International Journal of Clinical Pediatric Dentistry

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VOLUME 17 , ISSUE 11 ( November, 2024 ) > List of Articles

SCOPING REVIEW ARTICLE

Tech Bytes—Harnessing Artificial Intelligence for Pediatric Oral Health: A Scoping Review

Dhvani A Tanna, Srikala Bhandary, K Sundeep Hegde

Keywords : Artificial intelligence, Deep learning, Machine learning, Patient satisfaction

Citation Information : Tanna DA, Bhandary S, Hegde KS. Tech Bytes—Harnessing Artificial Intelligence for Pediatric Oral Health: A Scoping Review. Int J Clin Pediatr Dent 2024; 17 (11):1289-1295.

DOI: 10.5005/jp-journals-10005-2971

License: CC BY-NC 4.0

Published Online: 19-12-2024

Copyright Statement:  Copyright © 2024; The Author(s).


Abstract

Aim and background: The applications of artificial intelligence (AI) are escalating in all frontiers, specifically healthcare. It constitutes the umbrella term for a number of technologies that enable machines to independently solve problems they have not been programmed to address. With its aid, patient management, diagnostics, treatment planning, and interventions can be significantly improved. The aim of this review is to analyze the current data to assess the applications of artificial intelligence in pediatric dentistry and determine their clinical effectiveness. Materials and methods: A search of published studies in PubMed, Web of Science, Scopus, and Google Scholar databases was included till January 2024. Results: This review consisted of 30 published studies in the English language. The use of AI has been employed in the detection of dental caries, dental plaque, behavioral science, interceptive orthodontics, predicting the dental age, and identification of teeth which can enhance patient care. Conclusion: Artificial intelligence models can be used as an aid to the clinician as they are of significant help at individual and community levels in identifying an increased risk to dental diseases. Clinical significance: Artificial intelligence can be used as an asset in preventive school health programs, dental education for students and parents, and to assist the clinician in the dental practice. Further advancements in technology will give rise to newer potential innovations and applications.

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  1. McCarthy J, Minsky ML, Rochester N, et al. A proposal for the Dartmouth summer research project on artificial intelligence. 2006.
  2. Barr A, Feigenbaum EA, Cohen PR. The Handbook of Artificial Intelligence. HeurisTech Press; 1981; vol. 3.
  3. Doshi-Velez F, Kim B. Towards a rigorous science of interpretable machine learning. 2017; Available from: http://arxiv.org/abs/1702.08608.
  4. Abhinav GVKS, Naga Subrahmanyam S. Artificial intelligence in healthcare. J Drug Delivery Ther 2019;9(5-s):164–166.
  5. Chan HP, Samala RK, Hadjiiski LM, et al. Deep learning in medical image analysis. Adv Exp Med Biol 2020;3–21.
  6. Chen YW, Stanley K, Att W. Artificial intelligence in dentistry: current applications and future perspectives. Quintessence Int 2020;51(3):248–257.
  7. Chan H, Hadjiiski LM, Samala RK. Computer-aided diagnosis in the era of deep learning. Med Phys 2020;47(5):e218–e227.
  8. Manne R, Kantheti SC. Application of artificial intelligence in healthcare: chances and challenges. Curr J Appl Sci Technol 2021;24:78–89.
  9. Ledley RS, Lusted LB. Reasoning Foundations of Medical Diagnosis, New Series. 1959; vol. 130.
  10. Gunn AA. The diagnosis of acute abdominal pain with computer analysis. J R Coll Surg Edinb 1976;21(3):170–172.
  11. Khanagar SB, Al-Ehaideb A, Maganur PC, et al. Developments, application, and performance of artificial intelligence in dentistry—a systematic review. J Dent Sci 2021;16(1):508–522.
  12. Kılıc MC, Bayrakdar IS, Çelik Ö, et al. Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs. Dentomaxillofac Radiol 2021;50(6):20200172.
  13. Caliskan S, Tuloglu N, Celik O, et al. A pilot study of a deep learning approach to submerged primary tooth classification and detection. Int J Comput Dent 2021;24(1):1–9.
  14. Kim J, Hwang JJ, Jeong T, et al. Deep learning-based identification of mesiodens using automatic maxillary anterior region estimation in panoramic radiography of children. Dentomaxillofac Radiol 2022;51(7):20210528.
  15. Moghimi S, Talebi M, Parisay I. Design and implementation of a hybrid genetic algorithm and artificial neural network system for predicting the sizes of unerupted canines and premolars. Eur J Orthod 2012;34(4):480–486.
  16. Liu J, Liu Y, Li S, et al. Artificial intelligence-aided detection of ectopic eruption of maxillary first molars based on panoramic radiographs. J Dent 2022;125:104239.
  17. Zhu H, Yu H, Zhang F, et al. Automatic segmentation and detection of ectopic eruption of first permanent molars on panoramic radiographs based on nnU-Net. Int J Pediatr Dent 2022;32(6):785–792.
  18. Galibourg A, Cussat-Blanc S, Dumoncel J, et al. Comparison of different machine learning approaches to predict dental age using Demirjian's staging approach. Int J Legal Med 2021;135:665–675.
  19. Zaborowicz M, Zaborowicz K, Biedziak B, et al. Deep learning neural modelling as a precise method in the assessment of the chronological age of children and adolescents using tooth and bone parameters. Sensors 2022;22(2):637.
  20. Aljameel SS, Althumairy L, Albassam B, et al. Predictive artificial intelligence model for detecting dental age using panoramic radiograph images. Big Data Cogn Comput 2023;7(1):8.
  21. Kök H, Acilar AM, İzgi MS. Usage and comparison of artificial intelligence algorithms for determination of growth and development by cervical vertebrae stages in orthodontics. Prog Orthod 2019;20:1–10.
  22. Akçam MO, Takada K. Fuzzy modelling for selecting headgear types. Eur J Orthod 2002;24(1):99–106.
  23. Mario MC, Abe JM, Ortega NRS, et al. Paraconsistent artificial neural network as auxiliary in cephalometric diagnosis. Artif Organs 2010;34(7):E215–E221.
  24. Chung EJ, Yang BE, Park IY, et al. Effectiveness of cone-beam computed tomography-generated cephalograms using artificial intelligence cephalometric analysis. Sci Rep 2022;12(1):20585.
  25. You W, Hao A, Li S, et al. Deep learning-based dental plaque detection on primary teeth: a comparison with clinical assessments. BMC Oral Health 2020;20:1–7.
  26. Raksakmanut R, Thanyasrisung P, Sritangsirikul S, et al. Prediction of future caries in 1-year-old children via the salivary microbiome. J Dent Res 2023;102:626–635.
  27. Sadegh-Zadeh SA, Rahmani Qeranqayeh A, Benkhalifa E, et al. Dental caries risk assessment in children 5 years old and under via machine learning. Dent J (Basel) 2022;10(9):164.
  28. Zaorska K, Szczapa T, Borysewicz-Lewicka M, et al. Prediction of early childhood caries based on single nucleotide polymorphisms using neural networks. Genes (Basel) 2021;12(4):462.
  29. Ramos-Gomez F, Marcus M, Maida CA, et al. Using a machine learning algorithm to predict the likelihood of presence of dental caries among children aged 2 to 7. Dent J (Basel) 2021;9(12):141.
  30. Toledo Reyes L, Knorst JK, Ortiz FR, et al. Early childhood predictors for dental caries: a machine learning approach. J Dent Res 2023;102:999–1006.
  31. Alevizakos V, Bekes K, Steffen R, et al. Artificial intelligence system for training diagnosis and differentiation with molar incisor hypomineralization (MIH) and similar pathologies. Clin Oral Investig 2022;26:1–7.
  32. Park YH, Kim SH, Choi YY. Prediction models of early childhood caries based on machine learning algorithms. Int J Environ Res Public Health 2021;18(16):8613.
  33. Karhade DS, Roach J, Shrestha P, et al. An automated machine learning classifier for early childhood caries. Pediatr Dent 2021;43(3):191–197.
  34. Li RZ, Zhu JX, Wang YY, et al. Development of a deep learning based prototype artificial intelligence system for the detection of dental caries in children. Zhonghua Kou Qiang Yi Xue Za Zhi 2021;56(12):1253–1260.
  35. Goertzen E, Casas MJ, Barrett EJ, et al. Interactive computer-assisted learning as an educational method for learning pediatric interproximal dental caries identification. Oral Surg Oral Med Oral Pathol Oral Radiol 2023;136:371–381.
  36. Javed S, Zakirulla M, Baig RU, et al. Development of artificial neural network model for prediction of post-Streptococcus mutans in dental caries. Comput Methods Programs Biomed 2020;186:105198.
  37. Xiao J, Luo J, Ly-Mapes O, et al. Assessing a smartphone app (AICaries) that uses artificial intelligence to detect dental caries in children and provides interactive oral health education: protocol for a design and usability testing study. JMIR Res Protoc 2021;10(10):e32921.
  38. Ragodos R, Wang T, Padilla C, et al. Dental anomaly detection using intraoral photos via deep learning. Sci Rep 2022;12(1):11577.
  39. Kuwada C, Ariji Y, Kise Y, et al. Detection and classification of unilateral cleft alveolus with and without cleft palate on panoramic radiographs using a deep learning system. Sci Rep 2021;11(1):16044.
  40. Kuwada C, Ariji Y, Kise Y, et al. Deep-learning systems for diagnosing cleft palate on panoramic radiographs in patients with cleft alveolus. Oral Radiol 2023;39(2):349–354.
  41. Kasimoglu Y, Kocaaydin S, Karsli E, et al. Robotic approach to the reduction of dental anxiety in children. Acta Odontol Scand 2020;78(6):474–480.
  42. Mossey P, Catilla E. Global Registry and Database on Craniofacial Anomalies. 2003.
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