nach oben

15.03.2024

How Artificial Inelegance Is Transforming Aesthetic Dentistry: A Review

verfasst von: Tamer M. Hamdy

Erschienen in: Current Oral Health Reports

Einloggen, um Zugang zu erhalten

Abstract

Purpose of Review

This review was conducted to focus on how artificial intelligence (AI) could be applied to provide great development in aesthetic dentistry. The historical development, advantages and disadvantages, classification, types, present and advanced dental applications, and their potential perspectives are summarized and explained.

Recent Findings

Most of the AI applications in dentistry revolve around diagnosis (radiographic or optical images), decision-making improvement, suggestion of treatment plans, and prediction of treatment progress. Various recent innovative AI tools could be employed for the improvement of professional practice and patient outcomes. Dental applications of AI can be divided into four categories: diagnostics, decision-making, treatment planning, and treatment result prediction. Advanced dental applications include the creation of perfect digital smile designs, AI-based 3D printing, assessment of endodontic complexity, AI-based FEA, AI-based CAD/CAM, AI dental phonetic apps, and eye-tracking dental applications.

Summary

AI has significantly transformed aesthetic dentistry in recent years. It is developing quickly, with potential uses in many different aspects like diagnosis, prognosis, and treatment prediction. Although AI is a relatively young technology, it is widely used in dentistry. AI applications in dentistry include nearly all dental disciplines, such as dental radiology, dental caries diagnosis, orthodontics, implant dentistry, fixed prosthetic restorations, and endodontics. The knowledge of the dental professional must be updated regarding the application of AI in the dental field. Hence, the objective of this review was to represent the literature relevant to the applications of AI in the context of diagnosis, clinical decision-making, and predicting successful treatment in aesthetic dentistry and, additionally, to identify the advanced and potential applications of AI.

1.••

Lyu L qing, Cui H yan, Shao M yi, Fu Y, Zhao R xia, Chen Q ping. Computational medicine: past, present and future. Chin J Integr Med. 2022;28:453–62. https://doi.org/10.1007/s11655-021-3453-z. Was an emerging research project that described the uses of computer models and complex software to simulate the development and treatment of dental problems.

Turing AM. Computing machinery and intelligence psychology and philosophy. Nature. 1950;236:433–60. https://doi.org/10.1093/mind/LIX.236.433.CrossRef

3.•

Rajaraman V. JohnMcCarthy — father of artificial intelligence. Resonance. 2014;19:198–207. https://doi.org/10.1007/s12045-014-0027-9. Was the first study that referred to the history of artificial intelligence.CrossRef

Bonnefon JF, Rahwan I. Machine thinking, fast and slow. Trends Cogn Sci. 2020;24:1019–27. https://doi.org/10.1016/j.tics.2020.09.007.CrossRefPubMed

Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM. 2017;60:84–90. https://doi.org/10.1145/3065386.CrossRef

Mörch CM, Atsu S, Cai W, Li X, Madathil SA, Liu X, et al. Artificial intelligence and ethics in dentistry: a scoping review. J Dent Res. 2021;100:1452–60. https://doi.org/10.1177/00220345211013808.CrossRefPubMed

Ayad N, Schwendicke F, Krois J, van den Bosch S, Bergé S, Bohner L, et al. Patients’ perspectives on the use of artificial intelligence in dentistry: a regional survey. Head Face Med. 2023;19:1–10. https://doi.org/10.1186/s13005-023-00368-z.CrossRef

Galante N, Cotroneo R, Furci D, Lodetti G, Casali MB. Applications of artificial intelligence in forensic sciences: current potential benefits, limitations and perspectives. Int J Legal Med. 2023;137:445–58. https://doi.org/10.1007/s00414-022-02928-5.CrossRefPubMed

Siddiqui TA, Sukhia RH, Ghandhi D. Artificial intelligence in dentistry, orthodontics and orthognathic surgery: a literature review. J Pak Med Assoc. 2022;S91–6.

10.

Fatima A, Shafi I, Afzal H, Díez IDLT, Lourdes DR-SM, Breñosa J, et al. Advancements in dentistry with artificial intelligence: current clinical applications and future perspectives. Healthcare. 2022;10:2188. 2227–9032/10/11/2188.

11.

Pareek M, Kaushik B. Artificial intelligence in prosthodontics: a scoping review on current applications and future possibilities. Int J Adv Med. 2022;9:367–70. https://doi.org/10.18203/2349-3933.ijam20220444.CrossRef

12.

Karobari MI, Adil AH, Basheer SN, Murugesan S, Savadamoorthi KS, Mustafa M, et al. Evaluation of the diagnostic and prognostic accuracy of artificial intelligence in endodontic dentistry: a comprehensive review of literature. Patil RU, editor. Comput Math Methods Med [Internet]. 2023;2023:1–9. https://doi.org/10.1155/2023/7049360.

13.

Tay JRH, Ng E, Chow DY, Sim CPC. The use of artificial intelligence to aid in oral hygiene education: a scoping review. J Dent. 2023;135:1–11. https://doi.org/10.1016/j.jdent.2023.104564.CrossRef

14.

Chae YM, Yoo KB, Kim ES, Chae H. The adoption of electronic medical records and decision support systems in Korea. Healthc Inform Res. 2011;17:172–7. https://doi.org/10.4258/hir.2011.17.3.172.CrossRefPubMedPubMedCentral

15.

Zhu J, Chen Z, Zhao J, Yu Y, Li X, Shi K, et al. Artificial intelligence in the diagnosis of dental diseases on panoramic radiographs: a preliminary study. BMC Oral Health. 2023;358:172–7. https://doi.org/10.1186/s12903-023-03027-6.CrossRef

16.

Zhang K, Wu J, Chen H, Lyu P. An effective teeth recognition method using label tree with cascade network structure. Comput Med Imaging Graph. 2018;68:61–70. https://doi.org/10.1016/j.compmedimag.2018.07.001.CrossRefPubMed

17.

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. Dentomaxillofacial Radiol. 2019;48:1–11. https://doi.org/10.1259/dmfr.20180051.CrossRef

18.

Kühnisch J, Meyer O, Hesenius M, Hickel R, Gruhn V. Caries detection on intraoral images using artificial intelligence. J Dent Res. 2022;101:158–65. https://doi.org/10.1177/00220345211032524.CrossRefPubMed

19.

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;100:369–76. https://doi.org/10.1177/0022034520972335.CrossRefPubMed

20.

Del Real A, Del Real O, Sardina S, Oyonarte R. Use of automated artificial intelligence to predict the need for orthodontic extractions. Korean J Orthod. 2022;1–11. https://doi.org/10.4041/kjod.2022.52.2.102.

21.

Wang X, Cai B, Cao Y, Zhou C, Yang L, Liu R, et al. Objective method for evaluating orthodontic treatment from the lay perspective: an eye-tracking study. Am J Orthod Dentofac Orthop. 2016;601–610. https://doi.org/10.1016/j.ajodo.2016.03.028.

22.

Preoteasa CT, Axante A, Hagiu AM, Cristea AD, Preoteasa E. Comparative analysis of phonetic tests used for assessing the smile display. Rom J Oral Rehabil. 2016;8:43–50.

23.

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;1–9. https://doi.org/10.1186/s12880-021-00618-z.

24.

Revilla-León M, Gómez-Polo M, Vyas S, Barmak BA, Galluci GO, Att W, et al. Artificial intelligence applications in implant dentistry: a systematic review. J Prosthet Dent. 2023;293–301. https://doi.org/10.1016/j.prosdent.2021.05.008.

25.

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;1–9.https://doi.org/10.3390/diagnostics11020233.

26.

Bruellmann DD, Tjaden H, Schwanecke U, Barth P. An optimized video system for augmented reality in endodontics: a feasibility study. Clin Oral Investig. 2013;17:441–8. https://doi.org/10.1007/s00784-012-0718-0.CrossRefPubMed

27.

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;53:680–9. https://doi.org/10.1111/iej.13265.CrossRefPubMed

28.

Fuss Z, Lustig J, Katz A, Tamse A. An evaluation of endodontically treated vertical root fractured teeth: impact of operative procedures. J Endod. 2001;27:46–68948. https://doi.org/10.1097/00004770-200101000-00017.CrossRefPubMed

29.

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;36:337–43. https://doi.org/10.1007/s11282-019-00409-x.CrossRefPubMed

30.

Campo L, Aliaga IJ, De Paz JF, García AE, Bajo J, Villarubia G, et al. Retreatment predictions in odontology by means of CBR systems. Comput Intell Neurosci. 2016;2016:1–11. 2016/7485250.

31.

Qu Y, Lin Z, Yang Z, Lin H, Huang X, Gu L. Machine learning models for prognosis prediction in endodontic microsurgery. J Dent. 2022;118:1–7. https://doi.org/10.1016/j.jdent.2022.103947.CrossRef

32.

Basudan T, Althaqafi K, Alblowi S, Alhawas Y, Alblow S, Alzamil A, et al. An overview of digital smile and its use in aesthetic dentistry. J Healthc Sci. 2022;2:139–43. https://doi.org/10.52533/JOHS.2022.2703.CrossRef

33.

Ceylan G, Özel GS, Memi G. Evaluating the facial esthetic outcomes of digital smile designs generated by artificial intelligence and dental professionals. Appl Sci. 2023;13:1–10. https://doi.org/10.3390/app13159001.CrossRef

34.

Jreige CS, Kimura RN, Segundo ÂRTC, Coachman C, Sesma N. Esthetic treatment planning with digital animation of the smile dynamics: a technique to create a 4-dimensional virtual patient. J Prosthet Dent. 2022;128:130–8. https://doi.org/10.1016/j.prosdent.2020.10.015.CrossRefPubMed

35.

Khan MI, Laxmikanth SM, Gopal T, Neela PK. Artificial intelligence and 3D printing technology in orthodontics: future and scope. AIMS Biophys. 2022;9:182–97. https://doi.org/10.3934/biophy.2022016.CrossRef

36.

Dadhwal R, Kumar R, Singh Chohan J, Singh S, Maurya S. Research trends and applications of artificial intelligence in 3D printing-a scientometric analysis. Lect Notes Networks Syst. 2023;389–95. Springer, Singapore.ISBN 9789811925375. https://doi.org/10.1007/978-981-19-2538-2_39.

37.

Essam O, Boyle EL, Whitworth JM, Jarad FD. The Endodontic Complexity Assessment Tool (E-CAT): a digital form for assessing root canal treatment case difficulty. Int Endod J. 2021;9:182–97. https://doi.org/10.1111/iej.13506.CrossRef

38.

Pesonen R, Tanner T, Käkilehto T, Oikarinen-Juusola K, Laitala M-L, Anttonen V. Usefulness of an endodontic case difficulty assessment form of root canal treatments in dental education in Finland. Dent J. 2021;9:1–10. https://doi.org/10.3390/dj9100118.CrossRef

39.

Meethal RE, Kodakkal A, Khalil M, Ghantasala A, Obst B, Bletzinger KU, et al. Finite element method-enhanced neural network for forward and inverse problems. Adv Model Simul Eng Sci. 2023;1–8. https://doi.org/10.1186/s40323-023-00243-1.

40.

Vardhan H, Sztipanovits J. Deep learning based FEA surrogate for sub-sea pressure vessel. Proc - 2022 6th Int Conf Comput Softw Model ICCSM 2022;36–39. https://doi.org/10.1109/ICCSM57214.2022.00013.

41.

Liu GR. FEA-AI and AI-AI: Two-way deepnets for real-time computations for both forward and inverse mechanics problems. Int J Comput Methods. 2019;16:1–8. https://doi.org/10.1186/s40323-023-00243-1.ADSMathSciNetCrossRef

42.

Naidu V, Jaju S. CAD/CAM engineering and artificial intelligence in dentistry. IOP Conf Ser Mater Sci Eng. 2022;1259:1–11. https://doi.org/10.1088/1757-899x/1259/1/012018.CrossRef

43.

Jiang Y, Tran TH, Williams L. Machine learning and mixed reality for smart aviation: applications and challenges. J Air Transp Manag. 2023;111:1–16. https://doi.org/10.1016/j.jairtraman.2023.102437.CrossRef

44.

Elsubeihi ES, Elkareimi Y, Elbishari H. Phonetic considerations in restorative dentistry. Dent Update. 2019;46:880–93. https://doi.org/10.12968/denu.2019.46.9.880.CrossRef

45.

Beddor PS. Advancements of phonetics in the 21st century: theoretical and empirical issues in the phonetics of sound change. J Phon. 2023;97:1–11. https://doi.org/10.1016/j.wocn.2023.101228.CrossRef

46.

Cho VY, Loh XH, Abbott L, Mohd-Isa NA, Anthonappa RP. Reporting eye-tracking studies in dentistry (RESIDE) checklist. J Dent. 2023;129:1–17. https://doi.org/10.1016/j.jdent.2022.104359.CrossRef

47.

Castner N, Frankemolle J, Keutel C, Huettig F, Kasneci E. LSTMs can distinguish dental expert saccade behavior with high “plaque-urracy.” Eye Track Res Appl Symp. 2022;8:1–7. https://doi.org/10.1145/3517031.3529631.CrossRef

48.

Försch M, Krull L, Hechtner M, Rahimi R, Wriedt S, Wehrbein H, et al. Perception of esthetic orthodontic appliances: an eye tracking and cross-sectional study. Angle Orthod. 2020;90:109–17. https://doi.org/10.2319/031419-198.1.CrossRefPubMed

49.

Song ES, Kim WH, Lee BH, Han DW, Lee JH, Kim B. Assessment of color perception and preference with eye-tracking analysis in a dental treatment environment. Int J Environ Res Public Health. 2021;18:1–18. https://doi.org/10.3390/ijerph18157981.CrossRef

50.

Bastos SQ, Tanaka OM, Kirsten GA, Gasparello GG, Meira TM, Camargo ES, et al. Esthetic perception of gummy smile with the application of botulinum toxin, via eye-tracking. Res Soc Dev. 2022;11:1–13. https://doi.org/10.33448/rsd-v11i6.29518.CrossRef

51.

Borchers C, Eder TF, Richter J, Keutel C, Huettig F, Scheiter K. A time slice analysis of dentistry students’ visual search strategies and pupil dilation during diagnosing radiographs. PLoS One. 2023;18:1–17. https://doi.org/10.1371/journal.pone.0283376.CrossRef

52.

Arsiwala-Scheppach LT, Castner N, Rohrer C, Mertens S, Kasneci E, CejudoGrano de Oro JE, et al. Gaze patterns of dentists while evaluating bitewing radiographs. J Dent. 2023;135:1–8. https://doi.org/10.1016/j.jdent.2023.104585.CrossRef

53.

Vodanović M, Subašić M, Milošević D, SavićPavičin I. Artificial intelligence in medicine and dentistry. Acta Stomatol Croat. 2023;57:70–84. https://doi.org/10.15644/asc57/1/8.CrossRefPubMedPubMedCentral

54.

Shin H, Markey MK. A machine learning perspective on the development of clinical decision support systems utilizing mass spectra of blood samples. J Biomed Inform. 2006;39:227–48. https://doi.org/10.1016/j.jbi.2005.04.002.CrossRefPubMed

55.

Hung KF, Yeung AWK, Bornstein MM, Schwendicke F. Personalized dental medicine, artificial intelligence, and their relevance for dentomaxillofacial imaging. Dentomaxillofac Radiol. 2023;52:1–22. https://doi.org/10.1259/dmfr.20220335.CrossRef

56.

Li J-PO, Liu H, Ting DSJ, Jeon S, Chan RVP, Kim JE, et al. Digital technology, tele-medicine and artificial intelligence in ophthalmology: a global perspective. Prog Retin Eye Res. 2021;82:1–11. https://doi.org/10.1016/j.preteyeres.2020.100900.CrossRef

57.

Liu L, Watanabe M, Ichikawa T. Robotics in dentistry: a narrative review. Dent J. 2023;11:1–15. 2304–6767/11/3/62.

58.

Eto N, Yamazoe J, Tsuji A, Wada N, Ikeda N. Development of an artificial intelligence-based algorithm to classify images acquired with an intraoral scanner of individual molar teeth into three categories. Bianconi F, editor. PLoS One. 2022;17:1–10. https://doi.org/10.1371/journal.pone.0261870.CrossRef

59.

Leven R, Schmidt A, Binder R, Kampschulte M, Vogler J, Wöstmann B, et al. Accuracy of digital impression taking with intraoral scanners and fabrication of CAD/CAM posts and cores in a fully digital workflow. Materials (Basel). 2022;15:4199. 1996–1944/15/12/4199.

Titel: How Artificial Inelegance Is Transforming Aesthetic Dentistry: A Review
verfasst von: Tamer M. Hamdy
Publikationsdatum: 15.03.2024
Verlag: Springer International Publishing
Erschienen in: Current Oral Health Reports
Elektronische ISSN: 2196-3002
DOI: https://doi.org/10.1007/s40496-024-00372-5

Neu im Fachgebiet Zahnmedizin

Parodontalbehandlung verbessert Prognose bei Katheterablation

19.04.2024 Vorhofflimmern Nachrichten

Werden Personen mit Vorhofflimmern in der Blanking-Periode nach einer Katheterablation gegen eine bestehende Parodontitis behandelt, verbessert dies die Erfolgsaussichten. Dafür sprechen die Resultate einer prospektiven Untersuchung.

Invasive Zahnbehandlung: Wann eine Antibiotikaprophylaxe vor infektiöser Endokarditis schützt

11.04.2024 Endokarditis Nachrichten

Bei welchen Personen eine Antibiotikaprophylaxe zur Prävention einer infektiösen Endokarditis nach invasiven zahnärztlichen Eingriffen sinnvoll ist, wird diskutiert. Neue Daten stehen im Einklang mit den europäischen Leitlinienempfehlungen.

Zell-Organisatoren unter Druck: Mechanismen des embryonalen Zahnwachstums aufgedeckt

08.04.2024 Zahnmedizin Nachrichten

Der Aufbau von Geweben und Organen während der Embryonalentwicklung wird von den Zellen bemerkenswert choreografiert. Für diesen Prozess braucht es spezielle sogenannte „Organisatoren“. In einer aktuellen Veröffentlichung im Fachjournal Nature Cell Biology berichten Forschende durch welchen Vorgang diese Organisatoren im Gewebe entstehen und wie sie dann die Bildung von Zähnen orchestrieren.

Die Oralprophylaxe & Kinderzahnheilkunde umbenannt

11.03.2024 Kinderzahnmedizin Nachrichten

Infolge der Umbenennung der Deutschen Gesellschaft für Kinderzahnheilkunde in Deutsche Gesellschaft für Kinderzahnmedizin (DGKiZ) wird deren Mitgliederzeitschrift Oralprophylaxe & Kinderzahnheilkunde in Oralprophylaxe & Kinderzahnmedizin umbenannt. Aus diesem Grunde trägt die erste Ausgabe in 2024 erstmalig den neuen Titel.

Bestellen Sie unseren kostenlosen Newsletter Update Zahnmedizin und bleiben Sie gut informiert – ganz bequem per eMail.

Newsletter bestellen