Dental implantation is a surgical procedure in oral and maxillofacial surgery. Detecting missing tooth regions is essential for planning dental implant placement. This study proposes an automated method that detects regions of missing teeth in panoramic radiographic images. Tooth instance segmentation is required to accurately detect a missing tooth region in panoramic radiographic images containing obstacles, such as dental appliances or restoration. Therefore, we constructed a dataset that contains 455 panoramic radiographic images and annotations for tooth instance segmentation and missing tooth region detection. First, the segmentation model segments teeth into the panoramic radiographic image and generates teeth masks. Second, a detection model uses the teeth masks as input to predict regions of missing teeth. Finally, the detection model identifies the position and number of missing teeth in the panoramic radiographic image. We achieved 92.14% mean Average Precision (mAP) for tooth instance segmentation and 59.09% mAP for missing tooth regions detection. As a result, this method assists diagnosis by clinicians to detect missing teeth regions for implant placement.

Extraction of mandibular third molars is a common procedure in oral and maxillofacial surgery. There are studies that simultaneously predict the extraction difficulty of mandibular third molar and the complications that may occur. Thus, we propose a method of automatically detecting mandibular third molars in the panoramic radiographic images and predicting the extraction difficulty and likelihood of inferior alveolar nerve (IAN) injury. Our dataset consists of 4903 panoramic radiographic images acquired from various dental hospitals. Seven dentists annotated detection and classification labels. The detection model determines the mandibular third molar in the panoramic radiographic image. The region of interest (ROI) includes the detected mandibular third molar, adjacent teeth, and IAN, which is cropped in the panoramic radiographic image. The classification models use ROI as input to predict the extraction difficulty and likelihood of IAN injury. The achieved detection performance was 99.0% mAP over the intersection of union (IOU) 0.5. In addition, we achieved an 83.5% accuracy for the prediction of extraction difficulty and an 81.1% accuracy for the prediction of the likelihood of IAN injury. We demonstrated that a deep learning method can support the diagnosis for extracting the mandibular third molar.

Lee JK, et al. J Korean Assoc Oral Maxillofac Surg. 2013 Feb;39(1):9-13. http://dx.doi.org/10.5125/jkaoms.2013.39.1.9

The utilization of a cone-beam computed tomography (CT)-assisted surgical template allows for predictable results because implant placement plans can be performed in the actual surgery. In order to assess the accuracy of the CT-guided surgery, angular errors and shoulder/apex distance errors were evaluated by data fusion from before and after the placement. Computer-guided implant surgery was performed in five patients with 19 implants. In order to analyze differences of the implant fixture body between preoperative planned implant and postoperative placed implant, angular error and distance errors were evaluated. The mean angular errors between the preoperative planned and postoperative placed implant was 3.84° ± 1.49°; the mean distance errors between the planned and placed implants were 0.45 ± 0.48 mm horizontally and 0.63 ± 0.51 mm vertically at the implant neck and 0.70 ± 0.63 mm horizontally and 0.64 ± 0.57 mm vertically at the implant apex for all 19 implants. It is important to be able to utilize these methods in actual clinical settings by improving the various problems, including the considerations of patient mouth opening limitations, surgical guide preparation, and fixation.