Automatic Tooth Detection and Numbering Using a Combination of a CNN and Heuristic Algorithm
Source abstract
Abstract
Dental panoramic radiography (DPR) is a method commonly used in dentistry for patient diagnosis. This study presents a new technique that combines a regional convolutional neural network (RCNN), Single Shot Multibox Detector, and heuristic methods to detect and number the teeth and implants with only fixtures in a DPR image. This technology is highly significant in providing statistical information and personal identification based on DPR and separating the images of individual teeth, which serve as basic data for various DPR-based AI algorithms. As a result, the mAP(@IOU = 0.5) of the tooth, implant fixture, and crown detection using the RCNN algorithm were obtained at rates of 96.7%, 45.1%, and 60.9%, respectively. Further, the sensitivity, specificity, and accuracy of the tooth numbering algorithm using a convolutional neural network and heuristics were 84.2%, 75.5%, and 84.5%, respectively. Techniques to analyze DPR images, including implants and bridges, were developed, enabling the possibility of applying AI to orthodontic or implant DPR images of patients.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Dental Radiography and Imaging · Advanced X-ray and CT Imaging · Medical Imaging and Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Industrial and Systems Engineering, Dongguk University, Seoul 04620, Korea
Medipartner Co., Ltd., Seoul 06135, Korea
Department of Oral and Maxillofacial Radiology, School of Dentistry, Chonnam National University, Gwangju 61186, Korea
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