Deep Learning Based Detection of Missing Tooth Regions for Dental Implant Planning in Panoramic Radiographic Images
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Dental Radiography and Imaging · Dental Implant Techniques and Outcomes · Dental Research and COVID-19
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Gwangju Institute of Science and Technology (GIST), School of Integrated Technology (SIT), Gwangju 61005, Korea
Department of Oral and Maxillofacial Surgery, College of Dentistry, Chosun University, Gwangju 61452, Korea
Funding
Ministry of Science ICT and Future Planning
References (49)
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