Literature DB >> 25220944

Data-based prediction of soft tissue changes after orthognathic surgery: clinical assessment of new simulation software.

N Abe1, S Kuroda2, M Furutani3, E Tanaka4.   

Abstract

The aim of the present study was to evaluate the accuracy of a novel simulation software package (OrthoForecast) for predicting the soft tissue profile after orthognathic surgery. The study included 15 patients with facial asymmetry (asymmetry group), 15 with a skeletal class II jaw relationship (class II group), and 15 with a skeletal class III jaw relationship (class III group). Twenty-four feature points were digitized, and the distances between points on the predicted and actual postoperative images were compared. Thirty-seven calibrated evaluators also graded the similarity of the predicted images compared to the actual postoperative photographs. Comparisons between the predicted and actual postoperative images revealed that the mean difference between feature points was 3.1 ± 1.4 mm for the frontal images and 2.9 ± 0.8 mm for the lateral images in the asymmetry group; 2.7 ± 0.9 and 2.1 ± 1.6 mm, respectively, in the class II group; and 1.8 ± 1.2 and 1.7 ± 1.0 mm, respectively, in the class III group. More than half of the evaluators assessed the predicted images as similar to the actual postoperative images in all groups. In conclusion, OrthoForecast can be regarded as useful, accurate, and reliable software to predict soft tissue changes after orthognathic surgery.
Copyright © 2014 International Association of Oral and Maxillofacial Surgeons. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  feature point; orthognathic surgery; prediction; simulation system

Mesh:

Year:  2014        PMID: 25220944     DOI: 10.1016/j.ijom.2014.08.006

Source DB:  PubMed          Journal:  Int J Oral Maxillofac Surg        ISSN: 0901-5027            Impact factor:   2.789


  3 in total

1.  Accuracy of three-dimensional virtual simulation of the soft tissues of the face in OrtogOnBlender for correction of class II dentofacial deformities: an uncontrolled experimental case-series study.

Authors:  Hugo Santos Cunha; Cícero André da Costa Moraes; Rodrigo de Faria Valle Dornelles; Everton Luis Santos da Rosa
Journal:  Oral Maxillofac Surg       Date:  2020-11-08

2.  A Fully Automatic Postoperative Appearance Prediction System for Blepharoptosis Surgery with Image-based Deep Learning.

Authors:  Yiming Sun; Xingru Huang; Qianni Zhang; Sang Yeul Lee; Yaqi Wang; Kai Jin; Lixia Lou; Juan Ye
Journal:  Ophthalmol Sci       Date:  2022-05-18

3.  Personalized Computational Models of Tissue-Rearrangement in the Scalp Predict the Mechanical Stress Signature of Rotation Flaps.

Authors:  Taeksang Lee; Sergey Y Turin; Casey Stowers; Arun K Gosain; Adrian Buganza Tepole
Journal:  Cleft Palate Craniofac J       Date:  2020-09-11
  3 in total

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