## 1 Introduction  
Bone quantity evaluation in dental implantology is crucial. It is an essential step for dental implant treatment planning. The result of faulty consideration during pre-surgical planning often leads to implant-related complications. The success of dental implant treatment is principally related to bone quality of patients, patient evaluation, and good treatment planning [1]. The precise measuring of bone architecture with conventional 2D radiography is limited by difficulty in assessing hard tissue morphology and both the quality and quantity of bone. There is little information about the buccolingual and cross-sectional dimension, resulting in inadequate identification of critical structures and restriction of spatial data for vital structures [2]. Cone-beam computed tomography (CBCT) provides a unique imaging analysis of proposed implant sites by reformatting the image data to create several imaging modalities. The specific software used for creating the images utilizes multiple tools, which can precisely mark the vital structures and provide a 1:1 image for accurate measurements. The utility of CBCT for dental implant treatment planning has been well investigated [2; 3; 4]. However, analyzing CBCT scans requires a specific level of training and expertise. This analysis is time-consuming, involving hundreds of images. Learning is an essential part of the human brain, which creates intelligent behavior. Artificial intelligence (AI) is defined as the branch of computer science focused on a simulation of human intelligence adopted by machines based on the information they collected [5]. These principles relate to the algorithms used in knowledge representation and their implementation. Computer vision is a field of AI that deals with the automation of the tasks mimicking the human visual system, which can be performed by enabling computers to derive significant information from digital visual inputs and take actions based on that information [6]. One of the most prominent applications in medical computer vision is the IBM Watson AI platform in the field of oncology. It has been deployed by cancer centers to help medical professionals quickly diagnose and determine the best treatments for patients [7]. Among AI technologies, the IBM PowerAI Vision platform offers a built-in deep learning algorithm that learns to analyze images. It includes a graphical user interface (GUI) to label objects in the images, which can be used to train and validate a model. The IBM PowerAI Vision platform provides a faster R-CNN algorithm as an image object detection model. The faster R-CNN algorithm has been optimized for accuracy [8; 9; 10]. It was first used as a proposal generator. Then, the feature vectors of generated proposals were encoded by deep convolutional neural networks, followed by making the object predictions. The developed model was then deployed for further application [11]. For limited data acquisition, the IBM PowerAI Vision platform also provides a data augmentation algorithm that processes the transformations of images, such as blur, sharpen, and rotate, to create new versions of existing images to increase the number of data samples for satisfactory AI performance. With the continued adoption of both CBCT and AI, interpreting a large number of images is another area in which AI can promote benefaction. There are many developers focusing on AI that can automatically assess whole images of CBCT scans and propose the most potential diagnosis by reviewing similar images stored in databases. These processes of interpretation can be performed by AI far faster than human technicians. Implant treatment planning in the posterior maxillary region is preferable for early AI development, because the implant's angulation is perpendicular to the occlusal plane and the vital structures, such as the maxillary sinus, are clearly visible.