PERIODONTICS IN ARTIFICIAL INTELLIGENCE ERA : A LITERATURE REVIEW

Authors

  • Ni Wayan Arni Sardi Faculty of Dentistry, Universitas Mahasaraswati Denpasar
  • Ni Luh Putu Sri Maryuni Adnyasari Faculty of Dentistry, Universitas Mahasaraswati Denpasar
  • Made Talitha Suryaningsih Pinatih Faculty of Dentistry, Universitas Mahasaraswati Denpasar

DOI:

https://doi.org/10.46862/interdental.v19i2.7859

Keywords:

Artificial Intelligence, periodontics, periodontitis

Abstract

Introduction: Artificial intelligence (AI) involves the creation of computer systems that imitate human actions, and it is progressively adopted as a supportive tool in aiding clinicians with disease diagnosis and treatment. One prevalent global ailment is periodontitis, which leads to the degradation and loss of the tooth-supporting tissues. The aim of this  review is to evaluate existing literature that delineates the influence of AI on diagnosing and studying the prevalence of this condition.

Review: A Pubmed advanced search with narrative review was conducted of the past ten years using several search term such as “artificial Intelligences” and “periodontics”. Thorough searches were conducted on Pubmed in June 2023, encompassing studies where AI functioned as the independent variable for assessing, diagnosing, or treating patients with periodontitis. After eliminating duplicates, a total of 100 articles were recognized for preliminary abstract scrutiny. Of these, 76 documents were excluded, resulting in 24 texts for comprehensive evaluation.

Conclusion: The development of artificial intelligence in the field of dentistry requires more systematic reviews and meta-analyses to enhance the knowledge and scope of artificial intelligence applications. AI models for periodontal applications are still under development and in the future, they have the potential to support diagnostic accuracy capability.

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References

Mijwel MM. History of artificial intelligence. Comput Sci Coll Sci. 2015:1-6.

Schwendicke F, Samek W, Krois J. Artificial Intelligence in Dentistry: Chances and Challenges. J Dent Res. 2020;99(7):769-74.

Smith C, McGuire B, Huang T, & Yang G. The history of artificial intelligence, University of Washington. 2006. Available from: https://courses.cs.washington.edu/courses/csep590/06au/projects/history-ai.pdf. Accessed 1 Juni 2023

Sachdeva S, Mani A, Vora H, Saluja H, Mani S, Manka N. Artificial intelligence in periodontics: A dip in the future. J Cell Biotech. 2021;7(2): 119-124 3

Mahmood H, Shaban M, Indave BI, Santos-Silva AR, Rajpoot N, Khurram SA. Use of artificial intelligence in diagnosis of head and neck precancerous and cancerous lesions: A systematic review. Oral Oncol. 2020;110:104885.

Nagi R, Aravinda K, Rakesh N, Gupta R, Pal A, Mann AK. Clinical applications and performance of Intelligence systems in dental and maxillofacial radiology: A review. Imaging Sci Dent. 2020;50(2):81-92.

Choi EM, Park BY, Noh HJ. Efficacy of mobile health care in patients undergoing fixed orthodontic treatment: A systematic review. Int J Dent Hyg. 2021;19(1):29-38.

Maffulli N, Rodriguez HC, Stone IW, Nam A, Song A, Gupta M, Alvarado R, Ramon D, Gupta A. Artificial intelligence and machine learning in orthopedic surgery: a systematic review protocol. J Orthop Surg Res. 2020;15(1):478.

Indovina P, Barone D, Gallo L, Chirico A, De Pietro G, Giordano A. Virtual Reality as a Distraction Intervention to Relieve Pain and Distress During Medical Procedures: A Comprehensive Literature Review. Clin J Pain. 2018;34(9):858- 77.

Renton T. Dental (Odontogenic) Pain. Rev Pain. 2011;5(1):2-7.

Rahaman MM, Li C, Yao Y, Kulwa F, Rahman MA, Wang Q, Qi S, Kong F, Zhu X, Zhao X. Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches. J Xray Sci Technol. 2020;28(5):821-39.

Kim CH, Hahm MH, Lee DE, Choe JY, Ahn JY, Park SY, Lee SH, Kwak Y, Yoon SY, Kim KH, Kim M, Chang SH, Son J, Cho J, Park KS, Kim JK. Clinical usefulness of deep learning-based automated segmentation in intracra- nial hemorrhage. Technol Health Care. 2021 ;29(5):881-895

Guo LH, Wang D, Qian YY, Zheng X, Zhao CK, Li XL, Bo XW, Yue WW, Zhang Q, Shi J, Xu HX. A two-stage multi- view learning framework based computer-aided diagnosis of liver tumors with contrast enhanced ultrasound images. Clin Hemorheol Microcirc. 2018;69(3):343-54.

Akst J. A primer: Artificial Intelligence Versus Neural Networks. Inspiring Innovation: The Scientist Exploring Life. 2019;65802.

Rashmi JK, Amandeep S, Sangeetha R. Dentistry and Artificial Intelligence. Acta Scientific Dental Sciences. 2020;4(10):26-32.

Lee JH, Lee JS, Choi JK, Kweon HI, Kim YT, Choi SH. National dental policies and socio-demographic factors affecting changes in the incidence of periodontal treatments in Korean: A nationwide population-based retrospective cohort study from 2002-2013. BMC Oral Health. 2016;16(1):118.

Lee JH, Kim DH, Jeong SN, Choi SH. Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm. J Periodontal Implant Sci. 2018;48(2):114-23. doi: 10.5051/jpis.2018.48.2.114

Yauney G, Rana A, Wong LC, Javia P, Muftu A, Shah P. Automated Process Incorporating Machine Learning Segmenta- tion and Correlation of Oral Diseases with Systemic Health. Annu Int Conf IEEE Eng Med Biol Soc. 2019;2019:3387-93.

Papantonopoulos G, Takahashi K, Bountis T, Loos BG. Artificial neural networks for the diagnosis of aggressive periodontitis trained by immunologic parameters. PLoS One. 2014;9(3):e89757.

Krois J, Ekert T, Meinhold L, Golla T, Kharbot B, Wittemeier A, Do¨rfer C, Schwendicke F. Deep Learning for the Radiographic Detection of Periodontal Bone Loss. Sci Rep. 2019;9(1):8495.

Li S, Liu J, Zhou Z, Zhou Z, Wu X, Li Y, Wang S, Liao W, Ying S, Zhao Z. Artificial intelligence for caries and periapical periodontitis detection. J Dent. 2022 Jul;122:104107.

Revilla-León M, Gómez-Polo M, Barmak AB, Inam W, Kan JYK, Kois JC, Akal O. Artificial intelligence models for diagnosing gingivitis and periodontal disease: A systematic review. J Prosthet Dent. 2022 Mar 14:S0022-3913(22)00075-0.

Furman E, Jasinevicius TR, Bissada NF, Victoroff KZ, Skillicorn R, Buchner M. Virtual reality distraction for pain control during periodontal scaling and root planing procedures. J Am Dent Assoc. 2009;140(12):1508-16.

Sohmura T, Kusumoto N, Otani T, Yamada S, Wakabayashi K, Yatani H. CAD/CAM fabrication and clinical appli- cation of surgical template and bone model in oral implant surgery. Clin Oral Implants Res. 2009;20(1):87-93.

Shan T, Tay FR, Gu L. Application of Artificial Intelligence in Dentistry. J Dent Res. 2021;100(3):232-44. doi: 10.1177/0022034520969115. Epub 2020 Oct 29. PMID: 33118431.

Ozden FO, O¨ zgo¨nenel O, O¨ zden B, Aydogdu A. Diagnosis of periodontal diseases using different classification algo- rithms: a preliminary study. Niger J Clin Pract. 2015;18(3):416-21.

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Published

2023-12-23

How to Cite

1.
Sardi NWA, Adnyasari NLPSM, Pinatih MTS. PERIODONTICS IN ARTIFICIAL INTELLIGENCE ERA : A LITERATURE REVIEW. interdental [Internet]. 2023 Dec. 23 [cited 2024 Apr. 29];19(2):80-5. Available from: https://e-journal.unmas.ac.id/index.php/interdental/article/view/7859

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Vol. 19 No. 2 (2023): Interdental Jurnal Kedokteran Gigi (IJKG)

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Copyright (c) 2023 Ni Wayan Arni Sardi, Ni Luh Putu Sri Maryuni Adnyasari, Made Talitha Suryaningsih Pinatih

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