Accuracy of the Intraoral Scanner for Detection of Tooth Wear
The aim of this work was to study the accuracy of the intraoral scanner for detection of tooth wear in natural teeth by using micro-computed tomography (micro-CT) as a gold standard. Twenty premolars were prepared, fixed in acrylic blocks, and scanned with an intraoral scanner (iTero Element® 2) and...
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Published in: | International dental journal Vol. 73; no. 1; pp. 56 - 62 |
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Main Authors: | , , , , , , , |
Format: | Journal Article |
Language: | English |
Published: |
England
Elsevier Inc
01-02-2023
Elsevier |
Subjects: | |
Online Access: | Get full text |
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Summary: | The aim of this work was to study the accuracy of the intraoral scanner for detection of tooth wear in natural teeth by using micro-computed tomography (micro-CT) as a gold standard.
Twenty premolars were prepared, fixed in acrylic blocks, and scanned with an intraoral scanner (iTero Element® 2) and micro-CT for baseline reference images before artificial tooth wear induction. The samples were then scrubbed with abrasive sandpaper 20 times and scanned with the intraoral scanner. They were then superimposed with the reference images utilising the “TimeLapse” feature of the scanner until the abraded area appeared yellow, indicating tooth surface loss in the 50-200 μm range. The same samples were then rescanned by micro-CT to measure the actual tooth surface loss. This procedure was repeated for the subsequent experimental tooth surface loss of 200-400 μm range (orange areas) and 400-750 μm range (red areas). The collected data were analysed for sensitivity, positive predictive value (PPV), and accuracy. Level of statistical significance was set at .05.
In the detection of experimental tooth surface loss, the specificity, PPV, and accuracy of the intraoral scanner were 98%, 98%, and 97%, respectively.
The iTero® intraoral scanner can be recommended to be a suitable screening tool for tooth wear in routine dental practice. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0020-6539 1875-595X |
DOI: | 10.1016/j.identj.2022.06.004 |