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A Novel Quality Assessment Method for the Clinical Reproduction of Orthodontic Attachments Based on Differential Entropy †.
- Source :
- Engineering Proceedings; 2023, Vol. 56, p15, 6p
- Publication Year :
- 2023
-
Abstract
- In this study, the effectiveness of an experimental clinical technique for the reproduction of attachments during an orthodontic treatment with clear aligners was evaluated using a new index (CorAl) for quality assessment that exploits the differential entropy of point clouds. The procedure involves the use of a pre-drilled template and a second pre-loaded template with a high-viscosity composite and is compared with the standard technique. Attachment planning was conducted on four prototypes of dental arches with extracted teeth which were divided into two groups according to the proposed operating procedures. Digital scans were utilized to capture dental impressions for both the purposes of virtual planning and to reproduce the clinical outcomes post-procedure. The point clouds obtained after the reproduction of the attachments were aligned with those from the virtual planning, and the deviation analysis was conducted using the quality index of the CorAl method. Though no significant discrepancies were found among the groups regarding morphological flaws, detachments, or maximum defect values, the differential entropy analysis revealed that the experimental technique offers good alignment in attachment placement. The outcome supports that the innovative procedure of the clinical reproduction of attachments proved to be reliable and operationally simple, with additional benefits derived from using the CorAl index. The advantages of CorAl include the use of a single comparison index, no problem of comparison commutativity, noise immunity, low influence from the presence of holes, and point cloud densities. This allows for the drawing of quality maps that show areas with the highest deviation. [ABSTRACT FROM AUTHOR]
- Subjects :
- ORTHODONTICS
ORTHODONTIC appliances
DIFFERENTIAL entropy
POINT cloud
DATA modeling
Subjects
Details
- Language :
- English
- ISSN :
- 26734591
- Volume :
- 56
- Database :
- Complementary Index
- Journal :
- Engineering Proceedings
- Publication Type :
- Academic Journal
- Accession number :
- 178214045
- Full Text :
- https://doi.org/10.3390/ASEC2023-15245