Deep Learning Based Detection of Missing Tooth Regions for Dental Implant Planning in Panoramic Radiographic Images

Abstract

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.

Missing Teeth and Restoration Detection Using Dental Panoramic Radiography Based on Transfer Learning With CNNs

“…This shows the excellent performance of tooth cutting and tooth positioning presented in this article. Compared with the 92.78% and 92.14% accuracy rates in [14] and [15], the accuracy rate in this study showed a 0.5% improvement as listed in Table Ⅳ. Even compared with 79.00% of the literature [13], the proposed method is a huge improvement.…”

“…AlexNet has 95.2% which is relatively low. The overall accuracy of the method in this paper is above 95% , which is greatly improved compared with the methods in the current state-of-the-art [15] and [33]- [34].…”

“…The accuracy for Restoration is even more pronounced that showed GoogleNet with better scores. Table Ⅸ summarizes the best results of the different models used in this study and compares them with current state-of-the-art [15] and [33]- [34]. From the results, whether it is for missing teeth or Restoration, this study has better recognition accuracy.…”

“…In this study, the previous research results [14] will be used to separate the teeth into a single sample for the tooth cutting of the X-ray ring dental film. And to perform tooth identification beyond wisdom teeth with through-like changes and spatial relationships Technology [15], and the recent automatic identification of tooth position based on Mask-RCNN [16] to discuss the accuracy. In this study, the technology of cutting teeth will be improved to increase accuracy.…”

Deep Learning to Detect and Classify Teeth, Dental Caries and Restorations: A Systematic Mapping

“…The primary version is composed of 1500 pairs of panoramic radiographs and binary masks of the pixel regions of the teeth in each radiograph, for semantic segmentation. One article combined the UFBA-UESC dataset with their own dataset [41].…”