Artificial Intelligence: A neoteric reach in Periodontics
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Abstract
Periodontal disease diagnosis is the fundament point for the accurate treatment planning. Precise diagnosis requires experience and knowledge of the dentist, But it may vary from each dentist to the other dentist causing errors in diagnosis and treatment planning. To overcome these limitations, an emerging technology like Artificial Intelligence(AI) is of immense use in the field of periodontics. In this technology, an Artificial Intelligence driven machine can be utilized for performing the human tasks perfectly. This review is an attempt to describe various current concepts and future applications of AI in periodontology
References
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2. DeBowes LJ. The effects of dental disease on systemic disease. Vet Clin North Am Small AnimPract 1998;28(5):1057-1062
3. Scott, J.; Biancardi, A.M.;Jones, O.; Andrew, D. ArtificialIntelligence in Periodontology: AScoping Review. Dent. J. 2023, 11, 43.https://doi.org/10.3390/dj11020043
4. Sachdeva, Shivani & Mani, Amit & Vora, Hiral & Saluja, Harish & Mani, Shubhangi & Manka, Nishant. (2021). Artificial intelligence in periodontics – A dip in the future. Journal of Cellular Biotechnology. 7. 1-6. 10.3233/JCB-210041.
5. Ding H, Wu J, Zhao W, Matinlinna JP, Burrow MF and Tsoi JKH (2023) Artificialintelligence in dentistry—A review.Front. Dent. Med 4:1085251.doi: 10.3389/fdmed.2023.1085251
6. Ray S. A quick review of machine learning algorithms. International conference on machine learning, big data, cloud and parallel computing (COMITCon); 2019 14–16 Feb (2019).
7. Zhu X, Goldberg AB. Introduction to semi-supervised learning. Synth LectArtifIntell Mach Learn. (2009) 3(1):1–130. doi: 10.1007/978-3-031-01548-9
8. Liebowitz J. Expert systems: a short introduction. EngFract Mech. (1995) 50(5–6):601–7. doi: 10.1016/0013-7944(94)E0047-K
9. "Neural Networks and Deep Learning: A Textbook" by Charu C. Aggarwal.
10. Celi, L.A.; Cellini, J.; Charpignon, M.-L.; Dee, E.C.; Dernoncourt, F.; Eber, R.; Mitchell,W.G.; Moukheiber, L.; Schirmer, J.; Situ, J.;et al. Sources of bias in artificial intelligence that perpetuate healthcare disparities—A global review. PLOS Digit. Heal. 2022, 1,e0000022.
11. Erin A. Kierce, RDH, MS, MPH; and Robert J. Kolts, DDS, MBA.Improving Periodontal Disease Management With Artificial Intelligence
12. Kim E-H, Kim S, Kim H-J, Jeong H-o, Lee J, Jang J, et al. Prediction of chronic periodontitis severity using machine learning models based on salivary bacterial copynumber. Front Cell Infect. (2020) 10:698. doi: 10.3389/fcimb.2020.571515
13. Gockel T, Laupp U, Salb T, Burgert O, Dillmann R (2002) Interactive simulation of the teeth cleaning process using volumetric prototypes. In: MMVR 10, studies in health technology and informatics. IOS Press, Amsterdam, pp 160–165
14. Zanetti F, Zivkovic Semren T, Battey JND, Guy PA, Ivanov NV, van der Plas A, Hoeng J. A Literature Review and Framework Proposal for Halitosis Assessment in Cigarette Smokers and Alternative Nicotine-Delivery Products Users. Front Oral Health. 2021 Dec 10;2:777442. doi: 10.3389/froh.2021.777442. PMID: 35048075; PMCID: PMC8757736.
15. Nakhleh, M. K., Amal, H., Jeries, R., Broza, Y. Y., Aboud, M., Gharra, A. … Haick, H. (2017). Diagnosis and classification of 17 diseases from 1404 subjects via pattern analysis of exhaled molecules. ACS Nano, 11, 112– 125.
16. Karban, A., Nakhleh, M. K., Cancilla, J. C., Vishinkin, R., Rainis, T., Koifman, E. … Haick, H. (2016). Programmed nanoparticles for tailoring the detection of inflammatory bowel diseases and irritable bowel syndrome disease via breathprint. Advanced Healthcare Materials, 5, 2339– 2344
17. M Feres Y Louzoun S Haber M Faveri LC Figueiredo L Levin Support vector machine-based differentiation between aggressive and chronic periodontitis using microbial profilesInt Dent J2018681394610.1111/idj.12326
18. Rana A, Yauney G, Wong LC, Gupta O, Muftu A, Shah P,et al. Automated segmentation of gingival diseases from oralimages. In: 2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT). Bethesda, MD; 2018. p. 144 7.doi:10.1109/HIC.2017.8227605.
19. Vadzyuk, S ; Boliuk, Y; Luchynskyi, M.; Papinko, I; Vadzyuk, N. Prediction of the development of periodontal disease. Proc. Shevchenko Sci. Soc. Med. Sci. 2021, 65. Available online: https://mspsss.org.ua/index.php/journal/article/view/363
20. 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.
21. Krois J, Ekert T, Meinhold L, Golla T, Kharbot B, Wittemeier A, et al. Deep learning for the radiographic detection of periodontal bone loss. Scientific Rep. 2019;9:8495. doi:10.1038/s41598-019-44839-3.
22. Ramani S, Vijayalakshmi R, Mahendra J, Burnice Nalin Kumari C, Ravi N. Artificial intelligence in periodontics- An overview. IP Int J Periodontol Implantol 2023;8(2):71-74.
23. Chen YW, Stanley K, Att W. Artificial intelligence in dentistry: current applications and future perspective,Quintessence Int.2020;51(3):248–57. doi:10.3290/j.qi.a43952.
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Published
Aug 28, 2023
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Dr. Lavanya Boyeena , Dr. K. Phani Yasaswini , Boyeena Nitin Sagar, Dr. Akhila Mydukuru, Katuri Sreeja, Muthyala Rohith. (2023). Artificial Intelligence: A neoteric reach in Periodontics. Journal of Population Therapeutics and Clinical Pharmacology, 30(1), 407–414. Retrieved from https://jptcp.com/index.php/jptcp/article/view/2651
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