Table 1.
Description of 3D soft tissue landmarks in the study.
Fig 1.
The diagrammatic representation of the proposed method.
Table 2.
Description of surface deviations areas for aesthetic units.
Fig 2.
Soft tissue boundaries were outlined on the reference mesh to assist in posterior measurements.
A, Total face surface. B, Frontal view of the areas from 1 to 5. C, Lateral view of the areas from 1 to 5.
Fig 3.
Representative color-coded deviation map showing the discrepancy between the reference mesh and a facial scan using the best-fit algorithm.
A, Overall surface area. B, From area -1. C, From area -2. D, From area -3. E, From area -4. F, From area -5.
Table 3.
The Intraclass Correlation Coefficient (ICC) index and F-test of averages were calculated for the five localized surface areas as marked.
Table 4.
Descriptive statistics of the overall scanning accuracy (trueness and precision) obtained using three different 3D scanners (Planmeca ProFace, EinScan H2, and EM3D Scanner application). Data provided in millimetres (mm).
Table 5.
Descriptive statistics of the scanning accuracy (trueness) values (RMS) obtained among the different areas. Data provided in millimeters (mm).
Table 6.
Descriptive statistics of the scanning accuracy (precision) values (RMS) obtained among the different areas. Data provided in millimeters (mm).
Fig 4.
Boxplot of the overall scanning accuracy (trueness) obtained using three different 3D scanners.
Fig 5.
Boxplot of the overall scanning accuracy (precision) obtained using three different 3D scanners.
Fig 6.
Mean of the scanning accuracy (trueness) values (RMS) obtained among the different areas.
Fig 7.
Mean of the scanning accuracy (precision) values (RMS) obtained among the different areas.
Table 7.
The average percentages and estimated confidence intervals of the tolerance measures for overlapping surfaces in five different areas across five tolerance bands, which were compared between each 3D facial scanner and CBCT soft tissue segmentation.