Research Article
Tooth Detection and Numbering in Panoramic Radiographs Using YOLOv8-Based Approach
@INPROCEEDINGS{10.1007/978-3-031-60665-6_18, author={Felipe Rog\^{e}rio Silva Teles and Alison Corr\"{e}a Mendes and Anselmo Cardoso de Paiva and Jo\"{a}o Dallyson Sousa de Almeida and Geraldo Braz Junior and Arist\^{o}fanes Corr\"{e}a Silva and Pedro De Alcantara Dos Santos Neto}, title={Tooth Detection and Numbering in Panoramic Radiographs Using YOLOv8-Based Approach}, proceedings={Wireless Mobile Communication and Healthcare. 12th EAI International Conference, MobiHealth 2023, Vila Real, Portugal, November 29-30, 2023 Proceedings}, proceedings_a={MOBIHEALTH}, year={2024}, month={6}, keywords={tooth detection panoramic radiography deep learning dental image}, doi={10.1007/978-3-031-60665-6_18} }- Felipe Rogério Silva Teles
Alison Corrêa Mendes
Anselmo Cardoso de Paiva
João Dallyson Sousa de Almeida
Geraldo Braz Junior
Aristófanes Corrêa Silva
Pedro De Alcantara Dos Santos Neto
Year: 2024
Tooth Detection and Numbering in Panoramic Radiographs Using YOLOv8-Based Approach
MOBIHEALTH
Springer
DOI: 10.1007/978-3-031-60665-6_18
Abstract
Before a dental professional performs any procedure or diagnosis, they need to know the patient’s dental arch. For that, it is common for them to ask the patient to take a panoramic radiograph. The use of neural networks to assist this professional in this stage is not recent, and most studies use segmentation networks to solve the problem. However, the segmentation result does not make explicit the specific position of the tooth and its numbering according to the international system (FDI), presenting only more specific details. In this study, we aimed to use a powerful and efficient detection neural network called You Only Look Once v8 to perform automated tooth detection and numbering based on FDI, using a dataset that contains 166 anonymized and deidentified panoramic dental radiographs of patients from Noor Medical Imaging Center, Qom, Iran, and are public. Labels were created using an online tool for production in the YOLO standard. The metrics used to evaluate the trained model were precision, recall, and mAP50. The results of each were 0.95818, 0.95505, and 0.97384. The conclusion of the study uses the model training generated a weight to test the model in a real-world scenario.
