We believe 3D printed templates usage increased time efficiency, improved the match of skin flaps in donor and recipient arms, allowed us to control the amount of skin surplus without skin flap tip necrosis. In these procedures where time is of the essence, we believe pre-operative planning is imperative for its success.
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).
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.
With more than 20 years of acknowledgment in peer-reviewed investigative research publications, 3dMD has become the near-ground-truth 3D dense-surface shape information benchmark for human subject input applications demanding image precision and workflow efficiency. Here are a few of these Applications…
