Accuracy of 3-dimensional soft tissue prediction for orthognathic surgery in a Chinese population. KJ Lee, SL Tan, HY Low, LJ Chen, CW Yong, MT Chew.
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Training AI, Wearing Tech,
and Imaging Health.
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This excerpt will show up on the home page Blog/News feed
This excerpt will show up on the home page Blog/News feed
This is a retrospective serial longitudinal study of consecutively enrolled infants from September 2012 to July 2016 with BCLP who underwent NAM before primary lip and nose reconstructive surgery.
This study evaluates the effects of rapid maxillary expansion (RME) and mandibular midline distraction osteogenesis (MMDO) on facial soft tissues using three-dimensional (3D) images.
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).
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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.
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Dentoskeletal and soft-tissue treatment changes were examined objectively by cephalometric analysis and stereophotogrammetry, respectively. Pre- and posttreatment profile views were evaluated subjectively by orthodontists and laypeople using the 7-point Likert scale. Intra- and intergroup comparisons for the repeated measurements were performed with 2-way variance analysis. Bonferroni test was used for multiple comparisons (P ≤0.05).