Evaluation of Dental Prosthesis Colors Using a Neural Network

Ueki, Keisuke; Wakamatsu, Hayata; Hagiwara, Yasuaki

doi:10.1109/icsip49896.2020.9339381

Cited by 4 publications

(26 citation statements)

References 4 publications

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“…In the same context, a neural network model was used to automate the process of skilled dental technicians determining tooth color from pictures of patients. The study confirmed the effectiveness of the methodology proposed [27].…”

Section: Prediction Of Prosthodontic Treatmentsupporting

confidence: 73%

“…One of the useful methods for shade matching is using patient photographs by dental technicians; however, this could be affected by the subjectivity of technicians. A neural network model was used to automate the shade predicting process by expert dental technicians identifying patient photographs' tooth colors [27]. Some AI models were also compared among themselves, and the stable prediction ability of dental porcelain color matching from GA+BP was better than that of the BPNN model [5].…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, 5 studies from the selected studies looked at shade matching and prediction [5,8,[26][27][28]. Justiawan et al [29] used digital transformation, K-Nearest Neighbors (KNN), and neural network models to predict shades and color matching process.…”

Section: Prediction Of Prosthodontic Treatmentmentioning

confidence: 99%

“…Results of the Quadas-2 assessment of included studies are presented in Table 6. A total of 18.3% of studies (7/38) were identified as low-risk of bias studies [8,25,29,33,43,45,46], whereas 52.6% (20/38) and 28.9% (11/38) of included studies were identified as studies with high [12-15,17-19,20-22,26,28,30,34-38, 42,44] and unclear [4,5,16,23,24,27,31,32,39,40,46] risk of bias, respectively. Inter-rater and intra-rater reliability of the Cohen kappa were 0.8831 and 0.9232, respectively, which reflect near-perfect reliability.…”

Section: Quality Assessmentmentioning

confidence: 99%

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The Potential of Artificial Intelligence in Prosthodontics: A Comprehensive Review

Aljulayfi,

Almatrafi,

Althubaitiy

et al. 2024

Med Sci Monit

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Prosthodontics is a dental subspecialty that includes the preparation of dental prosthetics for missing or damaged teeth. It increasingly uses computer-assisted technologies for planning and preparing dental prosthetics. This study aims to present the findings from a systematic review of publications on artificial intelligence (AI) in prosthodontics to identify current trends and future opportunities. The review question was "What are the applications of AI in prosthodontics and how good is their performance in prosthodontics?" Electronic searching in the Web of Science, ScienceDirect, PubMed, and Cochrane Library was conducted. The search was limited to full text from January 2012 to January 2024. Quadas-2 was used for assessing quality and potential risk of bias for the selected studies. A total of 1925 studies were identified in the initial search. After removing the duplicates and applying exclusion criteria, a total of 30 studies were selected for this review. Results of the Quadas-2 assessment of included studies found that a total of 18.3% of studies were identified as low risk of bias studies, whereas 52.6% and 28.9% of included studies were identified as studies with high and unclear risk of bias, respectively. Although they are still developing, AI models have already shown promise in the areas of dental charting, tooth shade selection, automated restoration design, mapping the preparation finishing line, manufacturing casting optimization, predicting facial changes in patients wearing removable prostheses, and designing removable partial dentures.

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Section: Prediction Of Prosthodontic Treatmentsupporting

confidence: 73%

Section: Discussionmentioning

confidence: 99%

Section: Prediction Of Prosthodontic Treatmentmentioning

confidence: 99%

Section: Quality Assessmentmentioning

confidence: 99%

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The Potential of Artificial Intelligence in Prosthodontics: A Comprehensive Review

Aljulayfi,

Almatrafi,

Althubaitiy

et al. 2024

Med Sci Monit

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“…The included articles were published between 2010–2023. Most of the studies were from Asia‐Pacific countries such as Japan, China, Korea, Taiwan, Indonesia, and Thailand and the rest were from USA, Turkey, and Spain 35–47 …”

Section: Resultsmentioning

confidence: 99%

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

Shetty,

Gali,

Augustine

et al. 2023

Journal of Prosthodontics

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PurposeUses 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.MethodsA 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.ResultsFifty‐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%.ConclusionsLighting 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.

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