Artificial Intelligence in Dentistry: Literature Review
Iman Baig
Islamabad Medical and Dental College, Pakistan.
Saima Azam *
Islamabad Medical and Dental College, Pakistan.
Talha Bin Mushtaq
Islamabad Medical and Dental College, Pakistan.
*Author to whom correspondence should be addressed.
Abstract
Artificial Intelligence has exploded as a research subject in the 21st century as the hardware requirements of theoretical Artificial intelligence in the 19th and 20th century have translated into reality. This has led to rapid progress with the realization of multiple kinds of neural networks, as well as improvements along classical Machine Learning models like Decision Trees, Support Vector Machines etc. Most recently, the focus in Machine Learning has now shifted towards generative networks with Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) leading modern research topics.
With advent of the Internet of Medical Things (IoMT) and modern time leaps in the domain of Artificial Intelligence with the explosion of Machine and Deep Learning, Medical AI has grasped the central stage in modern research. Applications of Medical AI vary from detections of various cancerous tumors to prediction of arrhythmia attacks. The augmentation of such AI embedded techniques into the medical profession has streamlined analysis as well as aided professionals in reaching a more efficient and accurate diagnosis. This has also translated in the field of dentistry where strong deep learning architectures such as Convolutional neural networks in the form of Resnet, Inception, GoogleNet etc have been used to process raw images and detect a range of dental diseases.
This paper aims to provide an overview of progress made in the field of Machine Learning particularly focusing on its medical and dental applications. Different facets of Machine Learning are discussed with respect to their strengths, shortcomings, and the way artificial intelligence has been used to tackle problems in the medical field. Finally, a descriptive overview about state-of-the-art machine learning reliant applications that are being used in different dental subfields is discussed along with current challenges the industry faces today.
Keywords: Machine learning, dentistry, analog digital conversions, supervised learning
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References
Schwendicke F, Samek W, Krois J. Artificial intelligence in dentistry: Chances and challenges. J Dent Res. 2020;99(7):769-74.
Rodrigues JA, Krois J, Schwendicke F. Demystifying artificial intelligence and deep learning in dentistry. Braz Oral Res. 2021;35.
Schwendicke F, Golla T, Dreher M, Krois J. Convolutional neural networks for dental image diagnostics: A scoping review. J Dent. 2019(1879-176X (Electronic).
Lee DW, Kim SY, Jeong SN, Lee JH. Artificial intelligence in fractured dental implant detection and classification: Evaluation using dataset from two dental hospitals. Diagnostics. 2021;11(2):233.
Endres MG, Hillen F, Salloumis M, Sedaghat AR, Niehues SM, Quatela O, et al. Development of a deep learning algorithm for periapical disease detection in dental radiographs. Diagnostics. 2020;10(6):430.
Mertens S, Krois J, Cantu AG, Arsiwala LT, Schwendicke F. Artificial intelligence for caries detection: Randomized trial. J Dent. 2021;115:103849.
Yasaka K, Abe O. Deep learning and artificial intelligence in radiology: Current applications and future directions. PLoS Med. 2018;15(11):e1002707-e.
Nanayakkara S, Zhou X, Spallek H. Impact of big data on oral health outcomes. Oral Dis. 2019;25(5):1245-52.
Alauddin MS, Baharuddin AS, Mohd Ghazali MI. The modern and digital transformation of oral health care: A mini review. Healthcare. 2021;9(2):118.
Towers A, Field J, Stokes C, Maddock S, Martin N. A scoping review of the use and application of virtual reality in pre-clinical dental education. Br Dent J. 2019;226(5):358-66.
Israni ST, Verghese A. Humanizing Artificial Intelligence. JAMA. 2019; 321(1):29-30.
Topol E. Deep medicine: How artificial intelligence can make healthcare human again. New York Basic Books; 2019.
Tandon D, Rajawat J. Present and future of artificial intelligence in dentistry. J Oral Biol Craniofac Res. 2020;10(4):391-6.
Shan T, Tay FR, Gu L. Application of Artificial Intelligence in Dentistry. J Dent Res. 2021(1544-0591:232-44.
Shoukri B, Prieto JC, Ruellas A, Yatabe M, Sugai J, Styner M, et al. Minimally invasive approach for diagnosing TMJ osteoarthritis. J Dent Res. 2019;98(10): 1103-11.
Hung M, Voss MW, Rosales MN, Li W, Su W, Xu J, et al. Application of machine learning for diagnostic prediction of root caries. Gerodontology. 2019;36(4):395-404.
Mallishery S, Chhatpar P, Banga KS, Shah T, Gupta P. The precision of case difficulty and referral decisions: An innovative automated approach. Clin Oral Investig. 2020;24(6):1909-15.
Orhan K, Bayrakdar IS, Ezhov M, Kravtsov A, Özyürek T. Evaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans. Int Endod J. 2020;53(5):680-9.
Umer F. Could AI offer practical solutions for dentistry in the future? BDJ Team. 2022;9(2):26-8.
Pang L, Wang K, Tao Y, Zhi Q, Zhang J, Lin H. A new model for caries risk prediction in teenagers using a machine learning algorithm based on environmental and genetic factors. Front Genet. 2021;12.
Kunz F, Stellzig-Eisenhauer A, Zeman F, Boldt J. Artificial intelligence in orthodontics. J Orofac Orthop Fortschr Der Kieferorthopädie. 2020;81(1):52-68.
Takahashi T, Nozaki K, Gonda T, Ikebe K. A system for designing removable partial dentures using artificial intelligence. Part 1. Classification of partially edentulous arches using a convolutional neural network. J Prosthodont Res. 2021; 65(1):115-8.
Mangano F, Gandolfi A, Luongo G, Logozzo S. Intraoral scanners in dentistry: A review of the current literature. BMC Oral Health. 2017;17(1):149-.
Richert R, Goujat A, Venet L, Viguie G, Viennot S, Robinson P, et al. Intraoral scanner technologies: A review to make a successful impression. J Healthc Eng. 2017;2017:8427595
Khanna S, Dhaimade P. Artificial intelligence: Transforming dentistry today. IJBAMR. 2018;6(3):161-7.
Doméjean S, Banerjee A, Featherstone JDB. Caries risk/susceptibility assessment: Its value in minimum intervention oral healthcare. Br Dent J. 2017;223(3):191-7.
Walsh T. Fuzzy gold standards: Approaches to handling an imperfect reference standard. J Dent. 2018;74(1): S47-S9.
Gianfrancesco MA, Tamang S, Yazdany J, Schmajuk G. Potential biases in machine learning algorithms using electronic health record data. JAMA Intern Med. 2018; 178(11):1544-7.
Sunny S, Baby A, James BL, Balaji D, N V A, Rana MH, et al. A smart tele-cytology point-of-care platform for oral cancer screening. PLoS One. 2019;14(11): e0224885-e.
Umer F, Khan M. A call to action: Concerns related to artificial intelligence. Oral Surg Oral Med Oral Pathol Oral Radiol. 2021;132(2):255.