Identification of Mesiodens Using Machine Learning Application in Panoramic Images
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Seung, Jaegook
(Department of Pediatric Dentistry and Institute of Oral Bioscience, School of Dentistry, Jeonbuk National University)
Kim, Jaegon (Department of Pediatric Dentistry and Institute of Oral Bioscience, School of Dentistry, Jeonbuk National University) Yang, Yeonmi (Department of Pediatric Dentistry and Institute of Oral Bioscience, School of Dentistry, Jeonbuk National University) Lim, Hyungbin (Department of Pediatric Dentistry and Institute of Oral Bioscience, School of Dentistry, Jeonbuk National University) Le, Van Nhat Thang (Department of Pediatric Dentistry and Institute of Oral Bioscience, School of Dentistry, Jeonbuk National University) Lee, Daewoo (Department of Pediatric Dentistry and Institute of Oral Bioscience, School of Dentistry, Jeonbuk National University) |
1 | 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. DOI |
2 | 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. DOI |
3 | Rajab LD, Hamdan MA : Supernumerary teeth: review of the literature and a survey of 152 cases. Int J Paediatr Dent, 12:244-254, 2002. DOI |
4 | 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. |
5 | 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. DOI |
6 | 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. |
7 | Schwendicke F, Samek W, Krois J : Artificial Intelligence in Dentistry: Chances and Challenges. J Dent Res, 99:769-774, 2020. DOI |
8 | 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. DOI |
9 | 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. DOI |
10 | 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. |
11 | Chen Y W, Stanley K, Att W : Artificial intelligence in dentistry: current applications and future perspectives. Quintessence Int, 51:248-257, 2020. |
12 | 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. DOI |
13 | 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. DOI |
14 | 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. DOI |
15 | 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. DOI |
16 | 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. |
17 | Teachable Machine. Available from URL: https://teachablemachine.withgoogle.com (Accessed on March 30, 2021). |
18 | 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. |
19 | Ekert T, Krois J, Meinhold L, et al. : Deep Learning for the Radiographic Detection of Apical Lesions. J Endod, 45:917-922.e5, 2019. DOI |
20 | 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. DOI |
21 | Gavala S, Donta C, Tsiklakis K, et al. : Radiation dose reduction in direct digital panoramic radiography. Eur J Radiol, 71:42-48, 2009. DOI |
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