Automatic Placement of Simulated Dental Implants Within CBCT Images in Optimum Positions: A Deep Learning Model

Abstract The increasing use of three-dimensional scans in dentistry motivates the use of artificial intelligence systems in several clinical scopes. One of these scopes is implant dentistry, which is a complex process with multiple criteria of success. The dentist analyzes cone-beam computed tomography (CBCT) images and formulates a detailed implant treatment plan by specifying the length, diameter, position, and angulation of implants while considering the prosthodontic treatment plan, bone morphology, and position of adjacent vital anatomical structures. A supervised deep learning model was built to determine the optimum implant position, angulation, diameter, and length as dictated by prosthetic and anatomical requirements. The main goal of building this model was to facilitate the planning procedure and reduce the planning time of implant treatment, especially in multiple implant cases. This study had two phases; the first was detecting and placing a bounding box around the fiducial markers and adjacent bone within the CBCT images by training YOLOv3 model. The second phase trained six deep regression models to extract the apical and occlusal coordinates (X, Y, Z) of the implants; each model predicted one target value. In addition, two deep classification models were developed for predicting the implants’ intra-osseous length and intra-osseous diameter. After testing these models, YOLOv3 in the first phase showed 46.78\% average precision; in the regression model, the mean absolute error ranged from 11.931 to 15.954, and the classification of the intra-osseous diameter showed 76\% accuracy, and the intra-osseous length showed 59\% accuracy. A subjective assessment by an expert prosthodontist found the results to be promising regarding selection of implant size, and mesiodistal placement of the implants. However, angulation and vertical and bucco-lingual position of the implants was unacceptable. Thus, further investigations and model training are required to improve the accuracy of the output.