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Accuracy of Soft Tissue Prediction in Skeletal Type III Relationship Using a Computer-Aided Three-Dimensional Surgical Simulation Planning Program

Authors :
Jiratha Chantaraaumporn
Pongstorn Putongkam
Nathaphon Tangjit
Syrina Tantidhnazet
Somchart Raocharernporn
Source :
Applied Sciences, Vol 13, Iss 9, p 5623 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

Using a computer-aided, three-dimensional surgical simulation planning program, soft-tissue planning can help achieve adequate facial esthetics and patient satisfaction after orthognathic surgery. This study aimed to assess the Simplant O&O software’s soft tissue prediction accuracy. Fourteen skeletal type III patients who underwent orthognathic surgery by the same surgeons were included in this prospective study, and they were separated into two groups: the one-jaw (n = 5) and two-jaw (n = 9) groups. The software was used to analyze the preoperative (T0) and 4-month postoperative computed tomography data (T1), as well as intraoral scans. Data from cone-beam computed tomography and stereolithography from a scanned dental cast were used to reconstruct a composite skull model. Based on the presurgical CT data, the program generated a predicted soft tissue image (TP), which was then superimposed on the T1. The distances between seven T1 and TP landmarks were measured and evaluated using a one-sample t-test. In the one-jaw group, the mean error for all linear measurements was 1.73 ± 1.14 mm, whereas the mean error of the two-jaw group was 1.03 ± 0.83 mm, and both measurements were within clinically acceptable limits. Pronasele had the best correlation (mean error of 0.63 ± 0.45 mm) while soft tissue pogonion and soft tissue point B had the worst correlations (mean error of 2.87 ± 2.22 mm and 1.31 ± 0.98 mm, respectively). Even though there were some limitations, it was possible to conclude that the ability to accurately predict soft tissue changes using Simplant O&O for skeletal type III patients makes it adequate for use in clinical practice.

Details

Language :
English
ISSN :
20763417
Volume :
13
Issue :
9
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.f54d049072d246cab5d2571d5398272f
Document Type :
article
Full Text :
https://doi.org/10.3390/app13095623