Evaluation of a Novel Computer Color Matching System Based on the Improved Back‐Propagation Neural Network Model

Wei, Jinxin; Peng, Mengdong; Li, Qing; Wang, Yining

doi:10.1111/jopr.12561

Cited by 29 publications

(43 citation statements)

References 31 publications

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“…6 Deep learning systems, one of the methods of AI for various clinical tasks, has been introduced in recent years. 7 They were used effectively in various fields for image-based automated diagnosis, including lung cancer, 8 colorectal polyps, 9 prostate cancer, 10 hip osteoarthritis, 11 bone age assessment, 12 caries diagnosis, 13 color selection, 14 removable partial denture design, 15,16 temporomandibular disorders, 17,18 orthognathic treatment, 19 maxillary sinusitis, 20 root morphology, 21 periodontal diseases, 22 oral cancer, 23 periapical lesions, 24 radiolucent lesions, 25 cystic lesions, 26 cephalometric analysis, 27 and debonding of computer-aided design/computer-aided manufacturing (CAD/CAM) crowns. 28 Future systems are expected to be increasingly autonomous, going beyond recommending possible clinical actions to perform certain tasks independently, such as patient trial and screening references.…”

Section: Introductionmentioning

confidence: 99%

Attitudes and perceptions of dental students towards artificial intelligence

Yüzbaşıoğlu

2020

Journal of Dental Education

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IntroductionArtificial Intelligence (AI) is a burning topic and use of AI in our day‐to‐day life has increased exponentially. The purpose of this study was to evaluate the attitudes and perceptions of Turkish dental students towards AI and to provide information about their opinions to use of AI in dentistry.MethodsA 22‐question survey was distributed through Google Forms to all dental students at 9 Turkish dental schools in March 2019. It consisted of various sections aiming to evaluate the dental students’ knowledge and attitudes toward AI and possible applications in dentistry. Respondents’ anonymity was ensured.ResultsA total of 1103 students (650 female, 453 male; mean age was 21.36 ± 1.93 years) responded to the questionnaire for a 21.69% response rate. Of these, about 48.40% had basic knowledge about AI technologies and 10.6% stated that they had no information sources about AI. 85.70% agreed that dentistry would be revolutionized by AI but 28.60% did not agree that AI could replace themselves in the near future. In addition, 74.60% and 79.80% of agreed on topics about AI to be included in undergraduate and postgraduate dental education, respectively.ConclusionsAlthough participants have insufficient knowledge of AI, they are willing to improve their knowledge in this field. Participants shared an optimistic views and thought that AI will have a positive impact on dental future practice. Follow‐up surveys and multinational studies should be conducted to further explore these issues.

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Section: Introductionmentioning

confidence: 99%

Attitudes and perceptions of dental students towards artificial intelligence

Yüzbaşıoğlu

2020

Journal of Dental Education

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“…The seven included full-text articles came from diverse areas of the field of prosthodontics and demonstrated a wide range of the use of AI. Six studies investigated the training and application of different AI systems [ 19 , 22 , 23 , 24 , 25 , 26 ] and one study explored the function of an intrinsic AI system of a specific CAD software for designing prosthetic reconstructions [ 21 ]. Direct comparisons of the identified and included studies were not feasible because of the heterogeneity of the specific aims, defined outcomes, and topics within the field of reconstructive dentistry.…”

Section: Resultsmentioning

confidence: 99%

The Use and Performance of Artificial Intelligence in Prosthodontics: A Systematic Review

Bernauer

Zitzmann

Joda

2021

Sensors

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(1) Background: The rapid pace of digital development in everyday life is also reflected in dentistry, including the emergence of the first systems based on artificial intelligence (AI). This systematic review focused on the recent scientific literature and provides an overview of the application of AI in the dental discipline of prosthodontics. (2) Method: According to a modified PICO-strategy, an electronic (MEDLINE, EMBASE, CENTRAL) and manual search up to 30 June 2021 was carried out for the literature published in the last five years reporting the use of AI in the field of prosthodontics. (3) Results: 560 titles were screened, of which 30 abstracts and 16 full texts were selected for further review. Seven studies met the inclusion criteria and were analyzed. Most of the identified studies reported the training and application of an AI system (n = 6) or explored the function of an intrinsic AI system in a CAD software (n = 1). (4) Conclusions: While the number of included studies reporting the use of AI was relatively low, the summary of the obtained findings by the included studies represents the latest AI developments in prosthodontics demonstrating its application for automated diagnostics, as a predictive measure, and as a classification or identification tool. In the future, AI technologies will likely be used for collecting, processing, and organizing patient-related datasets to provide patient-centered, individualized dental treatment.

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“…Table 2 shows the AI methodologies and applications in dentistry reported in the included studies (120), which are also organized based on their data type. Table 3 shows a glossary with AI and ML terms used along the manuscript.…”

Section: Methodsmentioning

confidence: 99%

Applications of artificial intelligence in dentistry: A comprehensive review

Carrillo-Pérez

Pecho

Morales

et al. 2021

J Esthet Restor Dent

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Objective To perform a comprehensive review of the use of artificial intelligence (AI) and machine learning (ML) in dentistry, providing the community with a broad insight on the different advances that these technologies and tools have produced, paying special attention to the area of esthetic dentistry and color research. Materials and methods The comprehensive review was conducted in MEDLINE/PubMed, Web of Science, and Scopus databases, for papers published in English language in the last 20 years. Results Out of 3871 eligible papers, 120 were included for final appraisal. Study methodologies included deep learning (DL; n = 76), fuzzy logic (FL; n = 12), and other ML techniques (n = 32), which were mainly applied to disease identification, image segmentation, image correction, and biomimetic color analysis and modeling. Conclusions The insight provided by the present work has reported outstanding results in the design of high‐performance decision support systems for the aforementioned areas. The future of digital dentistry goes through the design of integrated approaches providing personalized treatments to patients. In addition, esthetic dentistry can benefit from those advances by developing models allowing a complete characterization of tooth color, enhancing the accuracy of dental restorations. Clinical significance The use of AI and ML has an increasing impact on the dental profession and is complementing the development of digital technologies and tools, with a wide application in treatment planning and esthetic dentistry procedures.

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Evaluation of a Novel Computer Color Matching System Based on the Improved Back‐Propagation Neural Network Model

Abstract: The novel CCM system produced greater accuracy in color reproduction within the given color space than the traditional visual approach.

Cited by 29 publications

References 31 publications

Attitudes and perceptions of dental students towards artificial intelligence

Attitudes and perceptions of dental students towards artificial intelligence

The Use and Performance of Artificial Intelligence in Prosthodontics: A Systematic Review

Applications of artificial intelligence in dentistry: A comprehensive review

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