Artificial intelligence systems in dental shade‐matching: A systematic review

Abstract Purpose Uses for artificial intelligence (AI) are being explored in contemporary dentistry, but artificial intelligence in dental shade‐matching has not been systematically reviewed and evaluated. The purpose of this systematic review was to evaluate the accuracy of artificial intelligence in predicting dental shades in restorative dentistry. Methods A systematic electronic search was performed with the databases MEDLINE (PubMed), Scopus, Cochrane Library, and Google Scholar. A manual search was also conducted. All titles and abstracts were subject to the inclusion criteria of observational, interventional studies, and studies published in the English language. Narrative reviews, systematic reviews, case reports, case series, letters to the editor, commentaries, studies that were not AI‐based, studies that were not related to dentistry, and studies that were related to other disciplines in dentistry, other than restorative dentistry (prosthodontics and endodontics) were excluded. Two investigators independently evaluated the quality assessment of the studies by applying the Joanna Briggs Institute Critical Appraisal Checklist for Quasi‐Experimental Studies (non‐randomized experimental studies). A third investigator was consulted to resolve the lack of consensus. Results Fifty‐three articles were initially found from all the searches combined from articles published from 2008 till March 2023. A total of 15 articles met the inclusion criteria and were included in the systematic review. AI algorithms for shade‐matching include fuzzy logic, a genetic algorithm with back‐propagation neural network, back‐propagation neural networks, convolutional neural networks, artificial neural networks, support vector machine algorithms, K‐nearest neighbor with decision tree and random forest, deep learning for detection of dental prostheses based on object‐detection applications, You Only Look Once‐YOLO. Moment invariant was used for feature extraction. XG (Xtreme Gradient) Boost was used in one study as a gradient‐boosting machine learning algorithm. The highest accuracy in the prediction of dental shades was the decision tree regression model for leucite‐based dental ceramics of 99.7% followed by the fuzzy decision of 99.62%, and support vector machine using cross‐validation of 97%. Conclusions Lighting conditions, shade‐matching devices and color space models, and the type of AI algorithm influence the accuracy of the prediction of dental shades. Knowledge‐based systems and neural networks have shown better accuracy in predicting dental shades.