Abstract
Artificial intelligence (AI) has proven very useful in dental applications in recent years. The automatic extraction of information from X-ray panoramic dental radiographs represents an opportunity to streamline dental diagnosis and improve the precision of detecting oral pathologies. In particular, deep learning (DL) algorithms based on convolutional neural networks (CNNs) have demonstrated high performance in dental image classification, detection, and segmentation applications. However, several challenges remain due to the high variability of categories in dental applications. To characterize four different third molars angles employing X-ray panoramic radiographs, we focused on using the CNN-based object detection technique You Only Look Once (YOLO), which works with the GoogleNet feature extractor CNN. For this, we applied Winter’s classification criterion, which describes the position of the third molars with respect to the longitudinal axis of the second molar. Third molar angles were divided into different categories: distoangular, vertical, mesioangular, and horizontal. A total of 644 panoramic X-ray images were used to train the YOLO algorithm. The proposed model reached an average accuracy performance of up to 97% on the test dataset. These findings exhibit the potential and reassuring results of employing CNNs for dental applications, specifically for object detection in panoramic X-rays.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Al-Huseiny, M.S., Sajit, A.S.: Transfer learning with googlenet for detection of lung cancer. Indonesian J. Electr. Eng. Comput. Sci. 22(2), 1078–1086 (2021)
AlSayyed, A., et al.: Employing CNN ensemble models in classifying dental caries using oral photographs. Int. J. Data Netw. Sci. 7(4), 1535–1550 (2023)
Arora, S., Tripathy, S.K., Gupta, R., Srivastava, R.: Exploiting multimodal CNN architecture for automated teeth segmentation on dental panoramic x-ray images. Proc. Inst. Mech. Eng. [H] 237(3), 395–405 (2023)
Chen, S.L., et al.: Missing teeth and restoration detection using dental panoramic radiography based on transfer learning with CNNs. IEEE Access 10, 118654–118664 (2022)
Chen, S.L., et al.: Automated detection system based on convolution neural networks for retained root, endodontic treated teeth, and implant recognition on dental panoramic images. IEEE Sens. J. 22(23), 23293–23306 (2022)
De Tobel, J., Radesh, P., Vandermeulen, D., Thevissen, P.W.: An automated technique to stage lower third molar development on panoramic radiographs for age estimation: a pilot study. J. Foren. Odontostomatol. 35(2), 42 (2017)
Du, J.: Understanding of object detection based on cnn family and yolo. In: Journal of Physics: Conference Series. vol. 1004, p. 012029. IOP Publishing (2018)
Escoda, C.G., Aytes, L.B.: Cirugía bucal. Océano (2011)
Fu, Q., et al.: A deep learning algorithm for detection of oral cavity squamous cell carcinoma from photographic images: a retrospective study. E Clin. Med. 27 (2020)
Lee, J.H., Kim, D.H., Jeong, S.N., Choi, S.H.: Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J. Dent. 77, 106–111 (2018)
Ormeño-Arriagada, P., Navarro, E., Taramasco, C., Gatica, G., Vásconez, J.P.: Deep learning techniques for oral cancer detection: Enhancing clinical diagnosis by resnet and densenet performance. In: International Conference on Applied Informatics, pp. 59–72. Springer (2024)
Primo, F.T., Primo, B.T., Scheffer, M.A.R., Hernández, P.A.G., Rivaldo, E.G.: Evaluation of 1211 third molars positions according to the classification of winter, pell & gregory. Int j odontostomatol 11(1), 61–5 (2017)
Sathya, B., Neelaveni, R.: Transfer learning based automatic human identification using dental traits-an aid to forensic odontology. J. Forensic Leg. Med. 76, 102066 (2020)
Terven, J., Córdova-Esparza, D.M., Romero-González, J.A.: A comprehensive review of yolo architectures in computer vision: From yolov1 to yolov8 and yolo-nas. Mach. Learn. Knowl. Extract. 5(4), 1680–1716 (2023)
Thanh, M.T.G., et al.: Deep learning application in dental caries detection using intraoral photos taken by smartphones. Appl. Sci. 12(11), 5504 (2022)
Tuzoff, D.V., et al.: Tooth detection and numbering in panoramic radiographs using convolutional neural networks. Dentomaxillofacial Radiol. 48(4), 20180051 (2019)
Vasconez, J.P., Admoni, H., Cheein, F.A.: A methodology for semantic action recognition based on pose and human-object interaction in avocado harvesting processes. Comput. Electron. Agric. 184, 106057 (2021)
Vasconez, J.P., Cheein, F.A.: Finding a proper approach to obtain cognitive parameters from human faces under illumination variations. In: 2018 5th International Conference on Control, Decision and Information Technologies (CoDIT), pp. 946–951. IEEE (2018)
Vasconez, J.P., Salvo, J., Auat, F.: Toward semantic action recognition for avocado harvesting process based on single shot multibox detector. In: 2018 IEEE International Conference on Automation/XXIII Congress of the Chilean Association of Automatic Control (ICA-ACCA), pp. 1–6. IEEE (2018)
Vilcapoma, P., et al.: Comparison of faster r-CNN, yolo, and SSD for third molar angle detection in dental panoramic x-rays. Sensors 24(18), 6053 (2024)
Yilmaz, E., Trocan, M.: A modified version of googlenet for melanoma diagnosis. J. Inform. Telecomm. 5(3), 395–405 (2021)
Acknowledgments
This work has been supported by ANID (National Research and Development Agency of Chile) under Fondecyt Iniciación 2024 Grant 11240105.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Vilcapoma, P., Meléndez, D.P., Vásconez, I.N., Gatica, G., Vásconez, J.P. (2025). Third Molar Angle Detection in Dental X-Ray Panoramic Radiographs Using YOLO and GoogleNet Convolutional Neural Networks. In: Guarda, T., Portela, F., Gatica, G. (eds) Advanced Research in Technologies, Information, Innovation and Sustainability. ARTIIS 2024. Communications in Computer and Information Science, vol 2346. Springer, Cham. https://doi.org/10.1007/978-3-031-83210-9_3
Download citation
DOI: https://doi.org/10.1007/978-3-031-83210-9_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-83209-3
Online ISBN: 978-3-031-83210-9
eBook Packages: Computer ScienceComputer Science (R0)