INTELLIGENT SYSTEMS FOR PRECISION DENTAL DIAGNOSIS AND TREATMENT PLANNING – A REVIEW

Alden Schnyder Jason D; Vidya Krishnan; Divya Vinayachandran

doi:10.7126/cumudj.991480

Review

INTELLIGENT SYSTEMS FOR PRECISION DENTAL DIAGNOSIS AND TREATMENT PLANNING – A REVIEW

Year 2022, Volume: 25 Issue: 2, 187 - 194, 30.06.2022

Alden Schnyder Jason D Vidya Krishnan Divya Vinayachandran

https://doi.org/10.7126/cumudj.991480

Abstract

Machines have changed the course of mankind. Simple machines were the basis of human civilization. Today with humongous technological development, machines are intelligent enough to carry out very complex nerve-racking tasks. The ability of a machine to learn from algorithms changed eventually into, the machine learning by itself, which constitutes artificial intelligence. Literature has plausible evidence for the use of intelligent systems in medical field. Artificial intelligence has been used in the multiple denominations of dentistry. These machines are used in the precision diagnosis, interpretation of medical images, accumulation of data, classification and compilation of records, determination of treatment and construction of a personalized treatment plan. Artificial intelligence can help in timely diagnosis of complex dental diseases which would ultimately aid in rapid commencement of treatment. Research helps us understand the effectiveness and challenges in the use of this technology. The apt use of intelligent systems could transform the entire medical system for the better.

Keywords

Deep learning, Artificial intelligence, convolutional neural networks, dentistry

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Year 2022, Volume: 25 Issue: 2, 187 - 194, 30.06.2022

Alden Schnyder Jason D Vidya Krishnan Divya Vinayachandran

https://doi.org/10.7126/cumudj.991480

Abstract

References

1. Zhang S, Bamakan SMH, Qu Q, Li S. Learning for personalized medicine: a comprehensive review from a deep learning perspective. IEEE Rev Biomed Eng 2019;12:194-208.
2. Belard A, Buchman T, Forsberg J, Potter BK, Dente CJ, Kirk A, et al. Precision diagnosis: a view of the clinical decision support systems (CDSS) landscape through the lens of critical care. J ClinMonitComput 2017;31(2):261–71.
3. Schork NJ. Artificial intelligence and personalized medicine. Cancer Treat Res 2019;178:265–83.
4. Russell S, Norvig P. Artificial intelligence: a modern approach. 3rd ed. Carmel, Indiana: Pearson;2009.
5. Amisha, Malik P, Pathania M, Rathaur VK. Overview of artificial intelligence in medicine. J Family Med Prim Care 2019;8(7):2328–31.
6. Hamlet P, Tremblay J. Artificial intelligence in medicine. Metabolism 2017;69S:S36–40.
7. Imran N, Jawaid M. Artificial intelligence in medical education: Are we ready for it? Pak J Med Sci 2020;36(5):857-9.
8. Sandip P, Yvonne C, Chander D, Morey J, Juan FM, Michel K. Artificial intelligence and the future of surgical robotics. Ann Surg 2019;270(2):223-6.
9. Du G, Cao X, Liang J, Chen X, Zhan Y. Medical image segmentation based on U-net: a review. J Imaging SciTechnol 2020;1:64.
10. Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. GastrointestEndosc 2020;92(4):807–12.
11. Perlovsky LI. Neural mechanisms of the mind, Aristotle, Zadeh, and fMRI. IEEE Trans Neural Netw 2010;21(5):718-33.
12. Turing A. On computable numbers, with an application to the Entscheidungs problem. Proc London Math Soc 1936;42:230–65.
13. Turing A. Computing Machinery and Intelligence. Mind. 1950;49:433–60.
14. Park WJ, Park J-B. History and application of artificial neural networks in dentistry. Eur J Dent 2018;12(4):594–601.
15. Weizenbaum J. ELIZA-a computer program for the study of natural language communication between man and machine. Commun ACM. 1966;9(1):36–45.
16. Kulikowski CA. Beginnings of artificial intelligence in medicine (AIM): computational artifice assisting scientific inquiry and clinical art - with reflections on present AIM challenges. Yearb Med Inform 2019;28(1):249-56.
17. Kulikowski CA. An opening chapter of the first generation of artificial intelligence in medicine: the first rutgers AIM workshop, June 1975. Yearb Med Inform 2015;10(1):227-33.
18. Weiss S, Kulikowski CA, Safir A. Glaucoma consultation by computer. ComputBiol Med 1978;8(1):25-40.
19. Comendador BEV, Francisco BMB, Medenilla JS, Nacion SMT, Serac TBE. Pharmabot: A pediatric generic medicine consultant chatbot. J autom control eng 2015;3(2):137–40.
20. Goldhahn J, Rampton V, Spinas G. Could artificial intelligence make doctors obsolete? BMJ 2018;363:k4563.
21. Yu D, Deng L. Deep learning and its applications to signal and information processing. IEEE Signal Process Mag 2011;28:145-54.
22. Hubel DH, Wiesel TN. Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J Physiol 1962;160:106–54.
23. Hubel DH, Wiesel TN. Receptive fields of single neurones in the cat’s striate cortex. J Physiol 1959;148:574–91.
24. LeCun Y, Touresky D, Hinton G, Sejnowski T. A theoretical framework for back-propagation. Proc 1988 Connect Model Summer Sch 1988;1:21–8.
25. Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science 2006;313:504-7.
26. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521:436-44.
27. Hinton G, Deng L, Yu D, Dahl GE, Mohamed A, Jaitly N. Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. Signal Process Mag IEEE 2012;29:82-97.
28. Lawrence S, Giles CL, Tsoi AC, Back AD. Face recognition: a convolutional neural-network approach. IEEE Trans Neural Netw Learn Syst 1997;8:98-113.
29. Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. Proc IEEE ComputSocConfComput Vis Pattern Recognit 2015;79:3431–40.
30. Karpathy A, Toderici G, Shetty S, Leung T, Sukthankar R, Li FF. Large-scale video classification with convolutional neural networks. Proc IEEE ConfComput Vis Pattern Recognit 2014;1725-32.
31. Lecun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE 1998;86:2278–324.
32. Cao C, Liu F, Tan H, Song D, Shu W, Li W, et al. Deep learning and its applications in biomedicine. Genomics Proteomics Bioinformatics 2018;16(1):17–32.
33. Naylor CD. On the prospects for a (deep) learning health care system. JAMA 2018;320(11):1099-100.
34. Israni ST, Verghese A. Humanizing artificial intelligence. JAMA 2019;321(1):29-30.
35. Schwendicke F, Samek W, Krois J. Artificial intelligence in dentistry: chances and challenges. J Dent Res 2020;99(7):769–74.
36. Jubair F, Al-karadsheh O, Malamos D, Al Mahdi S, Saad Y, Hassona Y. A novel lightweight deep convolutional neural network for early detection of oral cancer. Oral Dis 2021;00:1-8.
37. Avuçlu E, Başçiftçi F. Novel approaches to determine age and gender from dental X-ray images by using multiplayer perceptron neural networks and image processing techniques. Chaos Solitons Fractals 2019;120:127-38.
38. Matsuda S, Miyamoto T, Yoshimura H, Hasegawa T. Personal identification with orthopantomography using simple convolutional neural networks: a preliminary study. Sci Rep 2020;10(1):135-59.
39. Kuwada C, Ariji Y, Fukuda M, Kise Y, Fujita H, Katsumata A, et al. Deep learning systems for detecting and classifying the presence of impacted supernumerary teeth in the maxillary incisor region on panoramic radiographs. Oral Surg Oral Med Oral Pathol Oral Radiol 2020;130(4):464-9.
40. Lee JH, Kim DH, Jeong SN, Choi SH. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J Dent 2018;77:106-11.
41. Schwendicke F, Rossi JG, Göstemeyer G, Elhennawy K, Cantu AG, Gaudin R, et al. Cost-effectiveness of artificial intelligence for proximal caries detection. J Dent Res 2021;100(4):369–76.
42. Limonadi F, Mccartney S, Burchiel K. Use of an artificial neural network for diagnosis of facial pain syndromes: an update. StereotactFunctNeurosurg 2006;84:212–20.
43. Song A, Wu Z, Ding X, Hu Q, Di X. Neurologist standard classification of facial nerve paralysis with deep neural networks. Future Internet 2018;10:111.
44. Speight PM, Elliott AE, Jullien JA, Downer MC, Zakzrewska JM. The use of artificial intelligence to identify people at risk of oral cancer and precancer. Br Dent J 1995;179(10):382-7.
45. Welikala R, Remagnino P, Lim J, Chan CS, Rajendran S, George T, et al. Automated detection and classification of oral lesions using deep learning for early detection of oral cancer. IEEE Access 2020;1:1.
46. Men K, Geng H, Zhong H, Fan Y, Lin A, Xiao Y. A deep learning model for predicting xerostomia due to radiation therapy for head and neck squamous cell carcinoma in the rtog 0522 clinical trial. Int J RadiatOncolBiol Phys 2019;105(2):440-7.
47. Park J, Lee JS, Oh D, Ryoo HG, Han JH, Lee WW. Quantitative salivary gland SPECT/CT using deep convolutional neural networks. Sci Rep 2021;11(1):1–10.
48. Kim JY, Kim D, Jeon KJ, Kim H, Huh JK. Using deep learning to predict temporomandibular joint disc perforation based on magnetic resonance imaging. Sci Rep 2021;11(1):6680.
49. Cejudo JE, Chaurasia A, Feldberg B, Krois J, Schwendicke F. Classification of dental radiographs using deep learning. J Clin Med 2021;10:1496.
50. Tuzoff DV, Tuzova LN, Bornstein MM, Krasnov AS, Kharchenko MA, Nikolenko SI, et al. Tooth detection and numbering in panoramic radiographs using convolutional neural networks. DentomaxillofacRadiol 2019;48:20180051.
51. Sur J, Bose S, Khan F, Dewangan D, Sawriya E, Roul A. Knowledge, attitudes, and perceptions regarding the future of artificial intelligence in oral radiology in India: A survey. Imaging Sci Dent 2020;50(3):193–8.
52. Kise Y, Ikeda H, Fujii T, Fukuda M, Ariji Y, Fujita H, et al. Preliminary study on the application of deep learning system to diagnosis of Sjögren's syndrome on CT images. DentomaxillofacRadiol 2019;48:20190019.
53. Kise Y, Shimizu M, Ikeda H, Fujii T, Kuwada C, Nishiyama M, et al. Usefulness of a deep learning system for diagnosing Sjögren’s syndrome using ultrasonography images. DentomaxillofacRadiol 2020;49:20190348.
54. Dalitz GD. Age determination of adult human remains by teeth examination. J Forensic SciSoc 1962;3:11-21.
55. Bewes J, Low A, Morphett A, Pate FD, Henneberg M. Artificial intelligence for sex determination of skeletal remains: application of a deep learning artificial neural network to human skulls. J Forensic Leg Med 2019;62:40-3.
56. Gross GW, Boone JM, Bishop DM. Pediatric skeletal age: determination with neural networks. Radiology 1995;195:689-95.
57. Murata M, Ariji Y, Ohashi Y, Kawai T, Fukuda M, Funakoshi T, et al. Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography. Oral Radiol 2019;35(3):301–7.
58. Bouletreau P, Makaremi M, Ibrahim B, Louvrier A, Sigaux N. artificial intelligence: applications in orthognathic surgery. J Stomatol Oral MaxillofacSurg 2019;120(4):347-54.
59. Shin WS, Yeom HG, Lee GH, Yun JP, Jeong SH, Lee JH, et al. Deep learning based prediction of necessity for orthognathic surgery of skeletal malocclusion using cephalogram in Korean individuals. BMC Oral Health 2021;21(1):130.
60. Zhang W, Li J, Li ZB, Li Z. Predicting postoperative facial swelling following impacted mandibular third molars extraction by using artificial neural networks evaluation. Sci Rep 2018;8(1):122-81.
61. Miranda-Filho A, Bray F. Global patterns and trends in cancers of the lip, tongue and mouth. Oral Oncol 2020;102:104551.
62. Halicek M, Lu G, Little JV, Wang X, Patel M, Griffith CC, et al. Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging. J Biomed Opt 2017;22(6):60503.
63. Fu Q, Chen Y, Li Z, Jing Q, Hu C, Liu H, et al. A deep learning algorithm for detection of oral cavity squamous cell carcinoma from photographic images: A retrospective study. EClinicalMedicine 2020;27:100558.
64. Kim DW, Lee S, Kwon S, Nam W, Cha IH, Kim HJ. Deep learning-based survival prediction of oral cancer patients. Sci Rep 2019;9(1):6994.
65. Wang X, Li BB. Deep learning in head and neck tumor multiomics diagnosis and analysis: review of the literature. Front Genet 2021;12:624820.
66. Takahashi T, Nozaki K, Gonda T, Mameno T, Ikebe K. Deep learning-based detection of dental prostheses and restorations. Sci Rep 2021;11(1):1960.
67. Sukegawa S, Yoshii K, Hara T, Yamashita K, Nakano K, Yamamoto N, et al. Deep neural networks for dental implant system classification. Biomolecules 2020;10(7):984.
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Details

Primary Language	English
Subjects	Health Care Administration
Journal Section	Review
Authors	Alden Schnyder Jason D 0000-0002-4814-1342 Vidya Krishnan 0000-0003-1860-1296 Divya Vinayachandran 0000-0001-7042-6435
Publication Date	June 30, 2022
Submission Date	September 13, 2021
Published in Issue	Year 2022Volume: 25 Issue: 2

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EndNote	Schnyder Jason D A, Krishnan V, Vinayachandran D (June 1, 2022) INTELLIGENT SYSTEMS FOR PRECISION DENTAL DIAGNOSIS AND TREATMENT PLANNING – A REVIEW. Cumhuriyet Dental Journal 25 2 187–194.

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Cumhuriyet Dental Journal (Cumhuriyet Dent J, CDJ) is the official publication of Cumhuriyet University Faculty of Dentistry. CDJ is an international journal dedicated to the latest advancement of dentistry. The aim of this journal is to provide a platform for scientists and academicians all over the world to promote, share, and discuss various new issues and developments in different areas of dentistry. First issue of the Journal of Cumhuriyet University Faculty of Dentistry was published in 1998. In 2010, journal's name was changed as Cumhuriyet Dental Journal. Journal’s publication language is English.

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