Yağmur Ünal Suay, Namdar Pekiner Filiz
Marmara University, Faculty of Dentistry, Department of Oral and Maxillofacial Radiology, Istanbul, Turkey
Abstract
Background/Aim: The mandibular canal including the inferior alveolar nerve (IAN) is important in the extraction of the mandibular third molar tooth, which is one of the most frequently performed dentoalveolar surgical procedures in the mandible, and IAN paralysis is the biggest complication during this procedure. Today, deep learning, a subset of artificial intelligence, is in rapid development and has achieved significant success in the field of dentistry. Employing deep learning algorithms on CBCT images, a rare but invaluable resource, for precise mandibular canal identification heralds a significant leap forward in the success of mandibular third molar extractions, marking a promising evolution in dental practices. Material and Methods: The CBCT images of 300 patients were obtained. Labeling the mandibular canal was done and the data sets were divided into two parts: training (n=270) and test data (n=30) sets. Using the nnU-Netv2 architecture, training and validation data sets were applied to estimate and generate appropriate algorithm weight factors. The success of the model was checked with the test data set, and the obtained DICE score gave information about the success of the model. Results: DICE score indicates the overlap between labeled and predicted regions, expresses how effective the overlap area is in an entire combination. In our study, the DICE score found to accurately predict the mandibular canal was 0.768 and showed outstanding success. Conclusions: Segmentation and detection of the mandibular canal on CBCT images allows new approaches applied in dentistry and help practitioners with the diagnostic preoperative and postoperative process.
Keywords: deep learning; Artificial Intelligence; mandibular canal; CBCT
Reference
Ariji, Y., Mori, M., Fukuda, M., Katsumata, A., Ariji, E. (2022) Automatic visualization of the mandibular canal in relation to an impacted mandibular third molar on panoramic radiographs using deep learning segmentation and transfer learning techniques. Oral Surg Oral Med Oral Pathol Oral Radiol, 134(6): 749-757
Buyuk, C., Akkaya, N., Arsan, B., Unsal, G., Aksoy, S., Orhan, K. (2022) A fused deep learning architecture for the detection of the relationship between the mandibular third molar and the mandibular canal. Diagnostics (Basel), 12 (8): 2018
1
Castro, M.A.A., Lagravere-Vich, M.O., Amaral, T.M.P., Abreu, M.H.G., Mesquita, R.A. (2015) Classifications of mandibular canal branching: A review of literature. World J Radiol, 7(12): 531-537
Corbella, S., Srinivas, S., Cabitza, F. (2021) Applications of deep learning in dentistry. Oral Surg Oral Med Oral Pathol Oral Radiol, 132: 225-238
de Diego, I.M., Redondo, A.R., Fernández, R.R., Navarro, J., Moguerza, J.M. (2022) General Performance Score for classification problems. Applied Intelligence, 52(10): 12049-12063
Fukuda, M., Ariji, Y., Kise, Y., Nozawa, M., Kuwada, C., Funakoshi, T., i dr. (2020) Comparison of 3 deep learning neural networks for classifying the relationship between the mandibular third molar and the mandibular canal on panoramic radiographs. Oral Surg Oral Med Oral Pathol Oral Radiol, 130(3): 336-343
Goutte, C., Gaussier, E. (2005) A probabilistic interpretation of precision, recall and F-score, with implication for evaluation. u: European conference on information retrieval, Berlin, Heidelberg: Springer Berlin Heidelberg, 345-359
He, K., Zhang, X., Ren, S., Sun, J. (2015) Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. u: Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile: IEEE, 1026-1034
1
Hricak, H. (2016) New horizons lecture: Beyond imaging-radiology of tomorrow. Radiology, 286 (3): 764-775
1
Jung, Y., Nah, K., Cho, B. (2012) Correlation of panoramic radiographs and cone beam computed tomography in the assessment of a superimposed relationship between the mandibular canal and impacted third molars. Imaging Sci Dent, 42(3): 121-127
Keser, G., Pekiner, N.F. (2023) Artificial Intelligence Applications in Dentistry. u: Current Researches in Health Sciences-I, İstanbul: Özgür Publication, 51-68
Lahoud, P., Ezeedeen, M., Beznik, T., Willems, H., Leite, A., van Gerven, A., Jacobs, R. (2021) Artificial intelligence for fast and accurate 3-dimensional tooth segmentation on conebeam computed tomography. 47 (5): 827-835
Libersa, P., Savignat, M., Tonnel, A. (2007) Neurosensory disturbances of the inferior alveolar nerve: a retrospective study of complaints in a 10-year period. J Oral Maxillofac Surg, 65 (8): 1486-1489
Liu, M.Q., Xu, Z.N., Mao, W.Y., Li, Y., Zhang, X.H., Hl, B., i dr. (2022) Deep learning-based evaluation of the relationship between mandibular third molar and mandibular canal on CBCT. Clin Oral Investig, 26(1): 981-991
Mukherjee, S., Vikraman, B., Sankar, D., Veerabahu, M.S. (2016) Evaluation of outcome following coronectomy for the management of mandibular third molars in close proximity to inferior alveolar nerve. J Clin Diagn Res, 10 (8): ZC57-62
Orhan, K., Bilgir, E., Bayrakdar, I.S., Ezhov, M., Gusarev, M., Shumilov, E. (2021) Evaluation of artificial intelligence for detecting impacted third molars on cone-beam computed tomography scans. J Stomatol Oral Maxillofac Surg, 122 (4): 333-337
Prados-Privado, M., Villalón, J.G., Martínez-Martínez, C.H., Ivorra, C. (2020) Dental images recognition technology and applications: A literature review. Appl Sci (Basel), 10(8): 2856
1
Rood, J.P., Shehab, B.A. (1990) The radiological prediction of inferior alveolar nerve injury during third molar surgery. Br J Oral Maxillofac Surg, 28(1): 20-25
Russakovsky, O.J., Deng, J., Suetal, H. (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis, 115(3): 211-252
1
Schwendicke, F., Golla, T., Dreher, M., Krois, J. (2019) Convolutional neural networks for dental image diagnostics: A scoping review. J Dent, Vol. 91: 103226
Stafie, C.S., Sufaru, I.G., Ghiciuc, C.M., Stafie, I.I., Sufaru, E.C., Solomon, S.M., Hancianu, M. (2023) Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review. Diagnostics (Basel), 13 (12): 1995
Sukegawa, S., Tanaka, F., Hara, T., Yoshii, K., Yamashita, K., Nakano, K., Takabatake, K., Kawai, H., Nagatsuka, H., Furuki, Y. (2022) Deep learning model for analyzing the relationship between mandibular third molar and inferior alveolar nerve in panoramic radiography. Sci Rep, 12 (1): 16925
Suzuki, K. (2017) Overview of deep learning in medical imaging. Radiol Phys Technol, 10(3): 257-273
Vinayahalingam, S., Xi, T., Bergé, S., Maal, T., de Jong, G. (2019) Automated detection of third molars and mandibular nerve by deep learning. Sci Rep, 9 (1): 9007
Weckx, A., Agbaje, J.O., Sun, Y., Jacobs, R., Politis, C. (2016) Visualization techniques of the inferior alveolar nerve (IAN): a narrative review. Surg Radiol Anat, 38 (1): 55-63
Yoo, J.H., Yeom, H.G., Shin, W., Yun, J.P., Lee, J.H., Jeong, S.H., i dr. (2021) Deep learning based prediction of extraction difficulty for mandibular third molars. Sci Rep, 11 (1): 1954
Zeiler, M.D., Fergus, R. (2014) Visualizing and understanding convolutional networks. u: Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland: Springer International Publishing, 818-833
Zhu, T., Chen, D., Wu, F., Zhu, F., Zhu, H. (2021) Artificial intelligence model to detect real contact relationship between mandibular third molars and inferior alveolar nerve based on panoramic radiographs. Diagnostics (Basel), 11 (9): 1664
Balkan Journal of Dental Medicine, 2024, vol. 28, br. 2, str. 122-128