Abstract
Statement of problem
Artificial intelligence applications are increasing in prosthodontics. Still, the
current development and performance of artificial intelligence in prosthodontic applications
has not yet been systematically documented and analyzed.
Purpose
The purpose of this systematic review was to assess the performance of the artificial
intelligence models in prosthodontics for tooth shade selection, automation of restoration
design, mapping the tooth preparation finishing line, optimizing the manufacturing
casting, predicting facial changes in patients with removable prostheses, and designing
removable partial dentures.
Material and methods
An electronic systematic review was performed in MEDLINE/PubMed, EMBASE, Web of Science,
Cochrane, and Scopus. A manual search was also conducted. Studies with artificial
intelligence models were selected based on 6 criteria: tooth shade selection, automated
fabrication of dental restorations, mapping the finishing line of tooth preparations,
optimizing the manufacturing casting process, predicting facial changes in patients
with removable prostheses, and designing removable partial dentures. Two investigators
independently evaluated the quality assessment of the studies by applying the Joanna
Briggs Institute Critical Appraisal Checklist for Quasi-Experimental Studies (nonrandomized
experimental studies). A third investigator was consulted to resolve lack of consensus.
Results
A total of 36 articles were reviewed and classified into 6 groups based on the application
of the artificial intelligence model. One article reported on the development of an
artificial intelligence model for tooth shade selection, reporting better shade matching
than with conventional visual selection; 14 articles reported on the feasibility of
automated design of dental restorations using different artificial intelligence models;
1 artificial intelligence model was able to mark the margin line without manual interaction
with an average accuracy ranging from 90.6% to 97.4%; 2 investigations developed artificial
intelligence algorithms for optimizing the manufacturing casting process, reporting
an improvement of the design process, minimizing the porosity on the cast metal, and
reducing the overall manufacturing time; 1 study proposed an artificial intelligence
model that was able to predict facial changes in patients using removable prostheses;
and 17 investigations that developed clinical decision support, expert systems for
designing removable partial dentures for clinicians and educational purposes, computer-aided
learning with video interactive programs for student learning, and automated removable
partial denture design.
Conclusions
Artificial intelligence models have shown the potential for providing a reliable diagnostic
tool for tooth shade selection, automated restoration design, mapping the preparation
finishing line, optimizing the manufacturing casting, predicting facial changes in
patients with removable prostheses, and designing removable partial dentures, but
they are still in development. Additional studies are needed to further develop and
assess their clinical performance.
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Article info
Publication history
Published online: July 16, 2021
Publication stage
In Press Corrected ProofFootnotes
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
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© 2021 by the Editorial Council for the Journal of Prosthetic Dentistry.
