Dental Shade Matching Method Based on Hue, Saturation, Value Color Model with Machine Learning and Fuzzy Decision

Chen, Shih-Lun; Zhou, He-Sheng; Chen, Tsung‐Yi; Lee, Tsung‐Han; Chen, Chiung-An; Lin, Ting-Lan; Lin, Nung-Hsiang; Wang, Liang-Hung; Lin, Szu-Yin; Abu, Patricia Angela R.; Lin, Ming-Yi

doi:10.18494/sam.2020.2848

Cited by 8 publications

(22 citation statements)

References 32 publications

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“…Shade matching has been explored by a ML model with fuzzy decision making and has shown to improve the accuracy of colorimetric values compared with the traditional method to match shades using a shade guide 39 . However, the study did not compare their results to spectrophotometers 40…”

Section: Machine Learningmentioning

confidence: 99%

Promises and perils of artificial intelligence in dentistry

Pethani

2021

Australian Dental Journal

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Artificial intelligence (AI) is a subdiscipline of computer science that has made substantial progress in medicine and there is a growing body of AI research in dentistry. Dentists should have an understanding of the foundational concepts and the ability to critically evaluate dental research in AI. Machine learning (ML) is a subfield of AI that most dental AI research is dedicated to. The most prolific area of ML research is automated interpretation of dental imaging. Other areas include providing treatment recommendations, predicting future disease and treatment outcomes. The research impact is limited by small datasets that do not harness the positive correlation between very large datasets and ML performance. There is also a need to standardize research methodologies and utilize performance metrics that are appropriate for the clinical context. In addition to research challenges, this article discusses the ethical, legal and logistical considerations associated with implementation in clinical practice. This includes explainable AI, model bias, data privacy and security. The future implications of AI in dentistry involve a promise for a novel form of practicing dentistry however, the effect of AI on patient outcomes is yet to be determined.

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Section: Machine Learningmentioning

confidence: 99%

Promises and perils of artificial intelligence in dentistry

Pethani

2021

Australian Dental Journal

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“…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%

“…The peak signal-to-noise ratio was used to evaluate the above model in shade matching process. The peak signal-to-noise ratio was enhanced from 97.64 to 99.93 when the fuzzy decision model was applied [28]. One study used artificial models to observe teeth prognosis.…”

Section: Prediction Of Prosthodontic Treatmentmentioning

confidence: 99%

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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“…More and more cases have shown that the combination of artificial intelligence and medical imaging has good results, such as breast cancer (BC) [ 15 ], arrhythmia [ 16 ], and lung function prediction [ 17 ]. There are also related studies in dentistry, for example, adding machine learning to the color recognition of dentures [ 18 ] or adding deep learning to the detection of tooth decay [ 19 ]. Artificial intelligence was used to improve the apical lesion detection accuracy rate of the dental Panoramic Radiograph Identification System to about 75.53% in [ 20 ], while the accuracy rate of CT image identification is about 82% [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

A High-Accuracy Detection System: Based on Transfer Learning for Apical Lesions on Periapical Radiograph

et al. 2022

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Apical Lesions, one of the most common oral diseases, can be effectively detected in daily dental examinations by a periapical radiograph (PA). In the current popular endodontic treatment, most dentists spend a lot of time manually marking the lesion area. In order to reduce the burden on dentists, this paper proposes a convolutional neural network (CNN)-based regional analysis model for spical lesions for periapical radiographs. In this study, the database was provided by dentists with more than three years of practical experience, meeting the criteria for clinical practical application. The contributions of this work are (1) an advanced adaptive threshold preprocessing technique for image segmentation, which can achieve an accuracy rate of more than 96%; (2) a better and more intuitive apical lesions symptom enhancement technique; and (3) a model for apical lesions detection with an accuracy as high as 96.21%. Compared with existing state-of-the-art technology, the proposed model has improved the accuracy by more than 5%. The proposed model has successfully improved the automatic diagnosis of apical lesions. With the help of automation, dentists can focus more on technical and medical diagnoses, such as treatment, tooth cleaning, or medical communication. This proposal has been certified by the Institutional Review Board (IRB) with the certification number 202002030B0.

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Dental Shade Matching Method Based on Hue, Saturation, Value Color Model with Machine Learning and Fuzzy Decision

Cited by 8 publications

References 32 publications

Promises and perils of artificial intelligence in dentistry

Promises and perils of artificial intelligence in dentistry

The Potential of Artificial Intelligence in Prosthodontics: A Comprehensive Review

A High-Accuracy Detection System: Based on Transfer Learning for Apical Lesions on Periapical Radiograph

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