Accuracy of Artificial Intelligence-Based Photographic Detection of Gingivitis

Development of Artificial Intelligence Models for Tooth Numbering and Detection: A Systematic Review

Dental radiography is widely used in dental practices and offers a valuable resource for the development of AI technology. Consequently, many researchers have been drawn to explore its application in different areas. The current systematic review was undertaken to critically appraise developments and performance of artificial intelligence (AI) models designed for tooth numbering and detection using dento-maxillofacial radiographic images. In order to maintain the integrity of their methodology, the authors of this systematic review followed the diagnostic test accuracy criteria outlined in PRISMA-DTA. Electronic search was done by navigating through various databases such as PubMed, Scopus, Embase, Cochrane, Web of Science, Google Scholar, and the Saudi Digital Library for the articles published from 2018 to 2023. Sixteen articles that met the inclusion exclusion criteria were subjected to risk of bias assessment using QUADAS-2 and certainty of evidence was assessed using GRADE approach.AI technology has been mainly applied for automated tooth detection and numbering, to detect teeth in CBCT images, to identify dental treatment patterns and approaches. The AI models utilised in the studies included exhibited a highest precision of 99.4% for tooth detection and 98% for tooth numbering. The use of AI as a supplementary diagnostic tool in the field of dental radiology holds great potential.

Toward Digital Periodontal Health: Recent Advances and Future Perspectives

Periodontal diseases, ranging from gingivitis to periodontitis, are prevalent oral diseases affecting over 50% of the global population. These diseases arise from infections and inflammation of the gums and supporting bones, significantly impacting oral health. The established link between periodontal diseases and systemic diseases, such as cardiovascular diseases, underscores their importance as a public health concern. Consequently, the early detection and prevention of periodontal diseases have become critical objectives in healthcare, particularly through the integration of advanced artificial intelligence (AI) technologies. This paper aims to bridge the gap between clinical practices and cutting-edge technologies by providing a comprehensive review of current research. We examine the identification of causative factors, disease progression, and the role of AI in enhancing early detection and treatment. Our goal is to underscore the importance of early intervention in improving patient outcomes and to stimulate further interest among researchers, bioengineers, and AI specialists in the ongoing exploration of AI applications in periodontal disease diagnosis.

Artificial Intelligence-Related Dental Research: Bibliometric and Altmetric Analysis

BACKGROUND

Recent years have witnessed an explosive surge in dental research related to artificial intelligence (AI). These applications have optimised dental workflows, demonstrating significant clinical importance. Understanding the current landscape and trends of this topic is crucial for both clinicians and researchers to utilise and advance this technology. However, a comprehensive scientometric study regarding this field had yet to be performed.

METHODS

A literature search was conducted in the Web of Science Core Collection database to identify eligible "research articles" and "reviews." Literature screening and exclusion were performed by 2 investigators. Thereafter, VOSviewer was utilised in co-occurrence analysis and CiteSpace in co-citation analysis. R package Bibliometrix was employed to automatically calculate scientific impacts, determining the core authors and journals. Altmetric data were described narratively and supplemented with Spearman correlation analysis.

RESULTS

A total of 1558 research publications were included. During the past 5 years, AI-related dental publications drastically increased in number, from 36 to 581. Diagnostics and Scientific Reports published the most articles, whereas Journal of Dental Research received the highest number of citations per article. China, the US, and South Korea emerged as the most prolific countries, whilst Germany received the highest number of citations per article (23.29). Charité Universitätsmedizin Berlin was the institution with the highest number of publications and citations per article (29.16). Altmetric Attention Score was correlated with News Mentions (P < .001), and significant associations were observed amongst Dimension Citations, Mendeley Readers, and Web of Science Citations (P < .001).

CONCLUSIONS

The publication numbers regarding AI-related dental research have been rising rapidly and may continue their upwards trend. China, the US, South Korea, and Germany had promoted the progress of AI-related dental research. Disease diagnosis, orthodontic applications, and morphology segmentation were current hotspots. Attention mechanism, explainable AI, multimodal data fusion, and AI-generated text assistants necessitate future research and exploration.

Machine learning model to predict the width of maxillary central incisor from anthropological measurements

PURPOSE

To improve smile esthetics, clinicians should comprehensively analyze the face and ensure that the sizes selected for the maxillary anterior teeth are compatible with the available anthropological measurements. The inter commissural (ICW), interalar (IAW), intermedial-canthus (MCW), interlateral-canthus (LCW), and interpupillary (IPW) widths are used to determine the width of maxillary central incisors (CW). The aim of this study was to develop an automated approach using machine learning (ML) algorithms to predict central incisor width in a young Turkish population using anthropological measurements. This automation can contribute to digital dentistry and clinical decision-making.

METHODS

In the initial phase of this cross-sectional study, several ML regression models-including multiple linear regression (MLR), multi-layer-perceptron (MLP), decision-tree (DT), and random forest (RF) models-were validated to confirm the central width prediction accuracy. Datasets containing only male and female measurements, as well as combined were considered for ML model implementation, and the performance of each model was evaluated for an unbiased population dataset.

RESULTS

Compared with the other algorithms, the RF algorithm showed improved performance for all cases, with an accuracy of 96%, which represents the percentage of correct predictions. The plot reveals the applicability of the RF model in predicting the CW from anthropological measurements irrespective of the candidate's sex.

CONCLUSIONS

These results demonstrated the possibility of predicting central incisor widths based on anthropometric measurements using ML models. The accurate central incisor width prediction from these trials also indicates the applicability of the proposed model to be deployed for enhanced clinical decision-making.

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

PURPOSE

Uses 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.

METHODS

A 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.

RESULTS

Fifty-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%.

CONCLUSIONS

Lighting 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.

Empowering Modern Dentistry: The Impact of Artificial Intelligence on Patient Care and Clinical Decision Making

Advancements in artificial intelligence (AI) are poised to catalyze a transformative shift across diverse dental disciplines including endodontics, oral radiology, orthodontics, pediatric dentistry, periodontology, prosthodontics, and restorative dentistry. This narrative review delineates the burgeoning role of AI in enhancing diagnostic precision, streamlining treatment planning, and potentially unveiling innovative therapeutic modalities, thereby elevating patient care standards. Recent analyses corroborate the superiority of AI-assisted methodologies over conventional techniques, affirming their capacity for personalization, accuracy, and efficiency in dental care. Central to these AI applications are convolutional neural networks and deep learning models, which have demonstrated efficacy in diagnosis, prognosis, and therapeutic decision making, in some instances surpassing traditional methods in complex cases. Despite these advancements, the integration of AI into clinical practice is accompanied by challenges, such as data security concerns, the demand for transparency in AI-generated outcomes, and the imperative for ongoing validation to establish the reliability and applicability of AI tools. This review underscores the prospective benefits of AI in dental practice, envisioning AI not as a replacement for dental professionals but as an adjunctive tool that fortifies the dental profession. While AI heralds improvements in diagnostics, treatment planning, and personalized care, ethical and practical considerations must be meticulously navigated to ensure responsible development of AI in dentistry.

Radiographic diagnosis of periodontal diseases - Current evidence versus innovations

Accurate diagnosis of periodontal and peri-implant diseases relies significantly on radiographic examination, especially for assessing alveolar bone levels, bone defect morphology, and bone quality. This narrative review aimed to comprehensively outline the current state-of-the-art in radiographic diagnosis of alveolar bone diseases, covering both two-dimensional (2D) and three-dimensional (3D) modalities. Additionally, this review explores recent technological advances in periodontal imaging diagnosis, focusing on their potential integration into clinical practice. Clinical probing and intraoral radiography, while crucial, encounter limitations in effectively assessing complex periodontal bone defects. Recognizing these challenges, 3D imaging modalities, such as cone beam computed tomography (CBCT), have been explored for a more comprehensive understanding of periodontal structures. The significance of the radiographic assessment approach is evidenced by its ability to offer an objective and standardized means of evaluating hard tissues, reducing variability associated with manual clinical measurements and contributing to a more precise diagnosis of periodontal health. However, clinicians should be aware of challenges related to CBCT imaging assessment, including beam-hardening artifacts generated by the high-density materials present in the field of view, which might affect image quality. Integration of digital technologies, such as artificial intelligence-based tools in intraoral radiography software, the enhances the diagnostic process. The overarching recommendation is a judicious combination of CBCT and digital intraoral radiography for enhanced periodontal bone assessment. Therefore, it is crucial for clinicians to weigh the benefits against the risks associated with higher radiation exposure on a case-by-case basis, prioritizing patient safety and treatment outcomes.

Performance of Generative Artificial Intelligence in Dental Licensing Examinations

OBJECTIVES

Generative artificial intelligence (GenAI), including large language models (LLMs), has vast potential applications in health care and education. However, it is unclear how proficient LLMs are in interpreting written input and providing accurate answers in dentistry. This study aims to investigate the accuracy of GenAI in answering questions from dental licensing examinations.

METHODS

A total of 1461 multiple-choice questions from question books for the US and the UK dental licensing examinations were input into 2 versions of ChatGPT 3.5 and 4.0. The passing rates of the US and UK dental examinations were 75.0% and 50.0%, respectively. The performance of the 2 versions of GenAI in individual examinations and dental subjects was analysed and compared.

RESULTS

ChatGPT 3.5 correctly answered 68.3% (n = 509) and 43.3% (n = 296) of questions from the US and UK dental licensing examinations, respectively. The scores for ChatGPT 4.0 were 80.7% (n = 601) and 62.7% (n = 429), respectively. ChatGPT 4.0 passed both written dental licensing examinations, whilst ChatGPT 3.5 failed. ChatGPT 4.0 answered 327 more questions correctly and 102 incorrectly compared to ChatGPT 3.5 when comparing the 2 versions.

CONCLUSIONS

The newer version of GenAI has shown good proficiency in answering multiple-choice questions from dental licensing examinations. Whilst the more recent version of GenAI generally performed better, this observation may not hold true in all scenarios, and further improvements are necessary. The use of GenAI in dentistry will have significant implications for dentist-patient communication and the training of dental professionals.

Artificial intelligence and personalized diagnostics in periodontology: A narrative review

Periodontal diseases pose a significant global health burden, requiring early detection and personalized treatment approaches. Traditional diagnostic approaches in periodontology often rely on a "one size fits all" approach, which may overlook the unique variations in disease progression and response to treatment among individuals. This narrative review explores the role of artificial intelligence (AI) and personalized diagnostics in periodontology, emphasizing the potential for tailored diagnostic strategies to enhance precision medicine in periodontal care. The review begins by elucidating the limitations of conventional diagnostic techniques. Subsequently, it delves into the application of AI models in analyzing diverse data sets, such as clinical records, imaging, and molecular information, and its role in periodontal training. Furthermore, the review also discusses the role of research community and policymakers in integrating personalized diagnostics in periodontal care. Challenges and ethical considerations associated with adopting AI-based personalized diagnostic tools are also explored, emphasizing the need for transparent algorithms, data safety and privacy, ongoing multidisciplinary collaboration, and patient involvement. In conclusion, this narrative review underscores the transformative potential of AI in advancing periodontal diagnostics toward a personalized paradigm, and their integration into clinical practice holds the promise of ushering in a new era of precision medicine for periodontal care.

Dental education and practice: past, present, and future trends

This position paper explores the historical transitions and current trends in dental education and practice and attempts to predict the future. Dental education and practice landscape, especially after the COVID-19 epidemic, are at a crossroads. Four fundamental forces are shaping the future: the escalating cost of education, the laicization of dental care, the corporatization of dental care, and technological advances. Dental education will likely include individualized, competency-based, asynchronous, hybrid, face-to-face, and virtual education with different start and end points for students. Dental practice, similarly, will be hybrid, with both face-to-face and virtual opportunities for patient care. Artificial intelligence will drive efficiencies in diagnosis, treatment, and office management.

Dental photography for children: A global survey

BACKGROUND

Clinical photographs are now considered an essential element for accurate and objective dental records. Very little information exists on dental photography in children and the factors that can influence the dentist's decision to take dental photographs.

AIM

This study aimed to assess the current use, attitude and confidence of paediatric dentists using clinical dental photography of children worldwide.

DESIGN

This was a cross-sectional questionnaire-based study distributed online to paediatric dentists around the globe.

RESULTS

The survey was completed by 390 dentists. The majority of participants (82.3%, n = 321) took dental photographs of their patients, and over 74% of the participants were confident in taking dental photographs. Sixty-nine dentists (17.7%) did not take photographs. Seventy-four per cent (n = 240) of those who take dental photographs and 47.8% (n = 33) of those who do not take dental photographs reported an effect on the child's behaviour. The majority of participants expressed interest in receiving further training.

CONCLUSION

Most paediatric dentists take dental photographs of their patients and value their role in the behavioural management of the child patient. Paediatric dentists need further training in children's dental photography, including guidance on proper image recording, storage and transfer.

Application of omics technologies in cariology research: A critical review with bibliometric analysis

OBJECTIVES

To review the application of omics technologies in the field of cariology research and provide critical insights into the emerging opportunities and challenges.

DATA & SOURCES

Publications on the application of omics technologies in cariology research up to December 2022 were sourced from online databases, including PubMed, Web of Science and Scopus. Two independent reviewers assessed the relevance of the publications to the objective of this review.

STUDY SELECTION

Studies that employed omics technologies to investigate dental caries were selected from the initial pool of identified publications. A total of 922 publications with one or more omics technologies adopted were included for comprehensive bibliographic analysis. (Meta)genomics (676/922, 73 %) is the predominant omics technology applied for cariology research in the included studies. Other applied omics technologies are metabolomics (108/922, 12 %), proteomics (105/922, 11 %), and transcriptomics (76/922, 8 %).

CONCLUSION

This study identified an emerging trend in the application of multiple omics technologies in cariology research. Omics technologies possess significant potential in developing strategies for the detection, staging evaluation, risk assessment, prevention, and management of dental caries. Despite the numerous challenges that lie ahead, the integration of multi-omics data obtained from individual biological samples, in conjunction with artificial intelligence technology, may offer potential avenues for further exploration in caries research.

CLINICAL SIGNIFICANCE

This review presented a comprehensive overview of the application of omics technologies in cariology research and discussed the advantages and challenges of using these methods to detect, assess, predict, prevent, and treat dental caries. It contributes to steering research for improved understanding of dental caries and advancing clinical translation of cariology research outcomes.

Comparison between clinical and computerized methods for assessing gingival pigmentation

Background and Objective

Digital computerized assessment can provide objective values for the measurement of gingival pigmentation. This study aims to compare the Commission Internationale de l'Eclairage Lab color space (CIELAB) values and the computerized intensity values (CIVs) from digital imaging with clinical evaluations using the Dummett-Gupta Oral Pigmentation Index (DOPI) for assessing gingival pigmentation in a multi-ethnic population.

Methodology

Digital photographs of 188 participants were taken using standardized parameters. The buccal gingival pigmentation was evaluated using three methods (a) a clinical evaluation by two independent assessors using the DOPI, (b) the CIELAB values using the Adobe Photoshop® software (Version 23.1.1) and (c) the CIV calculated using the ImageJ software (Version 1.53k). A hierarchical clustering analysis was used to identify colour groups that clustered together. Agreement between the clinical and digital categorization of the pigmentation was carried out using weighted kappa analysis. Agreements between CIELAB and CIV were compared using intra-class correlation coefficient.

Results

There was a statistically significant difference in the DOPI, the L*, a*, and b* coordinates, and the CIV between the different ethnic groups of the participants. Cluster analysis for the CIELAB and CIV both identified four clusters. The gingival pigmentation categorization using the L*, a*, and b* values moderately agreed with the clinical evaluation using the DOPI index while the categorization with the CIV was in slight agreement with the clinical evaluations.

Conclusion

This study identified four clusters of gingival pigmentation in 188 multi-ethnic participants. The clusters, determined by CIELAB values, align with the clinical assessment of gingival pigmentation. Digital measurements derived from clinical photographs can serve as an effective means of pigmentation measurement in dental clinics.

Comparison of the colour accuracy of a single-lens reflex camera and a smartphone camera in a clinical context

OBJECTIVES

This study aimed to investigate the colour accuracy of digital photographs captured by a single-lens reflex (SLR) camera and a smartphone camera in a clinical setting.

METHODS

Dentate subjects were recruited, and their maxillary anterior teeth were photographed along with a colour target and a dental shade guide. There were eight groups: Group 1: SLR camera with a 100 mm macro-lens and a ring-flash (SLRC); Group 2: SLRC with a polarizer; Group 3: SLRC with white-balance calibration; Group 4: SLRC with a polarizer and white-balance calibration. Groups 5 to 8 were similar to Groups 1 to 4, except a smartphone camera and an external light source (SC) were used. The CIE LAB coordinates of the colour target, shade guide, and centre of the maxillary right central incisor (tooth 11) in the digital photographs were retrieved. The colour difference ΔE=[(ΔL*)2+(Δa*)2+(Δb*)2]1/2 to the reference colour coordinates or the reading of the dental spectrophotometer was calculated. The results were analysed by the Kruskal-Wallis test at α=0.05 with Bonferroni correction.

RESULTS

Thirty-nine subjects were photographed. SLRC with a polarizer showed the largest ΔE in this study (P<0.001). When capturing tooth 11, SLRC with calibrated white-balance resulted in the smallest ΔE in this study (P<0.001), and the use of a polarizer and/or calibrated white-balance did not result in a smaller ΔE than that of SC alone (P>0.001).

CONCLUSION

Calibration for white-balance is recommended for the SLRC. The use of a polarizer does not show an improvement in colour accuracy. SC alone may be sufficient for intraoral photography.

CLINICAL SIGNIFICANCE

When capturing intraoral photography using a single-lens reflex camera, it is recommended to calibrate the white-balance. The use of a polarizer does not significantly improve colour accuracy. However, a smartphone camera with an external light source can serve as a viable alternative.

A Systematic Review of the Use of mHealth in Oral Health Education among Older Adults

Oral diseases are largely preventable. However, as the number of older adults is expected to increase, along with the high cost and various barriers to seeking continuous professional care, a sustainable approach is needed to assist older adults in maintaining their oral health. Mobile health (mHealth) technologies may facilitate oral disease prevention and management through oral health education. This review aims to provide an overview of existing evidence on using mHealth to promote oral health through education among older adults. A literature search was performed across five electronic databases. A total of five studies were identified, which provided low to moderate evidence to support using mHealth among older adults. The selected studies showed that mHealth could improve oral health management, oral health behavior, and oral health knowledge among older adults. However, more quality studies regarding using mHealth technologies in oral health management, oral health behavior, and oral health knowledge among older adults are needed.

Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review

Deep learning and diagnostic applications in oral and dental health have received significant attention recently. In this review, studies applying deep learning to diagnose anomalies and diseases in dental image material were systematically compiled, and their datasets, methodologies, test processes, explainable artificial intelligence methods, and findings were analyzed. Tests and results in studies involving human-artificial intelligence comparisons are discussed in detail to draw attention to the clinical importance of deep learning. In addition, the review critically evaluates the literature to guide and further develop future studies in this field. An extensive literature search was conducted for the 2019-May 2023 range using the Medline (PubMed) and Google Scholar databases to identify eligible articles, and 101 studies were shortlisted, including applications for diagnosing dental anomalies (n = 22) and diseases (n = 79) using deep learning for classification, object detection, and segmentation tasks. According to the results, the most commonly used task type was classification (n = 51), the most commonly used dental image material was panoramic radiographs (n = 55), and the most frequently used performance metric was sensitivity/recall/true positive rate (n = 87) and accuracy (n = 69). Dataset sizes ranged from 60 to 12,179 images. Although deep learning algorithms are used as individual or at least individualized architectures, standardized architectures such as pre-trained CNNs, Faster R-CNN, YOLO, and U-Net have been used in most studies. Few studies have used the explainable AI method (n = 22) and applied tests comparing human and artificial intelligence (n = 21). Deep learning is promising for better diagnosis and treatment planning in dentistry based on the high-performance results reported by the studies. For all that, their safety should be demonstrated using a more reproducible and comparable methodology, including tests with information about their clinical applicability, by defining a standard set of tests and performance metrics.