Artificial Intelligence In Restorative Dentistry And Endodontics- A Short Review Artificial Intelligence In Restorative Dentistry And Endodontics Section Review Article
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Abstract
Artificial intelligence is a branch of computer science that was described by John McCarthy in the year 1956. It has the potential to replicate human intelligence and has been described as the fourth industrial revolution. In health care two types of artificial intelligence methods are used, namely virtual and physical. Different methods have been employed to train the artificial intelligence system. In restorative dentistry they have been used for the detection of dental caries, vertical root fracture, predict restoration failures, locate preparation margins etc. In endodontics they have proved to be helpful in detection of periapical lesion, crown and root fracture, working length determination and also throw light on retreatment predictions and the viability of stem cells etc.So this article provides an overview of the role of artificial intelligence in restorative dentistry and endodontics and its scope for the future.
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References
- Aminoshariae A, Kulild J, Nagendrababu V. Artificial intelligence in endodontics: current applications and future directions. Journal of Endodontics. 2021 Sep 1;47(9):1352-7.
- Revilla-León M, Gómez-Polo M, Vyas S, Barmak AB, Özcan M, Att W, Krishnamurthy VR. Artificial intelligence applications in restorative dentistry: A systematic review. The Journal of prosthetic dentistry. 2021 Apr 9.
- Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017 Apr 1;69:S36-40.
- Huang S, Yang J, Fong S, Zhao Q. Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges. Cancer letters. 2020 Feb 28;471:61-71.
- Bhandari M, Zeffiro T, Reddiboina M. Artificial intelligence and robotic surgery: current perspective and future directions. Current opinion in urology. 2020 Jan 1;30(1):48-54.
- LeCun Y, Bengio Y, Hinton G. Deep learning. nature. 2015 May;521(7553):436-44.
- Barmak BA, Galluci GO, Att W, Dent M. Artificial intelligence applications in implant dentistry: A systematic review.
- Alpaydin E. Introduction to machine learning. 4th ed. Cambridge, MA: Massachusetts Institute of Technology; 2020. p. 23-491.
- Craw S, Wiratunga N, Rowe RC. Learning adaptation knowledge to improve case-based reasoning. Artificial intelligence. 2006 Nov 1; 170(16-17):1175-92.
- Thukral S, Bal JS. Medical applications on fuzzy logic inference system: a review. International Journal of Advanced Networking and Applications. 2019;10(4):3944-50.
- Tuan TM, Duc NT, Van Hai P. Dental diagnosis from X-ray images using fuzzy rule-based systems. International Journal of Fuzzy System Applications (IJFSA). 2017 Jan 1;6(1):1-6.
- Ossowska A, Kusiak A, ?wietlik D. Artificial intelligence in dentistry—Narrative review. International journal of environmental research and public health. 2022 Mar 15;19(6):3449.
- Khanagar SB, Al-Ehaideb A, Maganur PC, Vishwanathaiah S, Patil S, Baeshen HA, Sarode SC, Bhandi S. Developments, application, and performance of artificial intelligence in dentistry–A systematic review. Journal of dental sciences. 2021 Jan 1;16(1):508-22.
- Mupparapu M, Wu CW, Chen YC. Artificial intelligence, machine learning, neural networks, and deep learning: Futuristic concepts for new dental diagnosis. Quintessence international (Berlin, Germany: 1985). 2018 Jan 1;49(9):687-8.
- Javed S, Zakirulla M, Baig RU, Asif SM, Meer AB. Development of artificial neural network model for prediction of post-streptococcus mutans in dental caries. Computer Methods and Programs in Biomedicine. 2020 Apr 1;186:105198.
- Meghil MM, Rajpurohit P, Awad ME, McKee J, Shahoumi LA, Ghaly M. Artificial intelligence in dentistry. Dentistry Review. 2022 Jan 3:100009.
- Nguyen TT, Larrivée N, Lee A, Bilaniuk O, Durand R. Use of artificial intelligence in dentistry. Current clinical trends and research advances. J Can Dent Assoc. 2021;87(l7):1488-2159.
- Kusy M, Kluska J. Probabilistic neural network structure reduction for medical data classification. InArtificial Intelligence and Soft Computing: 12th International Conference, ICAISC 2013, Zakopane, Poland, June 9-13, 2013, Proceedings, Part I 12 2013 (pp. 118-129). Springer Berlin Heidelberg.
- Othman MF, Basri MA. Probabilistic neural network for brain tumor classification. In2011 Second International Conference on Intelligent Systems, Modelling and Simulation 2011 Jan 25 (pp. 136-138). IEEE.
- 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. 2018 Oct 1; 77:106-11.
- Fukuda M, Inamoto K, Shibata N, Ariji Y, Yanashita Y, Kutsuna S, Nakata K, Katsumata A, Fujita H, Ariji E. Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography. Oral Radiology. 2020 Oct;36:337-43.
- Aliaga IJ, Vera V, De Paz JF, García AE, Mohamad MS. Modelling the longevity of dental restorations by means of a CBR system. BioMed Research International. 2015 Mar 19;2015.
- Saghiri MA, Garcia-Godoy F, Gutmann JL, Lotfi M, Asgar K. The reliability of artificial neural network in locating minor apical foramen: a cadaver study. Journal of Endodontics. 2012 Aug 1;38(8):1130-4.
- Lahoud P, EzEldeen M, Beznik T, et al. Artificial intelligence for fast and accurate 3-dimensionaln tooth segmentation on cone-beam computed tomography. J Endod 2021;47:827–35
- Bindal P, Bindal U, Lin CW, Kasim NH, Ramasamy TS, Dabbagh A, Salwana E, Shamshirband S. Neuro-fuzzy method for predicting the viability of stem cells treated at different time-concentration conditions. Technology and Health Care. 2017 Jan 1;25(6):1041-51.