## 2 Materials and Methods  
This study was approved by the Human Experimentation Committee, Faculty of Dentistry, Chiang Mai University, Chiang Mai, Thailand, with the certification of ethical clearance No. 30/2019. All of the 316 images used in this study were created from \"Digital Imaging and Communications in Medicine\" (DICOM) obtained from 184 CBCT scans of patients receiving treatment at the Center of Excellence for Dental Implantology, Faculty of Dentistry, Chiang Mai University, for the period from 2013 to 2018. The selection criteria of CBCT scans were based on the missing posterior maxillary teeth, which included the teeth from the first premolar to the second molar. The DICOM considered must be displayed in the properly formed dental arches without discontinuities in the hard tissues and without artefacts. One well-experienced dentist in digital dental implant treatment planning and AI was assigned to perform the study. DICOM reconstruction was performed in DentiPlan Pro version 3.7 software (DTP; NECTEC, NSTDA, Bangkok, Thailand) to create input images, which consisted of two types of images:  
1. The panoramic images are created by the imaginary panoramic line, which is drawn in the axial view from the left condyle connecting through the center of each tooth until reaching the right condyle. 2. The cross-sectional images are then generated in the desired implant position. If there was more than one implant to be placed in a patient, every cross-sectional image in each position of implants was created. For example, if the patient needed three implants, three cross-sectional images in each position of implants were created. A total of 316 images were then anonymously captured to ensure that no private information was revealed. The full-screenshot images were taken from DTP software with the joint photographic expert group (JPEG) format at 1920 \(\times\) 1080 pixels with 24-bit depth (Figure 1). Three hundred images were used for model training, which were randomly sorted from 1-300 to create six data sets, including 1-50, 1-100, 1-150, 1-200, 1-250, and 1-300. These six data sets were called the training set. The remaining 16 images were selected for accuracy testing to assess the performance of the developed model; this set of 16 images was called the testing set. The six original training sets were separately uploaded into the IBM PowerAI Vision platform (IBM Thailand Co., Ltd., Bangkok, Thailand), then the labelling process for model training was applied within the GUI of the platform. This user-defined data is significant for describing the desired output data. The labelled area in each image was generated in a square shape by connecting the four lines from the farthest border of the alveolar bone available for implant placement. The mentioned four lines included the upper border, lower border, mesial border, and distal border in a panoramic image, together with the upper border, lower border, buccal border, and lingual border in a cross-sectional image. The demonstration of the labelling process is displayed in Figures 2 and 3a. Figure 1: The full screen image capture of DentiPlan Pro version 3.7. Since this was an early step of AI development for dental implant treatment planning. A dental implant system with its available 3-dimensional stereolithography file (\(S\)TL) was used in this study (NOVEM dental implant system, Novem Innovations Co., Ltd., Chiang Mai, Thailand; Figure 4). The available diameter and length, which will be used in the posterior maxilla are as follows:  
* Implant diameter: 4.2 and 5.0 mm. * Implant length: 8, 10 and 12 mm. Figure 3: GUI of the IBM PowerAI Vision platform during the labelling process (**a**), during the testing process (**b**), the magnification of cross-sectional image during the labelling process (**c**), and the magnification of cross-sectional image during the testing process (**d**). Figure 2: Image labelling in both cross-sectional and panoramic images. The implant selection was dependent upon the available space, which included bone width and bone height.