The Use and Performance of Artificial Intelligence in Prosthodontics: A Systematic Review

Overview

Journal Sensors (Basel)

Publisher MDPI

Specialty Biotechnology

Date 2021 Oct 13

PMID 34640948

Citations 29

Authors

Selina A Bernauer

Nicola U Zitzmann

Tim Joda

Affiliations

Soon will be listed here.

Abstract

(1) Background: The rapid pace of digital development in everyday life is also reflected in dentistry, including the emergence of the first systems based on artificial intelligence (AI). This systematic review focused on the recent scientific literature and provides an overview of the application of AI in the dental discipline of prosthodontics. (2) Method: According to a modified PICO-strategy, an electronic (MEDLINE, EMBASE, CENTRAL) and manual search up to 30 June 2021 was carried out for the literature published in the last five years reporting the use of AI in the field of prosthodontics. (3) Results: 560 titles were screened, of which 30 abstracts and 16 full texts were selected for further review. Seven studies met the inclusion criteria and were analyzed. Most of the identified studies reported the training and application of an AI system ( = 6) or explored the function of an intrinsic AI system in a CAD software ( = 1). (4) Conclusions: While the number of included studies reporting the use of AI was relatively low, the summary of the obtained findings by the included studies represents the latest AI developments in prosthodontics demonstrating its application for automated diagnostics, as a predictive measure, and as a classification or identification tool. In the future, AI technologies will likely be used for collecting, processing, and organizing patient-related datasets to provide patient-centered, individualized dental treatment.

Citing Articles

Verification of the accuracy and design time of crowns designed with artificial intelligence.

Nagata K, Inoue E, Nakashizu T, Seimiya K, Atsumi M, Kimoto K J Adv Prosthodont. 2025; 17(1):1-10.

PMID: 40061025 PMC: 11886404. DOI: 10.4047/jap.2025.17.1.1.

Artificial Intelligence in Dentistry: A Descriptive Review.

Mallineni S, Sethi M, Punugoti D, Kotha S, Alkhayal Z, Mubaraki S Bioengineering (Basel). 2025; 11(12.

PMID: 39768085 PMC: 11673909. DOI: 10.3390/bioengineering11121267.

The relationships of personality traits on perceptions and attitudes of dentistry students towards AI.

Ozbey F, Yasa Y BMC Med Educ. 2025; 25(1):26.

PMID: 39762843 PMC: 11706038. DOI: 10.1186/s12909-024-06630-5.

Artificial Intelligence and Modern Technology in Dentistry: Attitudes, Knowledge, Use, and Barriers Among Dentists in Croatia-A Survey-Based Study.

Ivanisevic A, Tadin A Clin Pract. 2024; 14(6):2623-2636.

PMID: 39727795 PMC: 11674914. DOI: 10.3390/clinpract14060207.

Application of Convolutional Neural Networks for Determining Gender and Age in Forensic Dentistry.

Hundur Hiyari M, Pasic M, Zukic S Cureus. 2024; 16(11):e73028.

PMID: 39640166 PMC: 11618129. DOI: 10.7759/cureus.73028.

References

Alafif T, Tehame A, Bajaba S, Barnawi A, Zia S . Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions. Int J Environ Res Public Health. 2021; 18(3). PMC: 7908539. DOI: 10.3390/ijerph18031117. View

Moher D, Liberati A, Tetzlaff J, Altman D . Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009; 6(7):e1000097. PMC: 2707599. DOI: 10.1371/journal.pmed.1000097. View

Grischke J, Johannsmeier L, Eich L, Griga L, Haddadin S . Dentronics: Towards robotics and artificial intelligence in dentistry. Dent Mater. 2020; 36(6):765-778. DOI: 10.1016/j.dental.2020.03.021. View

Zitzmann N, Krastl G, Hecker H, Walter C, Waltimo T, Weiger R . Strategic considerations in treatment planning: deciding when to treat, extract, or replace a questionable tooth. J Prosthet Dent. 2010; 104(2):80-91. DOI: 10.1016/S0022-3913(10)60096-0. View

Joda T, Yeung A, Hung K, Zitzmann N, Bornstein M . Disruptive Innovation in Dentistry: What It Is and What Could Be Next. J Dent Res. 2020; 100(5):448-453. DOI: 10.1177/0022034520978774. View

Raith S, Vogel E, Anees N, Keul C, Guth J, Edelhoff D . Artificial Neural Networks as a powerful numerical tool to classify specific features of a tooth based on 3D scan data. Comput Biol Med. 2016; 80:65-76. DOI: 10.1016/j.compbiomed.2016.11.013. View

Chen Q, Wu J, Li S, Lyu P, Wang Y, Li M . An ontology-driven, case-based clinical decision support model for removable partial denture design. Sci Rep. 2016; 6:27855. PMC: 4906524. DOI: 10.1038/srep27855. View

Zitzmann N, Matthisson L, Ohla H, Joda T . Digital Undergraduate Education in Dentistry: A Systematic Review. Int J Environ Res Public Health. 2020; 17(9). PMC: 7246576. DOI: 10.3390/ijerph17093269. View

Meyer P, Noblet V, Mazzara C, Lallement A . Survey on deep learning for radiotherapy. Comput Biol Med. 2018; 98:126-146. DOI: 10.1016/j.compbiomed.2018.05.018. View

10.

Bayrakdar S, Orhan K, Bayrakdar I, Bilgir E, Ezhov M, Gusarev M . A deep learning approach for dental implant planning in cone-beam computed tomography images. BMC Med Imaging. 2021; 21(1):86. PMC: 8132372. DOI: 10.1186/s12880-021-00618-z. View

11.

Currie G . Intelligent Imaging: Anatomy of Machine Learning and Deep Learning. J Nucl Med Technol. 2019; 47(4):273-281. DOI: 10.2967/jnmt.119.232470. View

12.

Lee J, Kim D, Jeong S, Choi S . Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm. J Periodontal Implant Sci. 2018; 48(2):114-123. PMC: 5944222. DOI: 10.5051/jpis.2018.48.2.114. View

13.

Hiraiwa T, Ariji Y, Fukuda M, Kise Y, Nakata K, Katsumata A . A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography. Dentomaxillofac Radiol. 2018; 48(3):20180218. PMC: 6476355. DOI: 10.1259/dmfr.20180218. View

14.

Joda T, Bornstein M, Jung R, Ferrari M, Waltimo T, Zitzmann N . Recent Trends and Future Direction of Dental Research in the Digital Era. Int J Environ Res Public Health. 2020; 17(6). PMC: 7143449. DOI: 10.3390/ijerph17061987. View

15.

Lee J, Kim D, Jeong S, Choi S . Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J Dent. 2018; 77:106-111. DOI: 10.1016/j.jdent.2018.07.015. View

16.

Hung K, Montalvao C, Tanaka R, Kawai T, Bornstein M . The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review. Dentomaxillofac Radiol. 2019; 49(1):20190107. PMC: 6957072. DOI: 10.1259/dmfr.20190107. View

17.

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):1234-1238. DOI: 10.1177/0022034519867641. View

18.

Shan T, Tay F, Gu L . Application of Artificial Intelligence in Dentistry. J Dent Res. 2020; 100(3):232-244. DOI: 10.1177/0022034520969115. View

19.

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. PMC: 8136280. DOI: 10.7717/peerj.11451. View

20.

Lee J, Jeong S . Efficacy of deep convolutional neural network algorithm for the identification and classification of dental implant systems, using panoramic and periapical radiographs: A pilot study. Medicine (Baltimore). 2020; 99(26):e20787. PMC: 7328970. DOI: 10.1097/MD.0000000000020787. View