewing angle in a comparison of recent face landmark and alignment methods. X Li, J Liu, J Baron et al.

3dMD Customer Research, Computer Science | Computer Vision

The workflow… we first performed facial geometry capture with a 3dMD system; imported into landmarker.io to annotate each face manually to achieve ground-truth; rasterized each face at 49 angles and 6 focal lengths; calculated the ground-truth 2D landmark locations; and analyzed performance of each method by calculating NRMSE error between a method’s predicted landmarks and the 2D ground-truth locations.

Automatic Assessment of 3-Dimensional Facial Soft Tissue Symmetry Before and After Orthognathic Surgery Using a Machine Learning Model. LJ Lo, CT Yang, CT Ho, CH Liao, HH Lin.

3dMD Customer Research, Computer Science | Computer Vision, Medical, 3dMDface, Craniofacial/Cleft, Plastics

This study applied the transfer learning model with a convolutional neural network based on 3-dimensional (3D) contour line features to evaluate the facial symmetry before and after OGS. A total of 158 patients were recruited in a retrospective cohort study for the assessment and comparison of facial symmetry before and after OGS from January 2018 to March 2020. Three-dimensional facial photographs were captured by the 3dMD face system in a natural head position, with eyes looking forward, relaxed facial muscles, and habitual dental occlusion before and at least 6 months after surgery.