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Artificial Intelligence & Robotics in Dental Medicine

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (20 December 2022) | Viewed by 34318

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Special Issue Editor

Prof. Dr. Tim Joda

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Guest Editor

Head of eHealth in Reconstructive Dentistry, Clinic of Reconstructive Dentistry, Center for Dental Medicine, University of Zurich, 8006 Zürich, Switzerland
Interests: reconstructive dentistry; prosthodontics; implant dentistry; digital technology; dental materials; augmented/virtual reality; artificial intelligence; big data & eHealth; public health; translational research
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Special Issue Information

Dear Colleagues,

This Special Issue focuses on innovations built on Artificial Intelligence as the center technology influencing dental eHealth data management plus services and operations as well as dental Robotics for clinical and technical healthcare applications.

Artificial Intelligence systems enable personalized dental medicine workflows by analyzing all eHealth data gathered from an individual patient. In addition to dental-specific data, this also includes genomic, proteomic, and metabolomic information obtained from biosensors and therefore facilitates a holistic approach for personalized treatment strategies, patient monitoring, and risk management. Based on the power of Artificial Intelligence, the frame of data <> healthcare <> service is supplemented by technological advancements in the field of biomarker analysis and dental Robotics.

Innovation continues to be critical to tackle dental problems until its routine implementation based on sound scientific evidence. Novel technologies must be viewed critically in relation to the cost–benefit ratio and the ethical implications of a misleading diagnosis or treatment produced by software algorithms or robotic performance. The focus on patient-centered research and the development of precision dentistry has the potential to improve individual and public health as well as clarify the interconnectivity of disease in a more cost-effective way.

The objective of this Special Issue is to provide an update on the current knowledge with state-of-the-art theory and practical information on Artificial Intelligence and Robotics to determine the uptake of technological innovation in dentistry. In addition, emphasis is placed on identifying future research needs to manage the continuous increase in digitalization in combination with dental diagnostics and new treatment protocols in order to accomplish their clinical translation.

This Special Issue welcomes original research articles presenting clinical and laboratory trials, systematic reviews following the PRISMA guidelines, and communication articles considering the perspectives of the various stakeholders with regard to Artificial Intelligence and Robotics in dental medicine. Please check the online instructions for authors prior to submission: https://www.mdpi.com/journal/sensors/instructions#submission.

Prof. Dr. Tim Joda
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Dental eHealth and big data
Biosensors
Prosthodontics
Implant dentistry
Orthodontics
Digital technology
Radiology
3D imaging
Translational research

Published Papers (6 papers)

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Research

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12 pages, 4882 KiB

Open AccessCommunication

Dental Erosion Evaluation with Intact-Tooth Smartphone Application: Preliminary Clinical Results from September 2019 to March 2022

by Andrea Butera, Carolina Maiorani, Simone Gallo, Maurizio Pascadopoli, Sergio Buono and Andrea Scribante

Sensors 2022, 22(14), 5133; https://doi.org/10.3390/s22145133 - 08 Jul 2022

Cited by 4 | Viewed by 2153

Abstract

Dental erosion is a process of deterioration of the dental hard tissue; it is estimated that about 30% of permanent teeth are affected in adolescence. The Intact-Tooth application allows for the better estimation of the problem, inserting itself in the diagnosis process, and better care and prevention for the patient. It provides him with scientifically validated protocols, which the patient can consult at any time. The purpose of this report was to conduct an initial evaluation on the use of the application, which has been available since September 2019: the analysis of the collected data allowed the first investigation of the incidence of the problem and the degree of susceptibility in the registered patients. Photos of 3894 patients with dental erosion were uploaded, through which the degree of susceptibility and the BEWE (basic erosive wear examination index) index could be assessed; of these, 99.72% had a susceptibility grade of 0 to 8, while 0.28% had a medium-high susceptibility grade; this result is related to the age and sex of the patients. The management of patients through the help of the application could promote the diagnosis and treatment of enamel diseases and encourage the self-learning of the learning machine, thanks to the number of clinical cases uploaded. Full article

(This article belongs to the Special Issue Artificial Intelligence & Robotics in Dental Medicine)

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18 pages, 7199 KiB

Open AccessArticle

The Accuracy and Reliability of Tooth Shade Selection Using Different Instrumental Techniques: An In Vitro Study

by Nattapong Sirintawat, Tanyaporn Leelaratrungruang, Pongsakorn Poovarodom, Sirichai Kiattavorncharoen and Parinya Amornsettachai

Sensors 2021, 21(22), 7490; https://doi.org/10.3390/s21227490 - 11 Nov 2021

Cited by 13 | Viewed by 4445

Abstract

This study aimed to investigate and compare the reliability and accuracy of tooth shade selection in the model using 30 milled crowns via five methods: (1) digital single-lens reflex (DSLR) camera with twin flash (TF) and polarized filter (DSLR + TF), (2) DSLR camera with a ring flash (RF) and polarized filter (DSLR + RF), (3) smartphone camera with light corrector and polarized filter (SMART), (4) intraoral scanner (IOS), and (5) spectrophotometer (SPEC). These methods were compared with the control group or manufacturer’s shade. The CIE Lab values (L, a, and b values) were obtained from five of the methods to indicate the color of the tooth. Adobe Photoshop was used to generate CIE Lab values from the digital photographs. The reliability was calculated from the intraclass correlation based on two repetitions. The accuracy was calculated from; (a) ΔE calculated by the formula comparing each method to the control group, (b) study and control groups were analyzed by using the Kruskal–Wallis test, and (c) the relationship between study and control groups were calculated using Spearman’s correlation. The reliability of the intraclass correlation of L, a, and b values obtained from the five methods showed satisfactory correlations ranging from 0.732–0.996, 0.887–0.994, and 0.884–0.999, respectively. The ΔE from all groups had statistically significant differences when compared to the border of clinical acceptance (ΔE = 6.8). The ΔE from DSLR + TF, DSLR + RF, SMART, and SPEC were higher than clinical acceptance (ΔE > 6.8), whereas the ΔE from IOS was 5.96 and all of the L, a, and b values were not statistically significantly different from the manufacturer’s shade (p < 0.01). The ΔE of the DSLR + RF group showed the least accuracy (ΔE = 19.98), whereas the ∆E of DSLR + TF, SMART, and SPEC showed similar accuracy ∆E (ΔE = 10.90, 10.57, and 11.57, respectively). The DSLR camera combined with a ring flash system and polarized filter provided the least accuracy. The intraoral scanner provided the highest accuracy. However, tooth shade selection deserves the combination of various techniques and a professional learning curve to establish the most accurate outcome. Full article

(This article belongs to the Special Issue Artificial Intelligence & Robotics in Dental Medicine)

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17 pages, 3748 KiB

Open AccessArticle

Detection of Dental Apical Lesions Using CNNs on Periapical Radiograph

by Chun-Wei Li, Szu-Yin Lin, He-Sheng Chou, Tsung-Yi Chen, Yu-An Chen, Sheng-Yu Liu, Yu-Lin Liu, Chiung-An Chen, Yen-Cheng Huang, Shih-Lun Chen, Yi-Cheng Mao, Patricia Angela R. Abu, Wei-Yuan Chiang and Wen-Shen Lo

Sensors 2021, 21(21), 7049; https://doi.org/10.3390/s21217049 - 24 Oct 2021

Cited by 22 | Viewed by 7271

Abstract

Apical lesions, the general term for chronic infectious diseases, are very common dental diseases in modern life, and are caused by various factors. The current prevailing endodontic treatment makes use of X-ray photography taken from patients where the lesion area is marked manually, which is therefore time consuming. Additionally, for some images the significant details might not be recognizable due to the different shooting angles or doses. To make the diagnosis process shorter and efficient, repetitive tasks should be performed automatically to allow the dentists to focus more on the technical and medical diagnosis, such as treatment, tooth cleaning, or medical communication. To realize the automatic diagnosis, this article proposes and establishes a lesion area analysis model based on convolutional neural networks (CNN). For establishing a standardized database for clinical application, the Institutional Review Board (IRB) with application number 202002030B0 has been approved with the database established by dentists who provided the practical clinical data. In this study, the image data is preprocessed by a Gaussian high-pass filter. Then, an iterative thresholding is applied to slice the X-ray image into several individual tooth sample images. The collection of individual tooth images that comprises the image database are used as input into the CNN migration learning model for training. Seventy percent (70%) of the image database is used for training and validating the model while the remaining 30% is used for testing and estimating the accuracy of the model. The practical diagnosis accuracy of the proposed CNN model is 92.5%. The proposed model successfully facilitated the automatic diagnosis of the apical lesion. Full article

(This article belongs to the Special Issue Artificial Intelligence & Robotics in Dental Medicine)

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12 pages, 743 KiB

Open AccessArticle

Classification of Approximal Caries in Bitewing Radiographs Using Convolutional Neural Networks

by Maira Moran, Marcelo Faria, Gilson Giraldi, Luciana Bastos, Larissa Oliveira and Aura Conci

Sensors 2021, 21(15), 5192; https://doi.org/10.3390/s21155192 - 31 Jul 2021

Cited by 35 | Viewed by 4591

Abstract

Dental caries is an extremely common problem in dentistry that affects a significant part of the population. Approximal caries are especially difficult to identify because their position makes clinical analysis difficult. Radiographic evaluation—more specifically, bitewing images—are mostly used in such cases. However, incorrect interpretations may interfere with the diagnostic process. To aid dentists in caries evaluation, computational methods and tools can be used. In this work, we propose a new method that combines image processing techniques and convolutional neural networks to identify approximal dental caries in bitewing radiographic images and classify them according to lesion severity. For this study, we acquired 112 bitewing radiographs. From these exams, we extracted individual tooth images from each exam, applied a data augmentation process, and used the resulting images to train CNN classification models. The tooth images were previously labeled by experts to denote the defined classes. We evaluated classification models based on the Inception and ResNet architectures using three different learning rates: 0.1, 0.01, and 0.001. The training process included 2000 iterations, and the best results were achieved by the Inception model with a 0.001 learning rate, whose accuracy on the test set was 73.3%. The results can be considered promising and suggest that the proposed method could be used to assist dentists in the evaluation of bitewing images, and the definition of lesion severity and appropriate treatments. Full article

(This article belongs to the Special Issue Artificial Intelligence & Robotics in Dental Medicine)

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Review

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15 pages, 307 KiB

Open AccessReview

Dental Robotics: A Disruptive Technology

by Paras Ahmad, Mohammad Khursheed Alam, Ali Aldajani, Abdulmajeed Alahmari, Amal Alanazi, Martin Stoddart and Mohammed G. Sghaireen

Sensors 2021, 21(10), 3308; https://doi.org/10.3390/s21103308 - 11 May 2021

Cited by 29 | Viewed by 7711

Abstract

Robotics is a disruptive technology that will change diagnostics and treatment protocols in dental medicine. Robots can perform repeated workflows for an indefinite length of time while enhancing the overall quality and quantity of patient care. Early robots required a human operator, but robotic systems have advanced significantly over the past decade, and the latest medical robots can perform patient intervention or remote monitoring autonomously. However, little research data on the therapeutic reliability and precision of autonomous robots are available. The present paper reviews the promise and practice of robots in dentistry by evaluating published work on commercial robot systems in dental implantology, oral and maxillofacial surgery, prosthetic and restorative dentistry, endodontics, orthodontics, oral radiology as well as dental education. In conclusion, this review critically addresses the current limitations of dental robotics and anticipates the potential future impact on oral healthcare and the dental profession. Full article

(This article belongs to the Special Issue Artificial Intelligence & Robotics in Dental Medicine)

Other

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11 pages, 504 KiB

Open AccessSystematic Review

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

by Selina A. Bernauer, Nicola U. Zitzmann and Tim Joda

Sensors 2021, 21(19), 6628; https://doi.org/10.3390/s21196628 - 05 Oct 2021

Cited by 35 | Viewed by 6693

Abstract

(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. Full article

(This article belongs to the Special Issue Artificial Intelligence & Robotics in Dental Medicine)

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