Acknowledgement
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2020R1F1A1072484).
References
- Chen Y W, Stanley K, Att W : Artificial intelligence in dentistry: current applications and future perspectives. Quintessence Int, 51:248-257, 2020.
- Hwang J J, Jung Y H, Cho B H, et al. : An overview of deep learning in the field of dentistry. Imaging Sci Dent, 49:1-7, 2019. https://doi.org/10.5624/isd.2019.49.1.1
- Ariji Y, Yanashita Y, Kutsuna S, et al. : Automatic detection and classification of radiolucent lesions in the mandible on panoramic radiographs using a deep learning object detection technique. Oral Surg Oral Med Oral Pathol Oral Radiol, 128:424-430, 2019. https://doi.org/10.1016/j.oooo.2019.05.014
- Schwendicke F, Samek W, Krois J : Artificial Intelligence in Dentistry: Chances and Challenges. J Dent Res, 99:769-774, 2020. https://doi.org/10.1177/0022034520915714
- Anthonappa RP, King NM, Rabie AB : Diagnostic tools used to predict the prevalence of supernumerary teeth: a meta-analysis. Dentomaxillofac Radiol, 41:444-449, 2012. https://doi.org/10.1259/dmfr/19442214
- Tsiklakis K, Mitsea A, Tsichlaki A, et al. : A systematic review of relative indications and contra-indications for prescribing panoramic radiographs in dental paediatric patients. Eur Arch Paediatr Dent, 21:387-406, 2020. https://doi.org/10.1007/s40368-019-00478-w
- Shah A, Gill D S, Tredwin C, et al. : Diagnosis and management of supernumerary teeth. Dent Update, 35:510-512, 514-516, 519-520, 2008. https://doi.org/10.12968/denu.2008.35.8.510
- Rajab LD, Hamdan MA : Supernumerary teeth: review of the literature and a survey of 152 cases. Int J Paediatr Dent, 12:244-254, 2002. https://doi.org/10.1046/j.1365-263X.2002.00366.x
- Omer RS, Anthonappa RP, King NM : Determination of the optimum time for surgical removal of unerupted anterior supernumerary teeth. Pediatr Dent, 32:14-20, 2010.
- Ata-Ali F, Ata-Ali J, Penarrocha-Oltra D, et al. : Prevalence, etiology, diagnosis, treatment and complications of supernumerary teeth. J Clin Exp Dent, 6:414-418, 2014. https://doi.org/10.4317/jced.51499
- Park K, Lee D, Kim J, et al. : Timing for Removal of Mesiodens in Relation to the Maxillary Cental Incisors. J Korean Acad Pediatr Dent, 43:246-253, 2016.
- Katheria BC, Kau CH, Tate R, et al. : Effectiveness of impacted and supernumerary tooth diagnosis from traditional radiography versus cone beam computed tomography. Pediatr Dent, 32:304-309, 2010.
- Anthonappa RP, King NM, Rabie AB, et al. : Reliability of panoramic radiographs for identifying supernumerary teeth in children. Int J Paediatr Dent, 22:37-43, 2012. https://doi.org/10.1111/j.1365-263X.2011.01155.x
- Gavala S, Donta C, Tsiklakis K, et al. : Radiation dose reduction in direct digital panoramic radiography. Eur J Radiol, 71:42-48, 2009. https://doi.org/10.1016/j.ejrad.2008.03.018
- Kuwada C, Ariji Y, Fukuda M, et al. : Deep learning systems for detecting and classifying the presence of impacted supernumerary teeth in the maxillary incisor region on panoramic radiographs. Oral Surg Oral Med Oral Pathol Oral Radiol, 130:464-469, 2020. https://doi.org/10.1016/j.oooo.2020.04.813
- Ryu G, Song JS, Shin TJ, et al. : Retrospective study on three-dimensional characteristics of mesiodens using CBCT in pediatric dentistry. J Korean Acad Pediatr Dent, 48:77-94, 2021.
- Kilic MC, Bayrakdar IS, Celik O, et al. : Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs. Dentomaxillofac Radiol, 20200172, 2021.
- Abdalla-Aslan R, Yeshua T, Kabla D, et al. : An artificial intelligence system using machine-learning for automatic detection and classification of dental restorations in panoramic radiography. Oral Surg Oral Med Oral Pathol Oral Radiol, 130:593-602, 2020. https://doi.org/10.1016/j.oooo.2020.05.012
- Ekert T, Krois J, Meinhold L, et al. : Deep Learning for the Radiographic Detection of Apical Lesions. J Endod, 45:917-922.e5, 2019. https://doi.org/10.1016/j.joen.2019.03.016
- Lee J H, Kim D H, Jeong S N, et al. : Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J Dent, 77:106-111, 2018. https://doi.org/10.1016/j.jdent.2018.07.015
- Teachable Machine. Available from URL: https://teachablemachine.withgoogle.com (Accessed on March 30, 2021).