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VOLUME 24 , ISSUE 1 ( January, 2023 ) > List of Articles

REVIEW ARTICLE

Artificial Intelligence in Diagnosis of Oral Diseases: A Systematic Review

Shaul Hameed Kolarkodi, Khalid Zabin Alotaibi

Keywords : Artificial intelligence, Computer-assisted imaging, Oral diagnostic imaging

Citation Information : Kolarkodi SH, Alotaibi KZ. Artificial Intelligence in Diagnosis of Oral Diseases: A Systematic Review. J Contemp Dent Pract 2023; 24 (1):61-68.

DOI: 10.5005/jp-journals-10024-3465

License: CC BY-NC 4.0

Published Online: 04-05-2023

Copyright Statement:  Copyright © 2023; The Author(s).


Abstract

Aim: To understand the role of Artificial intelligence (AI) in oral radiology and its applications. Background: Over the last two decades, the field of AI has undergone phenomenal progression and expansion. Artificial intelligence applications have taken up new roles in dentistry like digitized data acquisition and machine learning and diagnostic applications. Materials and methods: All research papers outlining the population, intervention, control, and outcomes (PICO) questions were searched for in PubMed, ERIC, Embase, CINAHL, database from the last 10 years on first January 2023. Two authors independently reviewed the titles and abstracts of the selected studies, and any discrepancy between the two review authors was handled by a third reviewer. Two independent investigators evaluated all the included studies for the quality assessment using the modified tool for the quality assessment of diagnostic accuracy studies (QUADAS- 2). Review results: After the removal of duplicates and screening of titles and abstracts, 18 full texts were agreed upon for further evaluation, of which 14 that met the inclusion criteria were included in this review. The application of artificial intelligence models has primarily been reported on osteoporosis diagnosis, classification/segmentation of maxillofacial cysts and/or tumors, and alveolar bone resorption. Overall study quality was deemed to be high for two (14%) studies, moderate for six (43%) studies, and low for another six (43%) studies. Conclusion: The use of AI for patient diagnosis and clinical decision-making can be accomplished with relative ease, and the technology should be regarded as a reliable modality for potential future applications in oral diagnosis.

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  1. Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng 2018;2(10):719–731. DOI: 10.1038/s41551-018-0305-z.
  2. Lee JH, Kim DH, Jeong SN, et al. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J Dent 2018;77:106–111. DOI: 10.1016/j.jdent.2018.07.015.
  3. Hung M, Voss MW, Rosales MN, et al. Application of machine learning for diagnostic prediction of root caries. Gerodontology 2019;36(4):395–404. DOI: 10.1111/ger.12432.
  4. Xu X, Liu C, Zheng Y. 3D tooth segmentation and labeling using deep convolutional neural networks. IEEE Transactions on Visualization and Computer Graphics 2018;25(7):2336–2348. DOI: 10.1109/TVCG.2018.2839685.
  5. Tian S, Dai N, Zhang B, et al. Automatic classification and segmentation of teeth on 3D dental model using hierarchical deep learning networks. In IEEE Access 2019;7:84817–84828. DOI: 10.1109/ACCESS.2019.2924262.
  6. Hatvani J, Horváth A, Michetti J, et al. Deep learning-based super- resolution applied to dental computed tomography. IEEE Transactions on Radiation and Plasma Medical Sciences 2018;3(2): 120–128. DOI: 10.1109/TRPMS.2018.2827239.
  7. Thrall JH, Li X, Li Q, et al. Artificial intelligence and machine learning in radiology: Opportunities, challenges, pitfalls, and criteria for success. J Am Coll Radiol 2018;15 (Part 3B):504–508. DOI: 10.1016/j.jacr.2017.12.026.
  8. Hosny A, Parmar C, Quackenbush J, et al. Artificial intelligence in radiology. Nat Rev Cancer 2018;18(8):500–510. DOI: 10.1038/s41568-018-0016-5.
  9. Joda T, Yeung AWK, Hung K, et al. Disruptive innovation in dentistry: what it is and what could be next. J Dent Res 2021;100(5):448–453. DOI: 10.1177/0022034520978774.
  10. Whiting PF, Rutjes AWS, Westwood M, et al. QUADAS-2: A revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 2011;155(8):529–536. DOI: 10.7326/0003-4819-155-8-201110180-00009.
  11. Lin PL, Huang PY, Huang PW. Automatic methods for alveolar bone loss degree measurement in periodontitis periapical radiographs. Comput Methods Programs Biomed 2017;148:1–11. DOI: 10.1016/j.cmpb.2017.06.012.
  12. Lin PL, Huang PW, Huang PY, et al. Alveolar bone-loss area localization in periodontitis radiographs based on threshold segmentation with a hybrid feature fused of intensity and the H-value of fractional Brownian motion model. Comput Methods Programs Biomed 2015;121(3):117–126. DOI: 10.1016/j.cmpb.2015.05.004.
  13. Lee JH, Kim DH, Jeong SN, et al. Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm. J Periodontal Implant Sci 2018;48(2):114–123. DOI: 10.5051/jpis.2018.48.2.114.
  14. Kavitha MS, Ganesh Kumar P, Park SY, et al. Automatic detection of osteoporosis based on hybrid genetic Swarm fuzzy classifier approaches. Dentomaxillofac Radiol 2016;45(7):20160076. DOI: 10.1259/dmfr.20160076.
  15. Kavitha MS, An SY, An CH, et al. Texture analysis of mandibular cortical bone on digital dental panoramic radiographs for the diagnosis of osteoporosis in Korean women. Oral Surg Oral Med Oral Pathol Oral Radiol 2015;119(3):346–356. DOI: 10.1016/j.oooo.2014.11.009.
  16. Ohashi Y, Ariji Y, Katsumata A, et al. Utilization of computer-aided detection system in diagnosing unilateral maxillary sinusitis on panoramic radiographs. Dentomaxillofac Radiol 2016;45(3):20150419. DOI: 10.1259/dmfr.20150419.
  17. Hwang JJ, Lee JH, Han SS, et al. Strut analysis for osteoporosis detection model using dental panoramic radiography. Dentomaxillofac Radiol 2017;46(7):20170006. DOI: 10.1259/dmfr.20170006.
  18. Son LH, Tuan TM, Fujita H, et al. Dental diagnosis from X-ray images: An expert system based on fuzzy computing. Biomed Signal Process Control 2018;39:64–73. DOI: https://doi.org/10.1016/j.bspc.2017.07.005.
  19. Yilmaz E, Kayikcioglu T, Kayipmaz S. Computer-Aided diagnosis of periapical cyst and keratocystic odontogenic tumor on cone beam computed tomography. Comput Methods Programs Biomed 2017;146:91–100. DOI: 10.1016/j.cmpb.2017.05.012.
  20. Abdolali F, Zoroofi RA, Otake Y, et al. Automated classification of maxillofacial cysts in cone beam CT images using contourlet transformation and spherical harmonics. Comput Methods Programs Biomed 2017;139:197–207. DOI: 10.1016/j.cmpb.2016.10.024.
  21. Abdolali F, Zoroofi RA, Otake Y, et al. Automatic segmentation of maxillofacial cysts in cone beam CT images. Comput Biol Med 2016;72:108–119. DOI: 10.1016/j.compbiomed.2016.03.014.
  22. Ngan TT, Tuan TM, Son LH, et al. Decision making based on fuzzy aggregation operators for medical diagnosis from dental X-ray images. J Med Syst 2016;40(12):280. DOI: 10.1007/s10916-016-0634-y.
  23. Rana M, Modrow D, Keuchel J, et al. Development and evaluation of an automatic tumor segmentation tool: A comparison between automatic, semi-automatic and manual segmentation of mandibular odontogenic cysts and tumors. J Craniomaxillofac Surg 2015;43(3): 355–359. DOI: 10.1016/j.jcms.2014.12.005.
  24. de Medeiros FCFL, Kudo GAH, Leme BG, et al. Dental implants in patients with osteoporosis: A systematic review with meta-analysis. Int J Oral Maxillofac Surg 2018;47(4):480–491. DOI: 10.1016/j.ijom.2017.05.021.
  25. Aghaloo T, Pi-Anfruns J, Moshaverinia A, et al. The effects of systemic diseases and medications on implant osseointegration: A systematic review. Int J Oral Maxillofac Implants 2019;34:s35–s49. DOI: 10.11607/jomi.19suppl.g3.
  26. Taguchi A, Tsuda M, Ohtsuka M, et al. Use of dental panoramic radiographs in identifying younger postmenopausal women with osteoporosis. Osteoporos Int 2006;17(3):387–394. DOI: 10.1007/s00198-005-2029-7.
  27. Vlasiadis KZ, Damilakis J, Velegrakis GA, et al. Relationship between BMD, dental panoramic radiographic findings and biochemical markers of bone turnover in diagnosis of osteoporosis. Maturitas 2008;59(3):226–233. DOI: 10.1016/j.maturitas.2008.01.006.
  28. Devito KL, de Souza Barbosa F, Felippe Filho WN. An artificial multilayer perceptron neural network for diagnosis of proximal dental caries. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 2008;106(6):879–884. DOI: 10.1016/j.tripleo.2008.03.002.
  29. Firestone AR, Sema D, Heaven TJ, et al. The effect of a knowledge-based, image analysis and clinical decision support system on observer performance in the diagnosis of approximal caries from radiographic images. Caries Res 1998;32(2):127–134. DOI: 10.1159/000016442.
  30. Park SH, Han K. Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction. Radiology 2018;286:800–809.
  31. Khanagar SB, Al-ehaideb A, Maganur PC, et al. Developments, application and performance of artificial intelligence in dentistry – A systematic review. Journal of Dental Sciences 2021;16(1):508–522. DOI: 10.1016/j.jds.2020.06.019.
  32. Steyerberg EW. Validation of prediction models. In: Steyerberg EW (Ed). Clinical prediction models: A practical approach to development, validation, and updating. New York: Springer; 2010. pp.299–310.
  33. Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal 2017;42:60–88. DOI: 10.1016/j.media.2017.07.005.
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