Benefits of Artificial Intelligence to Dental Practice

Yıl 2023, Cilt: 2 Sayı: 3, 278 - 287, 26.01.2024

Cihan Akdoğan , Hatice Özdemir

https://doi.org/10.58711/turkishjdentres.vi.1296215

Cited By: 1

Öz

One of the newest fields in science and engineering which is “Artificial intelligence”, refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. Artificial intelligence has tremendous potential to improve patient care and revolutionize
the health care field when applied to medicine and dentistry. Owing to the powerful capabilities of artificial intelligence algorithms in data analysis, it is expected to identify normal and abnormal structures, increase the accuracy and efficiency of diagnosis, provide visualized anatomical guidance for
treatment, and predict and evaluate prospective results, in dentistry. The purpose of this review is to explain in dentistry, the application areas of artificial intelligence which has gained more importance with the developing technology today.

Anahtar Kelimeler

deep learning, dentistry, convolutional neural networks, artificial intelligence

Yapay Zekânın Diş Hekimliği Pratiğine Kazanımları

Öz

Bilim ve mühendislikteki en yeni alanlardan biri olan “yapay zekâ” insanlar gibi düşünmeye ve areketlerini taklit etmeye programlanmış makinelerde insan zekasının simülasyonunu ifade etmektedir. Yapay zekâ tıp ve diş hekimliğine uygulandığında hasta bakımını iyileştirmek ve sağlık alanında devrim yapmak için
muazzam bir potansiyele sahiptir. Yapay zekâ algoritmalarının veri analizindeki güçlü yetenekleri sayesinde diş hekimliğinde normal ve anormal yapıların tanımlanması, teşhisin doğruluğunu ve etkinliğini arttırması, tedavi için görselleştirilmiş anatomik rehberlik sağlaması, ileriye dönük sonuçları tahmin etmesi ve değerlendirmesi beklenmektedir. Bu derlemenin amacı, günümüzde gelişmekte olan teknolojiyle birlikte daha da önem kazanmış olan yapay zekanın diş hekimliğinde uygulama alanlarını açıklamaktadır.

Anahtar Kelimeler

derin öğrenme, diş hekimliği, evrişimli sinir ağları, yapay zeka

Kaynakça

• 1. Tandon D., Rajawat J. Present and future of artificial intelligence in dentistry. J Oral Biol Craniofacial Res. 2020;10(4):391–6.
• 2. Ahmed N., Abbasi MS., Zuberi F., Qamar W., Halim MS Bin., Maqsood A., et al. Artificial Intelligence Techniques: Analysis, Application, and Outcome in Dentistry - A Systematic Review. Biomed Res Int. 2021;2021.
• 3. Moor J. Artificial Intelligence Conference : The Next Fifty Years. AI Mag. 2006;27(4):87–91.
• 4. Shan T., Tay FR., Gu L. Application of Artificial Intelligence in Dentistry. J Dent Res. 2021;100(3):232– 44.
• 5. Aminoshariae A., Kulild J., Nagendrababu V. Artificial Intelligence in Endodontics: Current Applications and Future Directions. J Endod. 2021;47(9):1352–7.
• 6. 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(C):l7.
• 7. Khanagar SB., Al-ehaideb A., Maganur PC., Vishwanathaiah S., Patil S., Baeshen HA., et al. Developments, application, and performance of artificial intelligence in dentistry – A systematic review. J Dent Sci. 2021;16(1):508–22.
• 8. Rajaraman V. John McCarthy – Father of Artificial Intelligence. 2014;(March):198–207.
• 9. Ossowska A., Kusiak A. Artificial Intelligence in Dentistry — Narrative Review. Int J Env Res Public Heal. 2022;19(6):3449.
• 10. Hwang J-J., Azernikov S., Efros AA., Yu SX. Learning Beyond Human Expertise with Generative Models for Dental Restorations. ArXiv:180400064. 2018:1–18.
• 11. Khanna S., Dhaimade P. Artificial Intelligence: Transforming Dentistry Today. Indian J Basic Appl Med Res. 2018 May;6(3):161–7.
• 12. Mintz Y., Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol. 2019;28(2):73–81.
• 13. 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. Comput Methods Programs Biomed. 2020;186:105198.
• 14. Hamet P., Tremblay J. Artificial intelligence in medicine. Metabolism. 2017;69:S36–40. https://doi. org/10.1016/j.metabol.2017.01.011.
• 15. Schwendicke F., Samek W., Krois J. Artificial Intelligence in Dentistry: Chances and Challenges. J Dent Res. 2020;99(7):769–74.
• 16. Hopfield JJ. Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci U S A. 1982;79(8):2554–8.
• 17. Akalın B., Veranyurt Ü. Sağlık Hizmetleri ve Yönetiminde Yapay Zekâ. Acta Infologica. 2021;5(1):231–40.
• 18. Hung K., Montalvao C., Tanaka R., Kawai T., Bornstein MM. The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review.Dentomaxillofacial Radiol. 2019;49(1):20190107.
• 19. Wong SH., Al-Hasani H., Alam Z., Alam A. Artificial intelligence in radiology: how will we be affected? Eur Radiol. 2019;29(1):141–3.
• 20. Hosny A., Parmar C., Quackenbush J., Schwartz LH., Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18(8):500–10.
• 21. Geetha V., Aprameya KS., Hinduja DM. Dental caries diagnosis in digital radiographs using back-propagation neural network. Heal Inf Sci Syst. 2020;8(1):1–14.
• 22. Flores A., Rysavy S., Enciso R., Okada K. Non-invasive differential diagnosis of dental periapical lesions in cone-beam CT. Proc - 2009 IEEE Int Symp Biomed Imaging From Nano to Macro, ISBI 2009. 2009:566–9.
• 23. Okada K., Rysavy S., Flores A., Linguraru MG. Noninvasive differential diagnosis of dental periapical lesions in cone-beam CT scans. Med Phys. 2015;42(4):1653–65.
• 24. Kim Y., Lee KJ., Sunwoo L., Choi D., Nam CM., Cho J., et al. Deep Learning in Diagnosis of Maxillary Sinusitis Using Conventional Radiography. Invest Radiol. 2019;54(1):7–15.
• 25. Auconi P., Scazzocchio M., Cozza P., McNamara JA., Franchi L. Prediction of Class III treatment outcomes through orthodontic data mining. Eur J Orthod. 2015;37(3):257–67.
• 26. Gupta A., Kharbanda OP., Sardana V., Balachandran R., Sardana HK. A knowledge-based algorithm for automatic detection of cephalometric landmarks on CBCT images. Int J Comput Assist Radiol Surg. 2015;10(11):1737–52.
• 27. Thanathornwong B. Thanathornwong, B. (2018). Bayesian-based decision support system for assessing the needs for orthodontic treatment. Healthcare informatics research, 24(1), 22-28. 2018;24(1):22–8.
• 28. Spampinato C., Palazzo S., Giordano D., Aldinucci M., Leonardi R. Deep learning for automated skeletal bone age assessment in X-ray images. Med Image Anal. 2017;36:41–51.
• 29. Ribarevski R., Vig P., Dryland Vig K., Weyant R., O’Brien K. Consistency of orthodontic extraction decisions. Eur J Orthod. 1996;18(1):77–80.
• 30. 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.
• 31. Leonardi R., Giordano D., Maiorana F. An evaluation of cellular neural networks for the automatic identification of cephalometric landmarks on digital images. J Biomed Biotechnol. 2009;2009:717102.
• 32. Kim BS., Yeom HG., Lee JH., Shin WS., Yun JP., Jeong SH., et al. Deep learning-based prediction of paresthesia after third molar extraction: A preliminary study. Diagnostics. 2021;11(9):1–11.
• 33. 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):1–9.
• 34. Halicek M., Lu G., Little J V., 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):060503.
• 35. Poedjiastoeti W., Suebnukarn S. Application of convolutional neural network in the diagnosis of Jaw tumors. Healthc Inform Res. 2018;24(3):236–41.
• 36. Patcas R., Bernini DAJ., Volokitin A., Agustsson E., Rothe R., Timofte R. Applying artificial intelligence to assess the impact of orthognathic treatment on facial attractiveness and estimated age. Int J Oral Maxillofac Surg. 2019;48(1):77–83.
• 37. Patcas R., Timofte R., Volokitin A., Agustsson E., Eliades T., Eichenberger M., et al. Facial attractiveness of cleft patients: A direct comparison between artificial-intelligence-based scoring and conventional rater groups. Eur J Orthod. 2019;41(4):428–33.
• 38. Sukegawa S., Yoshii K., Hara T., Matsuyama T., Yamashita K., Nakano K., et al. Multi-task deep learning model for classification of dental implant brand and treatment stage using dental panoramic radiograph images. Biomolecules. 2021;11(6).
• 39. Kwak Y., Hieu Nguyen V., Hériveaux Y., Belanger P., Park j. Ultrasonic assessment of osseointegration phenomena at the bone-implant interface using convolutional neural network. J Acoust Soc Am. 2021;149(6):4337.
• 40. Teh Lee C., Kabir T., Nelson J., Sheng S., Wan Meng H., Van Dyke T., et al. Use of the deep learning approach to measure alveolar bone level. J Clin Periodontol. 2022;49(3):260–9.
• 41. Bayrakdar SK., Orhan K., Bayrakdar IS., Bilgir E., Ezhov M., Gusarev M., et al. A deep learning approach for dental implant planning in cone-beam computed tomography images. BMC Med Imaging. 2021;21(1):1–9.
• 42. Adel S., Zaher A., El Harouni N., Venugopal A., Premjani P., Vaid N. Robotic Applications in Orthodontics: Changing the Face of Contemporary Clinical Care. Biomed Res Int. 2021;2021:9954615.
• 43. Krois J., Ekert T., Meinhold L., Golla T., Kharbot B., Wittemeier A., et al. Deep Learning for the Radiographic Detection of Periodontal Bone Loss. Sci Rep. 2019;9(1):1–6.
• 44. Lee JH., Kim DH., Jeong SN., Choi SH. 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.
• 45. Papantonopoulos G., Takahashi K., Bountis T., Loos BG. Artificial neural networks for the diagnosis of aggressive periodontitis trained by immunologic parameters. PLoS One. 2014;9(3):4–11.
• 46. Cha JY., Yoon HI., Yeo IS., Huh KH., Han JS. Periimplant bone loss measurement using a region-based convolutional neural network on dental periapical radiographs. J Clin Med. 2021;10(5):1–12.
• 47. Chang HJ., Lee SJ., Yong TH., Shin NY., Jang BG., Kim JE., et al. Deep Learning Hybrid Method to Automatically Diagnose Periodontal Bone Loss and Stage Periodontitis. Sci Rep. 2020;10(1):1–8.
• 48. Ahmad P., Alam MK., Aldajani A., Alahmari A., Alanazi A., Stoddart M., et al. Dental robotics: A disruptive technology. Sensors. 2021;21(10):1–15.
• 49. Baugh D., Wallace J. The role of apical instrumentation in root canal treatment: A review of the literature. J Endod. 2005;31(5):333–40.
• 50. 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. J Endod. 2012;38(8):1130–4.
• 51. 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.
• 52. Ekert T., Krois J., Meinhold L., Elhennawy K., Emara R., Golla T., et al. Deep Learning for the Radiographic Detection of Apical Lesions. J Endod. 2019;45(7):917- 922.e5.
• 53. Fukuda M., Inamoto K., Shibata N., Ariji Y., Yanashita Y., Kutsuna S., et al. Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography. Oral Radiol. 2020;36(4):337–43.
• 54. Hiraiwa T., Ariji Y., Fukuda M., Kise Y., Nakata K., Katsumata A., et al. A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography. Dentomaxillofacial Radiol. 2019;48(3):1–7.
• 55. Revilla-León M., Gómez-Polo M., Vyas S., Barmak BA., Gallucci GO., Att W., et al. Artificial intelligence models for tooth-supported fixed and removable prosthodontics: A systematic review. J Prosthet Dent. 2021:1–17.
• 56. Paulus D., Wolf M., Meller S., Niemann H. Threedimensional computer vision for tooth restoration. Med Image Anal. 1999;3(1):1–19.
• 57. Matin I., Hadzistevic M., Vukelic D., Potran M., Brajlih T. Development of an expert system for the simulation model for casting metal substructure of a metal-ceramic crown design. Comput Methods Programs Biomed. 2017;146:27–35.
• 58. Wei J., Peng M., Li Q., Wang Y. Evaluation of a Novel Computer Color Matching System Based on the Improved Back-Propagation Neural Network Model. J Prosthodont. 2018;27(8):775–83. https://doi. org/10.1111/jopr.12561.
• 59. Zhang B., Dai N., Tian S., Yuan F., Yu Q. The extraction method of tooth preparation margin line based on S-Octree CNN. Int j Numer Method Biomed Eng. 2019;35(10):1–13.
• 60. Li H., Lai L., Chen L., Lu C., Cai Q. The prediction in computer color matching of dentistry based on GA+BP neural network. Comput Math Methods Med. 2015;2015.
• 61. Vera V., Corchado E., Redondo R., Sedano J., García ÁE. Applying soft computing techniques to optimise a dental milling process. Neurocomputing. 2013;109:94– 104.
• 62. Li M., Xu X., Punithakumar K., Le LH., Kaipatur N., Shi B. Automated integration of facial and intraoral images of anterior teeth. Comput Biol Med. 2020;122:103794.
• 63. Wang L., Wang D., Zhang Y., Ma L., Sun Y., Lv P. An automatic robotic system for three-dimensional tooth crown preparation using a picosecond laser. Lasers Surg Med. 2014;46(7):573–81.
• 64. Otani T., Raigrodski AJ., Mancl L., Kanuma I., Rosen J. In vitro evaluation of accuracy and precision of automated robotic tooth preparation system for porcelain laminate veneers. J Prosthet Dent. 2015;114(2):229–35.
• 65. Takahashi T., Nozaki K., Gonda T., Ikebe K. A system for designing removable partial dentures using artificial intelligence . Off J Japan Prosthodont Soc. 2021;65:115–8.
• 66. Xiao K., Zardawi F., Van Noort R., Yates JM. Color reproduction for advanced manufacture of soft tissue prostheses. J Dent. 2013;41(SUPPL.5).
• 67. Gang Jiang J., De Zhang Y. Motion planning and synchronized control of the dental arch generator of the tooth-arrangement robot. Int J Med Robot. 2013;9(1):94–102.
• 68. Lerner H., Mouhyi J., Admakin O., Mangano F. Artificial intelligence in fixed implant prosthodontics:A retrospective study of 106 implant-supported monolithic zirconia crowns inserted in the posterior jaws of 90 patients. BMC Oral Health. 2020;20(1):1– 16.
• 69. Yamaguchi S., Lee C., Karaer O., Ban S., Mine A., Imazato S. Predicting the Debonding of CAD/ CAM Composite Resin Crowns with AI. J Dent Res. 2019;98(11):1234–8.