## 2 Materials and Methods
Each labelled area was then annotated with the specific implant size and technique used. The criteria for annotation were described as follows:  
_Criteria for implant diameter selection in the mesiodistal dimension:_  
* In case of one implant placement: root space = 2x (T-I dist.) + Implant diameter. * In case of two or more implant placements: root space = T-I dist. + implant diameter + 3 + implant diameter + 3 +... + implant diameter + T-I dist. Note: T-I dist. refers to the distance between implant and tooth or the border of labelled space at marginal bone, which was 1.5 mm. _Criteria for implant diameter selection in the buccolingual dimension:_  
* Bone width = Buccal thickness + Implant diameter + Lingual thickness. Note: The minimal buccal and lingual thicknesses needed are 1 mm. _Criteria for implant length selection in the coronoapical dimension:_  
* Bone height \(\geq\) Implant length. Note: If there is any exposure of the available shortest length implant out of the alveolar ridge, this condition is indicated as \"Int SFE (internal sinus floor elevation)\" or \"Lat SFE (lateral sinus floor elevation)\" depending on the residual bone height. If the residual bone height was \(\geq\)5 mm, the Int SFE is indicated, but if the residual bone height was \(<\)5 mm, the Lat SFE is indicated. When Int SFE or Lat SFE were indicated, it was unnecessary to indicate the implant diameter. Subsequently, each labelled area was then annotated with eight different sets, including 4.2 \(\times\) 8, 4.2 \(\times\) 10, 4.2 \(\times\) 12, 5.0 \(\times\) 8, 5.0 \(\times\) 10, 5.0 \(\times\) 12, Int SFE, and Lat SFE for each panoramic and cross-sectional image. The annotations for both panoramic and cross-sectional images were independent because of their own characteristics, and they were indicated with the corresponding labelled box area (as displayed in Figure 3a). When all the images of the six original training sets were annotated, they were then continuously supplemented by using a data augmentation algorithm within the IBM PowerAI Vision platform. These features were composed of blur, sharpen, color, crop, vertical flip, horizontal flip, rotate, and noise. Data augmentation created the new data set that contained all the existing images, plus the newly generated images. In this study context, which focused on the posterior maxillary teeth, four image processing features, including blur, sharpen, color, and noise, were used separately in each former data set to create new data sets. Each form of data augmentation was described as follows:  
Blur image processing. This study selected the maximum amount of Gaussian and motion blur. Gaussian blur made the entire image appear out of focus by reducing detail and noise. Motion blur made the image appear as if it was in motion. Ten new images were generated in the range of each former data set, which included 1-50, 1-100, 1-150, 1-200, 1-250, and 1-300. The former data sets were supplemented to 1-550, 1-1100, 1-1650, 1-2200, 1-2750, and 1-3300, respectively. Figure 4: NOVEM dental implant system. Sharpen image processing. This study selected the maximum amount of sharpening. Five new images were generated in the range of each former data set. The former data sets were supplemented to 1-300, 1-600, 1-900, 1-1200, 1-1500, and 1-1800, respectively. Color image processing. This study selected the maximum amount of change in the brightness, contrast, hue, and saturation of the images. Five new images were generated by using randomly selected values in the selected ranges by the algorithm itself. The former data sets were supplemented to 1-300, 1-600, 1-900, 1-1200, 1-1500, and 1-1800, respectively. Noise image processing. This study selected the maximum amount of noise to add to the new images. The IBM PowerAI Vision platform determined the need for a reasonable amount of noise for the images to remain usable. If 100% is selected, none of the generated images will have 100% noise added.