Abstract
Artificial intelligence (AI) has made it possible for machines to perform human tasks. Healthcare is one of the main fields where AI and machine learning have been successfully employed. There have been many articles presenting the various applications of AI and its favorable outcomes in dentistry and maxillofacial surgery. It may be difficult for dental researchers to understand and interpret these studies since they are different in methodology. In addition, they are unfamiliar with the definitions and terminology used in these articles. The purpose of this chapter is to provide an explanation of the terms and concepts frequently used in AI articles and, specifically, in the following two chapters where we discussed its current application in maxillofacial surgery and its future.
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Notes
- 1.
In the following sections of this chapter, everywhere we used the term “machine learning”; it also includes “deep learning” approaches.
References
Hamblin MR. Shining light on the head: photobiomodulation for brain disorders. BBA Clin. 2016;6:113–24.
Chartrand G, Cheng PM, Vorontsov E, Drozdzal M, Turcotte S, Pal CJ, et al. Deep learning: a primer for radiologists. Radiographics. 2017;37(7):2113–31.
Wang S, Summers RM. Machine learning and radiology. Med Image Anal. 2012;16(5):933–51.
Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Science. 2015;349(6245):255–60.
Kohli M, Prevedello LM, Filice RW, Geis JR. Implementing machine learning in radiology practice and research. Am J Roentgenol. 2017;208(4):754–60.
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44.
Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60–88.
Ding Y, Sohn JH, Kawczynski MG, Trivedi H, Harnish R, Jenkins NW, et al. A deep learning model to predict a diagnosis of Alzheimer disease by using 18F-FDG PET of the brain. Radiology. 2018;290(2):456–64.
Parakh A, Lee H, Lee JH, Eisner BH, Sahani DV, Do S. Urinary stone detection on CT images using deep convolutional neural networks: evaluation of model performance and generalization. Radiol Artif Intell. 2019;1(4):e180066.
Vos BD, Wolterink JM, Leiner T, Jong PA, Lessmann N, Išgum I. Direct automatic coronary calcium scoring in cardiac and chest CT. IEEE Trans Med Imaging. 2019;38(9):2127–38.
Schelb P, Kohl S, Radtke JP, Wiesenfarth M, Kickingereder P, Bickelhaupt S, et al. Classification of cancer at prostate MRI: deep learning versus clinical PI-RADS assessment. Radiology. 2019;293(3):607–17.
Lehman CD, Yala A, Schuster T, Dontchos B, Bahl M, Swanson K, et al. Mammographic breast density assessment using deep learning: clinical implementation. Radiology. 2018;290(1):52–8.
Mohammad-Rahimi H, Nadimi M, Ghalyanchi-Langeroudi A, Taheri M, Ghafouri-Fard S. Application of machine learning in diagnosis of COVID-19 through X-ray and CT images: a scoping review. Front Cardiovasc Med. 2021;8(185):638011.
Ngo TA, Lu Z, Carneiro G. Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance. Med Image Anal. 2017;35:159–71.
Chang AC. Chapter 2 - History of artificial intelligence. In: Chang AC, editor. Intelligence-based medicine. London: Academic Press; 2020. p. 23–7.
Turing AM. On computable numbers, with an application to the Entscheidungs problem. Proc Lond Math Soc. 1937;2(1):230–65.
Turing AM. Computing machinery and intelligence. Parsing the Turing test. New York: Springer; 2009. p. 23–65.
Taulli T, Oni M. Artificial intelligence basics. New York: Springer; 2019.
Luger GF. Artificial intelligence: structures and strategies for complex problem solving. Pearson education; 2005.
Agah A. Introduction to medical applications of artificial intelligence. Medical Applications of Artificial Intelligence. 2013;19–26.
McCarthy J, Minsky ML, Rochester N, Shannon CE. A proposal for the Dartmouth summer research project on artificial intelligence. 1955. http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html.
Russell S, Norvig P. Artificial intelligence: a modern approach; 2002.
Matheny M, Israni ST, Ahmed M, Whicher D. Artificial intelligence in health care: the hope, the hype, the promise, the peril. NAM Special Publication. Washington, DC: National Academy of Medicine; 2019. p. 154.
van Melle W. MYCIN: a knowledge-based consultation program for infectious disease diagnosis. Int J Man Mach Stud. 1978;10(3):313–22.
Zhou L, Sordo M. Chapter 5 - Expert systems in medicine. In: Xing L, Giger ML, Min JK, editors. Artificial intelligence in medicine. London: Academic Press; 2021. p. 75–100.
Ren R, Luo H, Su C, Yao Y, Liao W. Machine learning in dental, oral and craniofacial imaging: a review of recent progress. PeerJ. 2021;9:e11451.
Grosan C, Abraham A. Intelligent systems. Cham: Springer; 2011.
Baxt WG. Use of an artificial neural network for the diagnosis of myocardial infarction. Ann Intern Med. 1991;115(11):843–8.
Mohammad-Rahimi H, Nadimi M, Rohban MH, Shamsoddin E, Lee VY, Motamedian SR. Machine learning and orthodontics, current trends and the future opportunities: a scoping review. Am J Orthod Dentofacial Orthop. 2021;160(2):170–92.e4.
Khanna SS, Dhaimade PA. Artificial intelligence: transforming dentistry today. Indian J Basic Appl Med Res. 2017;6(3):161–7.
Stheeman SE, van der Stelt PF, Mileman PA. Expert systems in dentistry. Past performance--future prospects. J Dent. 1992;20(2):68–73.
White SC. Computer-aided differential diagnosis of oral radiographic lesions. Dentomaxillofac Radiol. 1989;18(2):53–9.
Hyman JJ, Diehl MC. A dental trauma diagnostic program. Proc Annu Symp Comput Appl Med Care. 1983:133–4.
Hyman JJ, Doblecki W. Computerized endodontic diagnosis. J Am Dent Assoc. 1983;107(5):755–8.
Sims-Williams JH, Brown ID, Matthewman A, Stephens CD. A computer-controlled expert system for orthodontic advice. Br Dent J. 1987;163(5):161–6.
Abbey LM. An expert system for oral diagnosis. J Dent Educ. 1987;51(8):475–80.
Bayaraa T, Hyun CM, Jang TJ, Lee SM, Seo JK. A two-stage approach for beam hardening artifact reduction in low-dose dental CBCT. IEEE Access. 2020;8:225981–94.
Lee J-H, Kim D-h, Jeong S-N, Choi S-H. 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.
Mohammad-Rahimi H, Motamadian SR, Nadimi M, Hassanzadeh-Samani S, Minabi MAS, Mahmoudinia E, et al. Deep learning for the classification of cervical maturation degree and pubertal growth spurts: a pilot study. Korean J Orthod. 2022;52(2):112–22.
Mohammad-Rahimi H, Motamedian SR, Rohban MH, Krois J, Uribe S, Mahmoudinia E, et al. Deep learning for caries detection: a systematic review: DL for caries detection. J Dent. 2022;122:104115.
Saghiri MA, Asgar K, Boukani KK, Lotfi M, Aghili H, Delvarani A, et al. A new approach for locating the minor apical foramen using an artificial neural network. Int Endod J. 2012;45(3):257–65.
Kim DW, Kim H, Nam W, Kim HJ, Cha IH. Machine learning to predict the occurrence of bisphosphonate-related osteonecrosis of the jaw associated with dental extraction: a preliminary report. Bone. 2018;116:207–14.
Aliaga I, Vera V, Vera M, García E, Pedrera M, Pajares G. Automatic computation of mandibular indices in dental panoramic radiographs for early osteoporosis detection. Artif Intell Med. 2020;103:101816.
Lee KS, Kwak HJ, Oh JM, Jha N, Kim YJ, Kim W, et al. Automated detection of TMJ osteoarthritis based on artificial intelligence. J Dent Res. 2020;99(12):1363–7.
Setzer FC, Shi KJ, Zhang Z, Yan H, Yoon H, Mupparapu M, et al. Artificial intelligence for the computer-aided detection of periapical lesions in cone-beam computed tomographic images. J Endod. 2020;46(7):987–93.
Alabi RO, Elmusrati M, Sawazaki-Calone I, Kowalski LP, Haglund C, Coletta RD, et al. Machine learning application for prediction of locoregional recurrences in early oral tongue cancer: a Web-based prognostic tool. Virchows Arch. 2019;475(4):489–97.
Alhazmi A, Alhazmi Y, Makrami A, Masmali A, Salawi N, Masmali K, et al. Application of artificial intelligence and machine learning for prediction of oral cancer risk. J Oral Pathol Med. 2021;50(5):444–50.
Xie X, Wang L, Wang A. Artificial neural network modeling for deciding if extractions are necessary prior to orthodontic treatment. Angle Orthod. 2010;80(2):262–6.
Murphy KP. Machine learning: a probabilistic perspective. Cambridge: MIT Press; 2012.
Ho T. Random decision forests. In: International conference on document analysis and recognition, Montreal; 1995.
Prinzie A, Van den Poel D, editors. Random multiclass classification: generalizing random forests to random MNL and random NB. In: International conference on database and expert systems applications. Cham: Springer; 2007.
Chilamkurthy S, Ghosh R, Tanamala S, Biviji M, Campeau NG, Venugopal VK, et al. Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. Lancet. 2018;392(10162):2388–96.
Lindsey R, Daluiski A, Chopra S, Lachapelle A, Mozer M, Sicular S, et al. Deep neural network improves fracture detection by clinicians. Proc Natl Acad Sci. 2018;115(45):11591–6.
Erickson BJ, Korfiatis P, Akkus Z, Kline TL. Machine learning for medical imaging. Radiographics. 2017;37(2):505–15.
Hastie T, Tibshirani R, Friedman J. Statistical learning: data mining, inference, and prediction. Heidelberg: Springer; 2009.
Silver D, Huang A, Maddison CJ, Guez A, Sifre L, Van Den Driessche G, et al. Mastering the game of Go with deep neural networks and tree search. Nature. 2016;529(7587):484–9.
Kidoh M, Shinoda K, Kitajima M, Isogawa K, Nambu M, Uetani H, et al. Deep learning based noise reduction for brain MR imaging: tests on phantoms and healthy volunteers. Magn Reson Med Sci. 2020;19(3):195.
Rathi VGP, Palani S. Brain tumor detection and classification using deep learning classifier on MRI images. Res J Appl Sci Eng Technol. 2015;10(2):177–87.
Yang S, Kweon J, Roh J-H, Lee J-H, Kang H, Park L-J, et al. Deep learning segmentation of major vessels in X-ray coronary angiography. Sci Rep. 2019;9(1):16897.
Rostami B, Anisuzzaman DM, Wang C, Gopalakrishnan S, Niezgoda J, Yu Z. Multiclass wound image classification using an ensemble deep CNN-based classifier. Comput Biol Med. 2021;134:104536.
Sakellaropoulos T, Vougas K, Narang S, Koinis F, Kotsinas A, Polyzos A, et al. A deep learning framework for predicting response to therapy in cancer. Cell Rep. 2019;29(11):3367–73.e4.
Harvey H, Glocker B. A standardised approach for preparing imaging data for machine learning tasks in radiology. In: Ranschaert ER, Morozov S, Algra PR, editors. Artificial intelligence in medical imaging: opportunities, applications and risks. Cham: Springer; 2019. p. 61–72.
Gebru T, Morgenstern J, Vecchione B, Vaughan JW, Wallach H, Iii HD, Crawford K. Datasheets for datasets. Communications of the ACM. 2021;64(12):86–92.
Nelson GS. Practical implications of sharing data: a primer on data privacy, anonymization, and de-identification. InSAS global forum proceedings 2015;1–23.
Neubauer T, Heurix J. A methodology for the pseudonymization of medical data. Int J Med Inform. 2011;80(3):190–204.
Tang A, Tam R, Cadrin-Chênevert A, Guest W, Chong J, Barfett J, et al. Canadian Association of Radiologists white paper on artificial intelligence in radiology. Can Assoc Radiol J. 2018;69:120–35.
Soffer S, Ben-Cohen A, Shimon O, Amitai MM, Greenspan H, Klang E. Convolutional neural networks for radiologic images: a radiologist’s guide. Radiology. 2019;290(3):590–606.
Papandreou G, Chen L-C, Murphy KP, Yuille AL. Weakly-and semi-supervised learning of a deep convolutional network for semantic image segmentation. In: 2015 IEEE international conference on computer vision (ICCV); 2015. p. 1742–50.
Wang Y, Yao Q, Kwok JT, Ni LM. Generalizing from a few examples: A survey on few-shot learning. ACM computing surveys (csur). 2020;53(3):1–34.
Paul R, Hawkins SH, Balagurunathan Y, Schabath M, Gillies RJ, Hall LO, et al. Deep feature transfer learning in combination with traditional features predicts survival among patients with lung adenocarcinoma. Tomography. 2016;2(4):388–95.
Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L, editors. ImageNet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. IEEE; 2009.
Abdulazeez AM, Salim BW, Zeebaree DQ, Doghramachi D. Comparison of VPN Protocols at Network Layer Focusing on Wire Guard Protocol. iJIM. 2020;14(18):157.
Acharya MS, Armaan A, Antony AS, editors. A comparison of regression models for prediction of graduate admissions. In: 2019 International conference on computational intelligence in data science (ICCIDS). IEEE; 2019.
Zhang Z, Li Y, Li L, Li Z, Liu S, editors. Multiple linear regression for high efficiency video intra coding. In: ICASSP 2019-2019 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE; 2019.
Schmidhuber J. Deep learning in neural networks: an overview. Neural Netw. 2015;61:85–117.
Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735–80.
Cho K, van Merrienboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. arXiv e-prints. 2014 Jun:arXiv-1406.
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Generative adversarial networks. Communications of the ACM. 2020;63(11):139–44.
Luc P, Couprie C, Chintala S, Verbeek J. Semantic Segmentation using Adversarial Networks. arXiv e-prints. 2016 Nov:arXiv-1611.
Savioli N, Silva Vieira M, Lamata P, Montana G. A Generative Adversarial Model for Right Ventricle Segmentation. arXiv eprints. 2018 Sep:arXiv-1810.
Ronneberger O, Fischer P, Brox T, editors. U-Net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Cham: Springer; 2015.
Goodfellow I, Bengio Y, Courville A. Deep learning. London: MIT Press; 2016.
Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J, Devin M, Ghemawat S. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. arXiv e-prints. 2016 Mar:arXiv-1603.
Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, et al. PyTorch: An imperative style, high-performance deep learning library. Adv Neural Inf Process Syst. 2019;32:8026–37.
Raina R, Madhavan A, Ng AY. Large-scale deep unsupervised learning using graphics processors. InProceedings of the 26th annual international conference on machine learning 2009;873–80.
Ardagna CA, Asal R, Damiani E, Vu QH. From security to assurance in the cloud: A survey. ACM Computing Surveys (CSUR). 2015;48(1):1–50
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Motamedian, S.R. et al. (2023). Brief Introduction to Artificial Intelligence and Machine Learning. In: Khojasteh, A., Ayoub, A.F., Nadjmi, N. (eds) Emerging Technologies in Oral and Maxillofacial Surgery . Springer, Singapore. https://doi.org/10.1007/978-981-19-8602-4_14
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