Study aims to evaluate the quantitative facial soft tissue changes in relation to the underlying skeletal tissue following L-shaped RM using CT and 3dMD analysis and to investigate the patient’s pre- and post-operative satisfaction as well as surgical outcomes.
This study included a cohort of 47 patients who were admitted to the Peking University Hospital of Stomatology between 2013 and 2020 for maxillary resection procedures, necessitated by tumours.
The study included 120 individuals, 60 each in the nasal breathing (NB) and mouth breathing (MB) groups.
To establish and validate a novel method to orient a 3-dimensional (3D) facial model to natural head position (NHP) in a stereophotogrammetric system using a 2-dimensional frontal full-face photograph of NHP.
This study indicates that facial asymmetry significantly increases with age and is significantly larger in males than in females.
The outcome variables were postoperative pain and swelling over 7 days. Pain was measured using the visual analog scale (VAS) and swelling was measured using stereophotogrammetry.
The aim of this study was to clarify the hard-soft tissue relationships of the lower 1/3 of the face in skeletal Class II-hyperdivergent patients compared with those in Class I-normodivergent patients using network analysis.
A deep learning-based approach for automated landmark extraction from 3dMD facial photographs was developed and its precision was evaluated. The results showed high precision and consistency in landmark annotation, comparable to manual and semi-automatic annotation methods.
3dMD facial images and deep transfer learning have been firstly combined for evaluating the facial attractiveness in patients undergoing Orthognathic surgery.
The aim of this study was to describe a reproducible method to calculate facial volume change and to assess its reliability.