Background: The diagnosis of dental implants and the periapical tissues using periapical radiographs is crucial. Recently, artificial intelligence has shown a rapid advancement in the field of radiographic imaging. Purpose: This study attempted to detect dental implants and peri-implant tissues by using a deep learning method known as object detection on the implant image of periapical radiographs. Methods: After implant treatment, the periapical images were collected and data were processed by labeling the dental implant and peri-implant tissue together in the images. Next, 300 images of the periapical radiographs were split into 80:20 ratio (i.e. 80% of the data were used for training the model while 20% were used for testing the model). These were evaluated using an object detection model known as Faster R-CNN, which simultaneously performs classification and localization. This model was evaluated on the classification performance using metrics, including precision, recall, and F1 score. Additionally, in order to assess the localization performance, an evaluation through intersection over union (IoU) was utilized, and, Average Precision (AP) was used to assess both the classification and localization performance. Results: Considering the classification performance, precision = 0.977, recall = 0.992, and F1 score = 0.984 were derived. The indicator of localization was derived as mean IoU = 0.907. On the other hand, considering the indicators of both classification and localization performance, AP showed an object detection level of AP@0.5 = 0.996 and AP@0.75 = 0.967. Conclusion: Thus, the implementation of Faster R-CNN model for object detection on 300 periapical radiographic images including dental implants, resulted in high-quality object detection for dental implants and peri-implant tissues.

Evaluating tooth mobility is clinically significant, not only for diagnosing periodontal tissues but also in determining the overall periodontal treatment plan. Numerous studies related to tooth mobility have been conducted over the years, including the proposal of various classifications as well as the development of electronic devices for objective measurement. However, there is still no consensus on the measurement methods and criteria for assessing tooth mobility. In this study, we provide a comprehensive review of past and current tooth mobility classification and measurement methods. In order to propose a new method to intuitively evaluate tooth mobility based on previous studies, a digital approach capable of recording tooth micromovements induced by dynamic load should be considered.

The demand for implant dentistry, which includes customized abutments, is increasing. A lot of pollutions are generated on the customized abutment surface following milling procedure. This study evaluated the surface topography and cleanliness of customized abutments after cleaning procedures, which are simply applicable in the dental clinic. Thirty computer-aided design and computer-aided manufacturing internal connection type titanium abutments were produced, milled, and randomly divided into 3 groups: steam cleaning (control group), chlorhexidine (CHX) scrubbing (test group 1), and ultrasonic cleaning with CHX solution, acetone, and ethyl alcohol (test group 2). Each group was evaluated using microscopic and microbial analysis. Foreign bodies were observed on the abutment surfaces in control group and test group 1, but not in test group 2. Bacteria were observed on 40% of the agar plates following steam cleaning; most of the colonies consisted of Bacillus cereus and Staphylococcus warneri. Colony growth was absent following test group 1 and 2. For customized abutments, cleaning with steam is ineffective. CHX scrubbing effectively eliminates only bacteria. Ultrasonic cleaning with CHX solution, acetone, and ethyl alcohol successfully removes both foreign bodies and bacteria. Thus, the ultrasonic cleaning method is conveniently applicable in the dental clinic for eliminating contamination of the customized abutment surface.

Kwon JH, et al. J Adv Prosthodont. 2009 Mar;1(1):37-40. http://dx.doi.org/10.4047/jap.2009.1.1.37