Deep learning to support the detection and classification of teeth, dental caries and restorations: a review
DOI:
https://doi.org/10.34119/bjhrv7n2-156Keywords:
oral diagnosis, artificial intelligence, computer vision systems, dental caries, dental restorationAbstract
Oral health serves as a crucial indicator of individuals' overall well-being and quality of life, making it a pertinent concern regularly addressed by healthcare professionals. Utilizing imaging exams is imperative for detecting and identifying oral diseases and conditions, and the application of Artificial Intelligence (AI) has garnered attention for its potential in this realm. We conducted a systematic literature review focusing on the utilization of Deep Learning techniques in dental radiographs for the detection, segmentation, and classification of teeth, caries, and restorations. Our review encompassed automated searches across prominent databases including the ACM Digital Library, IEEE Xplore Digital Library, PubMed, and Scopus, yielding 393 primary papers published between 2012 and 2023. Following stringent inclusion and exclusion criteria, we thoroughly examined 68 papers, assessing their consistency and adequacy of different aspects in terms of the databases used, techniques implemented, and outcomes reported. It was noted that 41.66% of the analyzed papers lacked clear information regarding approval of data usage from ethics committees. Additionally, despite the interdisciplinary nature of computational techniques in oral health, 38.23% of the surveyed studies were conducted by teams comprising solely professionals from one specific area. Moreover, 66.18% of the papers focused solely on panoramic radiographs, with commonly utilized metrics including accuracy, recall, and precision. Notably, the U-Net and Mask R-CNN networks emerged as the most frequently applied methodologies. Despite the proliferation of investigations in this field, several challenges persist, including the limited availability of public datasets, inadequate detailing of developed methodologies, and a lack of systematization in result presentation. These challenges hinder a fair comparison between studies, presenting a significant obstacle to be addressed for further progress in the field.
References
Akkaya, N, Kansu O, Kansu H, Cagirankaya L, Arslan U. Comparing the accuracy of panoramic and intraoral radiography in the diagnosis of proximal caries. Dentomaxillofacial Radiology, v. 35, n. 3, p. 170-174, 2006.
Ari, T, Salam H, kszolu H, Kazan O, Bayrakdar , Duman SB, et al. Automatic feature segmentation in dental periapical radiographs. Diagnostica, v. 12, n. 12, 2022. ISSN 2075-4418. Disponível em: https://www.mdpi.com/ 2075-4418/12/12/3081.
Baydar,O, Ryo-Kalinowska I, Futyma-Gbka K, Salam H. The U-Net approaches to evaluation of dental bitewing radiographs: An artificial intelligence study. Diagnostica, v. 13, n. 3, 2023. ISSN 2075-4418. Disponível em: https://www.mdpi.com/2075-4418/13/3/453.
Bayrakdar I, Orhan K, Akarsu S, elik , Atasoy S, Pekince A, et al. Deep-learning approach for caries detection and segmentation on dental bitewing radiographs. Oral Radiology, 11 2021.
Bayraktar Y, Ayan E. Diagnosis of interproximal caries lesions with deep convolutional neural network in digital bitewing radiographs. Clinical Oral Investigations, v. 26, 01 2022.
Bilgir E, Bayrakdar I, elik , Orhan K, Akkoca Kaplan F, Saglam H, et al. An artificial intelligence approach to automatic tooth detection and numbering in panoramic radiographs. BMC Medica! Imaging, v. 21, 08 2021. DOI: https://doi.org/10.1136/bmj.h5527
Bossuyt PM, Reitsma JB, Bruns DE, Gatsonis CA, Glasziou PP, Irwig L, et al. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. BMJ, BMJ Publishing Group Ltd, v. 351, 2015.
Bouchahma M, Ben Hammouda S, Kouki S, Alshemaili M, Samara K. An automatic dental decay treatment prediction using a deep convolutional neural network on X-Ray images. In: 2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA). [S.l.: s.n.], 2019. p. 14.
Cantu AG, Gehrung S, Krois J, Chaurasia A, Rossi JG, Gaudin R, et al. Detecting caries lesions of different radiographic extensions on bitewings using Deep Learning. Journal of Dentistry, v. 100, p. 103425, 2020. ISSN 0300-5712.
Cha JY, Yoon HI, Yeo IS, Huh KH, Han JS. Panoptic segmentation on panoramic radiographs: Deep learning-based segmentation of various structures including maxillary sinus and mandibular canal. Journal of Clinical Medicine, v. 10, n. 12, 2021. ISSN 2077-0383.
Chandrashekar G, AlQarni S, Bumann EE, Lee Y. Collaborative deep learning model for tooth segmentation and identification using panoramic radiographs. Computers in Biology and Medicine, v. 148, p. 105829, 2022. ISSN 0010-4825. Disponível em www.sciencedirect.com/science/paper/pii/S0010482522005881.
Chang HJ, Lee S, Yong TH, Shin NY, Jang BG, Kim JE, et al. Deep learning hybrid method to automatically diagnose periodontal bone loss and stage periodontitis. Scientific Reports, v. 10, 05 2020.
Chen H, Zhang K, Lyu P, Li H, Zhang L, Wu J, et al. A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films. Scientific Reports, v. 9, 03 2019.
Chen SL, Chen TY, Huang YC, Chen CA, Chou HS, Huang YY, et al. Missing teeth and restoration detection using dental panoramic radiography based on transfer learning with CNNS. IEEE Access, v. 10, p. 118654118664, 2022.
Chen SL, Chen TY, Mao YC, Lin SY, Huang YY, Chen CA, et al. Automated detection system based on convolutional neural networks for retained root, endodontic treated teeth, and implant recognition on dental panoramic images. IEEE Sensors Journal, v. 22, n. 23, p. 23293-23306, 2022.
von-Elm E, Altman DG, Egger M. The strengthening the reporting of observational studies in epidemiology (STROBE) statement: Guidelines for reporting observational studies. Annals of Internai Medicine, v. 147, n. 8, p. 573-577, 2007. DOI:https://doi.org/10.1016/j.jclinepi.2007.11.008
Estai M, Tennant M, Gebauer D, Brostek A, Vignarajan J, Mehdizadeh M, et al. Deep learning for automated detection and numbering of permanent teeth on panoramic images. Dentomaxillofacial Radiology, v. 51, n. 2, p. 20210296, 2022.
Fariza A, Arifin AZ, Astuti ER. Automatic tooth and background segmentation in dental X-Ray using U-Net convolutional network. In: 2020 6th International Conference on Science in Information Technology (ICSITech), 2020. p. 144-149.
Faure J, Engelbrecht A. A convolutional neural network for dental panoramic radiograph classification. In: 2021 5th International Conference on Intelligent Systems, Metaheuristics and Swarm Intelligence. New York, NY, USA: Association for Computing Machinery, 2021. (ISMSI 2021), p. 54-59. ISBN 9781450389679. Disponível em: https://doi.org/10.1145/3461598.3461607.
Araujo Fernandes AF, Dorea J, Rosa G. Image analysis and computer vision applications in animal sciences: An overview. Frontiers in Veterinary Science, v. 7, p. 551269, 10 2020.
GURSES, A.; OKTAY, A. B. Human identification with panoramic dental images using mask R-CNN and surf. In: 2020 5th International Conference on Computer Science and Engineering (UBMK). [S.l.: s.n.], 2020. p. 232-237.
Gurses, A.; Oktay, A. B. Tooth restoration and dental work detection on panoramic dental images via CNN. In: 2020 Medical Technologies Congress (TIPTE- KNO). [S.l.: s.n.], 2020. p. 1-4.
Harsh P, Chakraborty R, Tripathi S, Sharma K. Attention U-Net architecture for dental image segmentation. In: 2021 International Conference on Intelligent Technologies (CONIT). [S.l.: s.n.], 2021. p. 1-5.
Hou S, Zhou T, Liu Y, Dang P, Lu H, Shi H. Teeth U-Net: a segmentation model of dental panoramic X-Ray images for context semantics and contrast enhancement. Computers in Biology and Medicine, v. 152, p. 106296, 2023. ISSN 0010-4825.https://www.sciencedirect.com/science/paper/pii/S001048252201004
Hou S, Zhou T, Liu Y, Dang P, Lu H, Shi H. Dental caries detection using score-based multi-input deep convolutional neural network. IEEE Access, v. 10, p. 18320-18329, 2022.
Jader G, Fontineli J, Ruiz M, Abdalla K, Pithon M, Oliveira L. Deep instance segmentation of teeth in panoramic X-Ray images. In: 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), 2018. p. 400-407.
Kabir T, Lee C, Chen L, Jiang X, Shams S. A comprehensive artificial intelligence framework for dental diagnosis and charting. BMC Oral Health, v.22, 2022.
Kim C, Kim D, Jeong H, Yoon SJ, Youm S. Automatic tooth detection and numbering using a combination of a CNN and heuristic algorithm. Applied Sciences, v. 10, n. 16, 2020. ISSN 2076-3417.
Koch TL, Perslev M, Igel C, Brandt SS. Accurate segmentation of dental panoramic radiographs with U-Nets. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). [S.l.: s.n.], 2019. p. 15-19.
Krizhevsky A, Sutskever I, Hinton G. Imagenet classification with deep convolutional neural networks. Neural Information Processing Systems, v. 25, 01 2012.
Krois, J.; Schneider, L.; Schwendicke, F. Impact of image context on Deep Learning for classification of teeth on radiographs. Journal of Clinical Medicine, v. 10, n. 8, 2021. ISSN 2077-0383.
Kumar, A.; Bhadauria, H. S.; Singh, A. descriptive analysis of dental X-Ray images using various practical methods: a review. PeerJ Computer Science, v. 7, 2021.
Kyventidis, N.; Angelopoulos, C. Intraoral radiograph anatomical region classification using Neural Networks. International Journal of Computer Assisted Ra- diology and Surgery, v. 16, 02 2021.
Lecun, Y.; Bengio, Y.; Hinton, G. Deep Learning. Nature, v. 521, p. 436-44, 05 2015.
Lee CT, Kabir T, Nelson J, Sheng S, Meng HW, Van Dyke TE, et al. Use of the deep learning approach to measure alveolar bone level. Journal of Clinical Periodontology. 2022;49(3):260–269.
Lee JH, Han SS, Kim YH, Lee C, Kim I. Application of a fully Deep Convolutional Neural Network to the automation of tooth segmentation on panoramic radiographs. Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology, v. 129, n. 6, p. 635-642, 2020. ISSN 2212-4403.
Lee JH, Kim DH, Jeong SN, Choi SH. Detection and diagnosis of dental caries using a Deep Learning-based Convolutional Neural Network algorithm. Journal of Dentistry, v. 77, p. 106-111, 2018. ISSN 0300-5712.
Lee JH, Kim Dh, Jeong SN, Choi SH. Diagnosis and prediction of periodontally compromised teeth using a Deep Learning-based Convolutional Neural Network algorithm. Journal of Periodontal and Implant Science, v. 48, p. 114, 04 2018.
Leite A, Gerven A, Willems H, Beznik T, Lahoud P, Gata-Araujo H, et al. Artificial intelligence-driven novel tool for tooth detection and segmentation on panoramic radiographs. Clinical Oral Investigations, v. 25, p. 1-11, 04 2021.
Li H, Zhou J, Zhou Y, Chen Q, She Y, Gao F, et al. An interpretable computer-aided diagnosis method for periodontitis from panoramic radiographs. Frontiers in Physiology, v. 12, 06 2021.
Li W, Zhu X, Wang X, Wang F, Liu J, Chen M, et al. Segmentation and accurate identification of large carious lesions on high quality x-ray images based on Attentional U-Net model. A proof of concept study. Journal of Applied Physics, v. 132, n. 3, p. 033103, 07 2022. ISSN 0021-8979. Disponível em: https://doi.org/10.1063/5.0084593.
Lian L, Zhu T, Zhu F, Zhu H. Deep Learning for caries detection and classification. Diagnostics, v. 11, n. 9, 2021. ISSN 2075-4418.
Lin, S.-Y.; Chang, H.-Y. Tooth numbering and condition recognition on dental panoramic radiograph images using CNNs. IEEE Access, PP, p. 1-1, 12 2021.
Mahdi, F. P.; Yagi, N.; Kobashi, S. Automatic teeth recognition in dental X-Ray images using transfer learning based faster R-CNN. In: 2020 IEEE 50th International Symposium on Multiple-Valued Logic (ISMVL), 2020. p. 16-21.
Mallya, S.; Ernest, L. White and Pharoah’s oral radiology: principles and interpretation. [S.l.]: Elsevier Health Sciences, 2018. ISBN 9780323543835.
Mohammad N, Muad A, Ahmad R, Mohd Yusof MYP. Reclassification of demirjian’s mandibular premolars staging for age estimation based on semi-automated segmentation of Deep Convolutional Neural Network. Forensic Imaging, v. 24, p. 200440, 02 2021.
Mohammad N, Muad A, Ahmad R, Mohd Yusof MYP. Accuracy of advanced Deep Learning with tensorflow and keras for classifying teeth developmental stages in digital panoramic imaging. BMC Medical Imaging, v. 22, 04 2022.
Mohammad-Rahimi H, Motamedian SR, Rohban MH, Krois J, Uribe SE, Mahmoudinia E, et al. Deep learning for caries detection: a systematic review. Journal of Dentistry, v. 122, p. 104115, 2022. ISSN 0300-5712.
Mongan J, Moy L, Kahn CE. Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiology: Artificial Intelligence, v. 2, n. 2, p. e200029, 2020. PMID: 33937821.
Moran M, Faria M, Giraldi G, Bastos L, Oliveira L, Conci A. Classification of approximal caries in bitewing radiographs using convolutional neural networks. Sensors, v. 21, 07 2021.
Motoki K, Mahdi FP, Yagi N, Nii M, Kobashi S. Automatic teeth recognition method from dental panoramic images using faster R-CNN and prior knowledge model. In: 2020 Joint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems (SCIS-ISIS). [S.l.: s.n.], 2020. p. 1-5.
Muramatsu C, Morishita T, Takahashi R, Hayashi T, Nishiyama W, Ariji Y, et al. Tooth detection and classification on panoramic radiographs for automatic dental chart filing: improved classification by multi-sized input data. Oral Radiology, v. 37, p. 1-7, 01 2020.
Muresan, M. P.; Barbura, A. R.; Nedevschi, S. Teeth detection and dental problem classification in panoramic X-Ray images using Deep Learning and image processing techniques. In: 2020 IEEE 16th International Conference on Intelligent Computer Communication and Processing (ICCP). [S.l.]: IEEE, 2020. (ICCP), p. 457-463.
Betul Oktay. Tooth detection with Convolutional Neural Networks. In: 2017 Medical Technologies National Congress (TIPTEKNO). [S.l.: s.n.], 2017. p. 1-4.
Oztekin F, Katar O, Sadak F, Aydogan M, Yildirim TT, Plawiak P, et al. Automatic semantic segmentation for dental restorations in panoramic radiography images using U-Net model. International Journal of Imag- ing Systems and Technology, v. 32, n. 6, p. 1990-2001, 2022. Disponível em: https: //onlinelibrary.wiley.com/doi/abs/10.1002/ima.22803.
Padalia, D.; Vora, K.; Sharma, D. An attention u-net for semantic segmentation of dental panoramic X-Ray images. In: 2022 5th International Conference on Advances in Science and Technology (ICAST). [S.l.: s.n.], 2022. p. 491-496.
Panetta K, Rajendran R, Ramesh A, Rao S. Tufts dental database: A multimodal panoramic X-Ray dataset for benchmarking diagnostic systems. IEEE Journal of Biomedical and Health Informatics, PP, p. 1-1, 10 2021.
Park J, Lee J, Moon S, Lee K. Deep Learning based detection of missing tooth regions for dental implant planning in panoramic radiographic images. Applied Sciences, v. 12, p. 1595, 02 2022.
Park W, Park JB. History and application of Artificial Neural Networks in dentistry. European Journal of Dentistry, v. 12, p. 594, 10 2018.
Pinheiro L, Silva B, Sobrinho B, Lima F, Cury P, Oliveira L. Numbering permanent and deciduous teeth via deep instance segmentation in panoramic X-Rays. In: RITTNER, L. et al. (Ed.). 17th International Symposium on Medical Information Processing and Analysis. [S.l.]: SPIE, 2021. v. 12088, p. 95 - 104.
Prados-Privado M, Villaln J, Torres A, Martnez-Martnez C, Ivorra C. A Convolutional Neural Network for automatic tooth numbering in panoramic images. BioMed Research International, v. 2021, p. 1-7, 12 2021.
Prados-Privado M, Villaln JG, Martnez-Martnez CH, Ivorra C. D. Dental images recognition technology and applications: a literature review. Applied Sciences, v. 10, n. 8, 2020. ISSN 2076-3417.
Prados-Privado M, Garca Villaln J, Blzquez Torres A, Martnez-Martnez CH, Ivorra C. A validation employing Convolutional Neural Network for the radiographic detection of absence or presence of teeth. Journal of Clinical Medicine, v. 10, n. 6, 2021. ISSN 2077-0383.
Prajapati, S. A.; Nagaraj, R.; Mitra, S. Classification of dental diseases using CNN and transfer learning. In: 2017 5th International Symposium on Computational and Business Intelligence (ISCBI). [S.l.: s.n.], 2017. p. 70-74.
Prince, S. J. Computer Vision: Models, Learning, a,n,d, Inference. [S.l.]: Cambridge University Press, 2012. 598 p. ISBN 1107011795.
Rashid U, Javid A, Khan AR, Liu L, Ahmed A, Khalid O, et al. A hybrid mask R-CNN-based tool to localize dental cavities from realtime mixed photographic images. PeerJ Computer Science, v. 8, p. e888, feb 2022. ISSN 2376-5992.
Rocha, E.; Endo, P. A comparative study of Deep Learning models for dental segmentation in panoramic radiograph. Applied Sciences, v. 12, p. 3103, 03 2022.
Rohrer C, Krois J, Patel J, Meyer-Lueckel H, Rodrigues JA, Schwendicke F. Segmentation of dental restorations on panoramic radiographs using Deep Learning. Diagnostics, v. 12, n. 6, 2022. ISSN 2075-4418. Disponível em: https://www.mdpi.eom/2075-4418/12/6/1316.
Salunke D, Mane D, Joshi R, Peddi P. Customized Convolutional Neural Network to detect dental caries from RadioVisioGraphy (RVG) images. Int. Journal of Advanced Technology and, Engineering Exploration, v. 9, p. 827-838, 2022.
Schneider L, Arsiwala-Scheppach L, Krois J, Meyer-Lueckel H, Bressem KK, Niehues SM, et al. Benchmarking Deep Learning models for tooth structure segmentation. Journal of Dental Research, 00220345221100169, 2022. Disponível em: https://doi.org/10.1177/00220345221100169.
Schneider L, Rischke R, Krois J, Krasowski A, Bttner M, Mohammad-Rahimi H, et al. Federated vs local vs central Deep Learning of tooth segmentation on panoramic radiographs. Journal of Dentistry, v. 135, p. 104556, 2023. ISSN 0300-5712.https://www.sciencedirect.com/science/paper/pii/S0300571223001422
Schwendicke, F.; Samek, W.; Krois, J. Artificial Intelligence in dentistry: chances and challenges. Journal of Dental Research, v. 99, n. 7, p. 769-774, 2020.
Schwendicke F, Singh T, Lee JH, Gaudin R, Chaurasia A, Wiegand T, et al. Artificial Intelligence in dental research: checklist for authors, reviewers, readers. Journal of Dentistry, v. 107, p. 103610, 2021. ISSN 03005712.
Sheng C, Wang L, Huang Z, Wang T, Guo Y, Hou W, et al. Transformer-based Deep Learning network for tooth segmentation on panoramic radiographs. Journal of systems science and complexity, p. 1—16, 2022. ISSN 1009-6124.
Silva B, Pinheiro L, Oliveira L, Pithon M. A study on tooth segmentation and numbering using end-to-end deep Neural Networks. In: 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), 2020. p. 164-171.
Silva, G.; Oliveira, L.; Pithon, M. Automatic segmentation teeth in X-Ray images: trends, a novel data set, benchmarking and future perspectives. Expert Systems with Applications, v. 107, p. 15-31, 2018. ISSN 0957-4174.
Singh, N. K.; Raza, K. Progress in Deep Learning-based dental and maxillofacial image analysis: A systematic review. Expert Systems with Applications, v. 199, p. 116968, 2022. ISSN 0957-4174.
Singh, P.; Sehgal, P. G.V Black dental caries classification and preparation technique using optimal CNN-LSTM classifier. Multimedia Tools and Applications, v. 80, p. 1-18, 02 2021.
Sivagami S, Chitra P, Kailash G, Muralidharan S. U-Net architecture based dental panoramic image segmentation. Int. Conference on Wireless Communications Signal Processing and Networking (WiSPNET), 2020. p.187-191.
Tuzoff D, Tuzova L, Bornstein M, Krasnov A, Kharchenko M, Nikolenko S, et al. Tooth detection and numbering in panoramic radiographs using Convolutional Neural Networks. Dentomaxillofacial Radiology, v. 48, n. 4, p. 20180051, 2019.
Uribe S, Sofi A, Raittio E, Maldupa I, Vilne B. Dental research data availability and quality according to the fair principles. J. of Dental Research, 2022.
Verma D, Puri S, Prabhu S, Smriti K. A. Anomaly detection in panoramic dental X-Rays using a hybrid Deep Learning and Machine Learning approach. In: 2020 IEEE REGION 10 CONFERENCE (TENCON). [S.l.: s.n.], 2020. p. 263-268.
Vinayahalingam S, shan Goey R, Kempers S, Schoep J, Cherici T, Moin DA, et al. Automated chart filing on panoramic radiographs using Deep Learning. Journal of Dentistry, v. 115, p. 103864, 2021. ISSN 0300-5712.
Wang CW, Huang CT, Lee JH, Li CH, Chang SW, Siao MJ, et al. A benchmark for comparison of dental radiography analysis algorithms. Medical Image Analysis, v. 31, p. 63-76, 2016. ISSN 1361-8415.
WHO. Health topics: Oral health. 2021. Disponível em: https://www.who.int/health-topics/oral-health
Wirtz, A.; Mirashi, S. G.; Wesarg, S. Automatic teeth segmentation in panoramic X-Ray images using a coupled shape model in combination with a Neural Network. In: FRANGI, A. F. et al. (Ed.). Medical Image Computing and Computer Assisted Intervention - MICCAI 2018. Cham: Springer International Publishing, 2018. p. 712-719. ISBN 978-3-030-00937-3.
Yaren Tekin B, Ozcan C, Pekince A, Yasa Y. An enhanced tooth segmentation and numbering according to FDI notation in bitewing radiographs. Computers in Biology and Medicine, v. 146, p. 105547, 2022. ISSN 0010-4825. Disponível em: https://www.sciencedirect.com/ science/paper/pii/S0010482522003390.
Yasa Y, elik , Bayrakdar I, Pekince A, Orhan K, Akarsu S, et al. An artificial intelligence proposal to automatic teeth detection and numbering in dental bitewing radiographs. Acta Odontologica Scandinavica, v. 79, p. 275-281, 2021.
ZHANG, K. et al. A relation-based framework for effective teeth recognition on dental periapical X-Rays. Computerized Medical Imaging and Graphics, v.95, p.102022, 2021.
Zhang K, Wu J, Chen H, Lyu P. An effective teeth recognition method using label tree with cascade network structure. Computerized Medical Imaging and Graphics, v. 68, 2018.
Zhao Y, Li P, Gao C, Liu Y, Chen Q, Yang F, et al. TSASNet: Tooth segmentation on dental panoramic X-Ray images by two-stage attention segmentation network. Knowledge-Based Systems, v. 206, p. 106338, 2020. ISSN 0950-7051.
Carneiro P, da Silva L, Silva L, de Melo N, Santo R. Classification of third molars and prevalence of impaction in panoramic radiographs: a literature review. Brazilian Journal of Health Review, v.7, n.1, p. 305-323, 2024. DOI:10.34119/bjhrv7n1-024
Goursand D, Magalhães B, Mendes, I, Maciel K, Santos V, Siqueira W. Injuries of permanent teeth in children and adolescents: a literature review. Brazilian Journal of Health Review,v.6,n.2,p.7273-7284, 2023 DOI:10.34119/bjhrv6n2-224
