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Accuracy of 3 Soft Tissue Prediction Methods After Double-Jaw Orthognathic Surgery in Class III Patients. | LitMetric

AI Article Synopsis

Article Abstract

Purpose: We aimed to evaluate the accuracy and validity of conventional manual prediction method (CM) and 2-dimensional and 3-dimensional (3D) soft tissue prediction methods for Class III bimaxillary orthognathic surgery patients.

Methods: Twenty skeletal Class III patients were included in this study. Soft tissue prediction was achieved with a traditional manual technique, 2-dimensional software (Dolphin Imaging, version 11.5), and 3-dimensional software (SimPlant Master, version 16.0) on preoperative lateral cephalometric radiographs and cone beam computurized tomography (CBCT) images and then compared with postoperative lateral cephalometric radiographs obtained at least 6 months after surgery (mean, 11.5 ± 6.77 months). Forty-eight measurements were done to determine the reliability of the methods.

Results: All prediction methods have limited postsurgery prediction accuracy for the subnasal upper lip area. Most of the differences were not more than 3 mm for all study groups, except the 3D software, which predicted the upper lip area to be, on average, 3.08 ± 1.38 mm further back (P < 0.001). Although the conventional method predicted 20 of 48 measurements similar to the final results, the poorest predictions were found at the nasal and labiomental areas (P < 0.001). Two-dimensional software predicted 23 of 48 measurements very close to the final results but made significantly different predictions for the nasolabial and labiomental angles, labrale superius point, and the chin area.

Conclusions: The 3D method predicted, especially the upper lip area, to be significantly and clinically incorrect, and its soft tissue prediction was insufficient compared with the other 2 methods. The 3D software needs to be updated, especially in evaluating soft tissue A point and upper lip changes after surgery.

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http://dx.doi.org/10.1097/SAP.0000000000002988DOI Listing

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