## Materials & Methods
### Data Processing  
The NA, PSM, SA, and POST datasets were obtained from 15 participants (\(n=15\)). All datasets in the STL files were loaded into surface analyzing software (Geomagic Control X; 3D Systems, Rock hill, SC, USA). The STL files of the three experimental groups (NA, PSM, and SA) were superimposed onto the reference data (POST) using the \"align between measured data\" function. The best-fit alignment was used in the arrangement. The best-fit alignment is performed using the iterative closest point (ICP) algorithm for the arrangement of mesh data [34]. The alignment of the scan data was processed via the minimization of the mesh distance error between each corresponding point. To avoid using the occlusal surface of the treated tooth for the arrangement, the field of interest was limited to the occlusal, buccal, and lingual surfaces of two premolars and one molar (Figure 4A). The tolerance level was set to 100 \(\upmu\)m [35; 36; 37; 38; 39]. In the case of a valid alignment of the experimental model with the reference model, all areas of the teeth, except the treated tooth, were coded green. After the valid arrangement was confirmed, the field of interest for the 3D comparison was limited to the occlusal surface by trimming off the area not belonging to the occlusal surface (Figure 4B). A 3D comparison was performed to obtain the root mean square (RMS) value, the +average (AVG) deviation value, the \(-\)AVG deviation value, and the proportion inside the tolerance (TOL) value of each experimental group compared to POST. These values were used in the quantitative analysis (Figure 4C). The RMS was calculated by applying the following formula [38; 40; 41]:  
\[\text{RMS}=\sqrt{\frac{\sum_{i=1}^{n}(X_{1,i}-X_{2,i})^{2}}{n}},\]  
where \(n\) is the total number of measuring points, \(X_{1,i}\) is measuring point \(i\) in the reference data, and \(X_{2,i}\) is measuring point \(i\) in the comparison data of the experimental group.  
The RMS can serve as a measure of how far deviations between the two different datasets vary from zero. In this study, assuming all systematic errors were removed, the only possible deviation between the reference data and the comparison data should be the sum of points in which occlusal adjustment was required. Based on this, the RMS value represents the amount of the occlusal error. To analyze the amount of occlusal error according to the functional properties of the occlusal surface, the trimmed occlusal surface was split into three subdivided areas: the functional cusp area, the non-functional cusp area, and the central groove area (Figure 4D). The reference line was determined by dividing the buccolingual dimension into three. The RMS, +AVG, -AVG, and in Tol values were calculated via a 3D comparison for each subdivided area. For the assessment of the area outside the tolerance, a dataset that included all points of deviation was exported to comma-separated values (CSV) files. After filtering out points inside the tolerance level from the dataset using spreadsheet software (Excel 2016; Microsoft Co., Redmond, WA, USA), the RMS value of the valid points were calculated.  
A color-coded map of the occlusal surface was generated for the qualitative analysis (Figure 4C). The color-coded map represents the 3D deviation between the two datasets. The tolerance level in the color-coded map was 100 \(\upmu\)m. An area coded with green represents an area inside the tolerance level, whereas an area with red or blue represents an area with a positive deviation out of the tolerance level or a negative deviation out of the tolerance level, respectively.