Kim HT, et al. J Adv Prosthodont. 2009 Nov;1(3):113-117. http://dx.doi.org/10.4047/jap.2009.1.3.113

Augmentation reality technology offers virtual information in addition to that of the real environment and thus opens new possibilities in various fields. The medical applications of augmentation reality are generally concentrated on surgery types, including neurosurgery, laparoscopic surgery and plastic surgery. Augmentation reality technology is also widely used in medical education and training. In dentistry, oral and maxillofacial surgery is the primary area of use, where dental implant placement and orthognathic surgery are the most frequent applications. Recent technological advancements are enabling new applications of restorative dentistry, orthodontics and endodontics. This review briefly summarizes the history, definitions, features, and components of augmented reality technology and discusses its applications and future perspectives in dentistry.

Determining the peri-implant marginal bone level on radiographs is challenging because the boundaries of the bones around implants are often unclear or the heights of the buccal and lingual bone levels are different. Therefore, a deep convolutional neural network (CNN) was evaluated for detecting the marginal bone level, top, and apex of implants on dental periapical radiographs. An automated assistant system was proposed for calculating the bone loss percentage and classifying the bone resorption severity. A modified region-based CNN (R-CNN) was trained using transfer learning based on Microsoft Common Objects in Context dataset. Overall, 708 periapical radiographic images were divided into training (n = 508), validation (n = 100), and test (n = 100) datasets. The training dataset was randomly enriched by data augmentation. For evaluation, average precision, average recall, and mean object keypoint similarity (OKS) were calculated, and the mean OKS values of the model and a dental clinician were compared. Using detected keypoints, radiographic bone loss was measured and classified. No statistically significant difference was found between the modified R-CNN model and dental clinician for detecting landmarks around dental implants. The modified R-CNN model can be utilized to measure the radiographic peri-implant bone loss ratio to assess the severity of peri-implantitis.

Kim HK, et al. J Adv Prosthodont. 2016 Feb;8(1):37-42. http://dx.doi.org/10.4047/jap.2016.8.1.37

Myagmar G, et al. J Adv Prosthodont. 2021 Jun;13(3):144-151. https://doi.org/10.4047/jap.2021.13.3.144