DentShadeAI: a Framework for Automatic Dental Shade Matching through Mobile Phone Camera

Wanna, Yupaporn; Wiratchawa, Kannika; Leenaracharoongruang, Rattaporn; Sittiwong, Waraporn; Panpisut, Piyaphong; Intharah, Thanapong

doi:10.1109/itc-cscc55581.2022.9894968

Cited by 3 publications

(22 citation statements)

References 9 publications

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“…One study showed a high risk of bias for Question 8 with no standardization of flash in capturing the images of shade tabs using digital cameras 43 . All the studies showed a low risk of bias for Question 9 except for one study, with no mention of statistical analysis 46 …”

Section: Resultsmentioning

confidence: 98%

“…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%

“…Only two studies compared the performance of AI with visual shade matching 41,43 . The remaining studies compared the performance of shade matching with other AI algorithms 36,37,39,40,42,44,46,47 . Two studies had unclear control groups as they compared with color space models and another compared with different types of prostheses 32,35 .…”

Section: Resultsmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 98%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

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

Shetty,

Gali,

Augustine

et al. 2023

Journal of Prosthodontics

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“…Within each methylation level, photographs were taken. The reference shades were used to refer to the illumination conditions at the capture time as successfully demonstrated by DentShadeAI …”

Section: Methodsmentioning

confidence: 99%

“…The reference shades were used to refer to the illumination conditions at the capture time as successfully demonstrated by DentShadeAI. 38 To assess the methylation level in urine samples, three regression models were used: linear regression (LR), decision tree regression (DTR), and random forest regression (RFR). To create the feature vector, we retrieved red (R), green (G), and blue (B) values from three 200 × 200 pixel windows: two reference regions and one target region.…”

Section: Methylation Level Detection Assisted By MLmentioning

confidence: 99%

Dual-Responsive Carbon Quantum Dots for the Simultaneous Detection of Cytosine and 5-Methylcytosine Interpreted by a Machine Learning-Assisted Smartphone

Thonghlueng

Ngernpimai

Chuaephon

et al. 2023

ACS Appl. Mater. Interfaces

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DNA methylation is an epigenetic alteration that results in 5-methylcytosine (5-mC) through the addition of a methyl group to the fifth carbon of a cytosine (C) residue. The methylation level, the ratio of 5-mC to C, in urine might be related to the whole-body epigenetic status and the occurrence of common cancers. To date, never before have any nanomaterials been developed to simultaneously determine C and 5-mC in urine samples. Herein, a dual-responsive fluorescent sensor for the urinary detection of C and 5-mC has been developed. This assay relied on changes in the optical properties of nitrogen-doped carbon quantum dots (CQDs) prepared by microwave-assisted pyrolysis. In the presence of C, the blue-shifted fluorescence intensity of the CQDs increased. However, fluorescence quenching was observed upon the addition of 5-mC. This was primarily due to photoinduced electron transfer as confirmed by the density functional theory calculation. In urine samples, our sensitive fluorescent sensor had detection limits for C and 5-mC of 43.4 and 74.4 μM, respectively, and achieved satisfactory recoveries ranging from 103.5 to 115.8%. The simultaneous detection of C and 5-mC leads to effective methylation level detection, achieving recoveries in the range of 104.6−109.5%. Besides, a machine learningenabled smartphone was also developed, which can be effectively applied to the determination of methylation levels (0−100%). These results demonstrate a simple but very effective approach for detecting the methylation level in urine, which could have significant implications for predicting the clinical prognosis.

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