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Accuracy of intraoral scanning methods for maxillary Kennedy class I arch

Authors :
I-Ching Chang
Chun-Cheng Hung
Je-Kang Du
Chih-Te Liu
Pei-Ling Lai
Ting-Hsun Lan
Source :
Journal of Dental Sciences, Vol 18, Iss 2, Pp 747-753 (2023)
Publication Year :
2023
Publisher :
Elsevier, 2023.

Abstract

Background/purpose: The optimal strategy for scanning removable partial dentures remains unknown. This study investigated scanning strategies for patients with a maxillary Kennedy Class I arch as well as the measurement deviations of three scanning strategies. Materials and methods: A standard maxilla model was positioned with a holder in a dental chair to simulate a natural patient position and posture. Standard Tessellation Language files for reference models were formatted with a desktop scanner, and model operation files were obtained with a TRIOS 3 Pod intraoral scanner and superimposed using Exocad computer-aided design software. The three scanning strategies evaluated in this study (Strategy M, T-R, and R-T) were used for nine scans each, and the resulting data were recorded. The deviation of the three strategies was statistically analyzed through one-way ANOVA and Tukey post hoc testing. Results: The trueness of Strategy M, T-R, and R-T was 52.6 ± 31.0, 54.9 ± 27.6, and 50.1 ± 22.3 μm, respectively. No statistically significant differences in trueness were detected among the three groups (P > 0.05). However, Strategy T-R had the evenest distribution of all measuring points. The deviations of the measurements obtained by three scanning strategies were mostly between 30 and 70 μm. The precision of the three strategies was similar as well. Conclusion: Trueness did not differ significantly among the three strategies. However, Strategy T-R is recommended for use with a TRIOS 3 Pod scanner because of its reduction of the seesaw effect and high stabilization of the RPD framework.

Details

Language :
English
ISSN :
19917902
Volume :
18
Issue :
2
Database :
Directory of Open Access Journals
Journal :
Journal of Dental Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.67459405a34247dda2fc12335f11217f
Document Type :
article
Full Text :
https://doi.org/10.1016/j.jds.2022.12.006