History and application of artificial neural networks in dentistry

ABSTRACT

Artificial intelligence (AI) is a commonly used term in daily life, and there are now two subconcepts that divide the entire range of meanings currently encompassed by the term. The coexistence of the concepts of strong and weak AI can be seen as a result of the recognition of the limits of mathematical and engineering concepts that have dominated the definition. This presentation reviewed the concept, history, and the current application of AI in daily life. Applications of AI are becoming a reality that is commonplace in all areas of modern human life. Efforts to develop robots controlled by AI have been continuously carried out to maximize human convenience. AI has also been applied in the medical decision-making process, and these AI systems can help nonspecialists to obtain expert-level information. Artificial neural networks are highly interconnected networks of computer processors inspired by biological nervous systems. These systems may help connect dental professionals all over the world. Currently, the use of AI is rapidly advancing beyond text-based, image-based dental practice. This presentation reviewed the history of artificial neural networks in the medical and dental fields, as well as current application in dentistry. As the use of AI in the entire medical field increases, the role of AI in dentistry will be greatly expanded. Currently, the use of AI is rapidly advancing beyond text-based, image-based dental practice. In addition to diagnosis of visually confirmed dental caries and impacted teeth, studies applying machine learning based on artificial neural networks to dental treatment through analysis of dental magnetic resonance imaging, computed tomography, and cephalometric radiography are actively underway, and some visible results are emerging at a rapid pace for commercialization.

Key words:

Artificial Intelligence - decision-making - dentistry - machine learning - neural networks

• REFERENCES

• 1 Legg S, Hutter M. Universal intelligence: A definition of machine intelligence. Minds Mach 2007; 17: 391-444
• 2 Albus JS. Outline for a theory of intelligence. IEEE Trans Sys Man Cybern 1991; 21: 473-509
• 3 Zackova E. Intelligence explosion quest for humankind. In: Romportl J, Zackova E, Kelemen J. editors Beyond Artificial Intelligence: The Disappearing Human-Machine Divide. Dordrecht: Springer; 2015: p. 34
• 4 Copeland BJ, Sylvan R. Beyond the universal Turing machine. Australas J Philos 1999; 77: 46-66
• 5 Pradhan T. Enhancement of Turing machine to universal Turing machine to halt for recursive enumerable language and its JFLAP simulation. Int J Hybrid Infor Tech 2015; 8: 193-202
• 6 Dreyfus H. The Myth of the Pervasiveness of the Mental. In: Schear J. editor Mind, Reason, and Being-in-the-World. New York: Routledge; 2013: p. 17-23
• 7 Weinbaum D, Veitas V. Open ended intelligence: the individuation of intelligent agents. J Exp Theor Artif Intell 2017; 29: 371-96
• 8 Kant I. Kritik der Reinen Vernunft. Hamburg: Felix Meiner; 1956: p. A76-80 B102-6
• 9 Floridi L. Philosophy and Computing: An Introduction. New York: Routledge; 1999: p. 148
• 10 Perlovsky LI. Neural mechanisms of the mind, aristotle, zadeh, and fMRI. IEEE Trans Neural Netw 2010; 21: 718-33
• 11 Nilsson N. The Quest for Artificial Intelligence. New York: Cambridge University Press; 2010
• 12 Uckelman SL. In: Computing with Concepts, Computing with Numbers: Llull, Leibniz, and Boole. Berlin: Springer; 2010: p. 427-37
• 13 Turing A. On computable numbers, with an application to the Entscheidungsproblem. Proc London Math Soc 1936; 42: 230-65
• 14 Turing A. Computing Machinery and Intelligence. Mind 1950; 49: 433-60
• 15 Kline R. Cybernetics, automata studies, and the Dartmouth conference on artificial intelligence. IEEE Ann History Computing 2011; 33: 5-16
• 16 McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity 1943. Bull Math Biol 1990; 52: 99-115
• 17 Poulton MM. A Brief History. Oxford: Elsevier Science; 2001: p. 10
• 18 Newell A, Simon HA. Computer science as empirical inquiry: Symbols and search. Commun ACM 1976; 19: 113-26
• 19 Bowling M, Fürnkranz J, Graepel T, Musick R. Machine learning and games. Mach Learn 2006; 63: 211-5
• 20 Dreyfus H. Alchemy and Artificial Intelligence. Santa Monica: Rand Corporation; 1965
• 21 Weizenbaum J. Computer Power and Human Reason: From Judgment to Calculation. New York: W.H. Freeman and Company; 1976
• 22 Weizenbaum J. ELIZA-a computer program for the study of natural language communication between man and machine. Commun ACM 1966; 9: 36-45
• 23 Ho Y, Bryson A, Baron S. Differential games and optimal pursuit-evasion strategies. IEEE Trans Autom Control 1965; 10: 385-9
• 24 Bryson A, Ho Y. Applied Optimal Control: Optimization, Estimation, and Control. New York: Taylor and Francis; 1975
• 25 Jackson P. Introduction to Expert Systems. London: Addison Wesley; 1986
• 26 Shortliffe EH, Buchanan BG. A model of inexact reasoning in medicine. Math Biosci 1975; 23: 351-79
• 27 Forbus KD. AI and cognitive science: The past and next 30 years. Top Cogn Sci 2010; 2: 345-56
• 28 Balint A, Belov A, Matti J, Sinz C. Overview and analysis of the SAT Challenge 2012 solver competition. Artif Intell 2015; 223: 120-55
• 29 Zinchenko K, Wu CY, Song KT. A study on speech recognition control for a surgical robot. IEEE Trans Indust Inform 2017; 13: 607-15
• 30 Dirican C. The impacts of robotics, artificial intelligence on business and economics. Proced Soc Behav Sci 2015; 195: 564-73
• 31 Alessandri E, Gasparetto A, Garcia RV, Béjar RM. An application of artificial intelligence to medical robotics. J Intell Robot Syst 2005; 41: 225-43
• 32 White SC. Decision-support systems in dentistry. J Dent Educ 1996; 60: 47-63
• 33 Steimann F. On the use and usefulness of fuzzy sets in medical AI. Artif Intell Med 2001; 21: 131-7
• 34 Ramesh AN, Kambhampati C, Monson JR, Drew PJ. Artificial intelligence in medicine. Ann R Coll Surg Engl 2004; 86: 334-8
• 35 Barbieri C, Molina M, Ponce P, Tothova M, Cattinelli I, Ion Titapiccolo J. et al. An international observational study suggests that artificial intelligence for clinical decision support optimizes anemia management in hemodialysis patients. Kidney Int 2016; 90: 422-9
• 36 Maes P. Artificial life meets entertainment: Lifelike autonomous agents. Commun ACM 1995; 38: 108-14
• 37 Wong BK, Monaco JA. Expert system applications in business: A review and analysis of the literature (1977 and ndash; 1993). Inf Manage 1995; 29: 141-52
• 38 Collen MF, Shortliffe EH. The Creation of a New Discipline. In: Collen MF, Ball MJ. editors The History of Medical Informatics in the United States. London: Springer London; 2015: p. 75-120
• 39 Best ML, Wade KW. The internet and democracy: Global catalyst or democratic dud?. B Sci Technol Soc 2009; 29: 255-71
• 40 Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S. et al. Artificial intelligence in healthcare: Past, present and future. Stroke Vasc Neurol 2017; 2: 230-43
• 41 Salari N, Shohaimi S, Najafi F, Nallappan M, Karishnarajah I. A novel hybrid classification model of genetic algorithms, modified k-nearest neighbor and developed backpropagation neural network. PLoS One 2014; 9: e112987
• 42 Zhu J, Wen D, Yu Y, Meudt HM, Nakhleh L. Bayesian inference of phylogenetic networks from bi-allelic genetic markers. PLoS Comput Biol 2018; 14: e1005932
• 43 Khanna S. Artificial intelligence: Contemporary applications and future compass. Int Dent J 2010; 60: 269-72
• 44 Kim EY, Lim KO, Rhee HS. Predictive modeling of dental pain using neural network. Stud Health Technol Inform 2009; 146: 745-6
• 45 Nieri M, Crescini A, Rotundo R, Baccetti T, Cortellini P, Prato GP. Factors affecting the clinical approach to impacted maxillary canines: A bayesian network analysis. Am J Orthod Dentofac Orthop 2010; 137: 755-62
• 46 Xie X, Wang L, Wang A. Artificial neural network modeling for deciding if extractions are necessary prior to orthodontic treatment. Angle Orthod 2010; 80: 262-6
• 47 Kakilehto T, Salo S, Larmas M. Data mining of clinical oral health documents for analysis of the longevity of different restorative materials in Finland. Int J Med Inform 2009; 78: e68-74
• 48 Jackson J. Data mining; A conceptual overview. Commun Assoc Infor Sys 2002; 8: 267-96
• 49 Korhonen M, Gundagar M, Suni J, Salo S, Larmas M. A practice-based study of the variation of diagnostics of dental caries in new and old patients of different ages. Caries Res 2009; 43: 339-44
• 50 Miladinović M, Mihailović B, Janković A, Tošić G, Mladenović D, Živković D. et al. Reasons for extraction obtained by artificial intelligence. Acta Fac Med Naissensis 2010; 27: 143-58