Application of Artificial Intelligence in Dentistry

Artificial intelligence knowledge, attitudes and application perspectives of undergraduate and specialty students of faculty of dentistry in Turkey: an online survey research

BACKGROUND

This study aimed to investigate the knowledge, attitudes, and perceptions of fourth- and fifth-year undergraduate as well as specialty dentistry students in Turkey concerning artificial intelligence (AI) and its applications.

METHODS

The study was conducted between October 16, 2023, and January 16, 2024, with participants consisting of volunteers from dental faculties in Turkey. A total of 335 undergraduate students and 62 specialty students participated in the survey, which utilized non-probability convenience and snowball sampling methods. Cronbach's alpha was utilized to measure the internal consistency of the scale. Statistical analysis was performed using IBM SPSS version 26.0, with quantitative data presented as mean ± standard deviation and categorical data as frequency (percentage). The statistical level was set at 0.05, and the analysis involved Pearson's Chi-square test and Fisher-Freeman-Halton tests.

RESULTS

The results indicate that undergraduate and specialty students perceive the integration of large datasets as the primary advantage of AI. The speed, objectivity, and potential to reduce misdiagnosis rates associated with AI are also highlighted. Undergraduate students express more significant concern about the impact of AI on patient understanding and empathy compared to specialty students. Additionally, both groups strongly advocate for the inclusion of AI-related courses in dental education and acknowledge the indispensability of AI in dental practice. The significant roles of AI in dentistry, such as providing evidence-based dental approaches and compensating for human intellectual limitations, are widely recognized. Furthermore, consensus exists that AI will primarily assist in diagnosis and treatment decisions.

CONCLUSIONS

The findings emphasize the importance of cautiously managing AI's role in healthcare services and underscore the need to prioritize patient privacy and data security. AI should be regarded as a complement to the work of dental professionals rather than a substitute. The study recommends further research involving a larger and more diverse sample to obtain a comprehensive understanding of attitudes toward AI in dentistry.

Use of a Novel Artificial Intelligence Approach for a Faster and More Precise Computerized Facial Evaluation in Aesthetic Dentistry

INTRODUCTION

AI is based on automated learning algorithms that use large bodies of information (big data). In the field of dentistry, AI allows the analysis of radiographs, intraoral images and other clinical recordings with unprecedented precision and speed. Facial analysis is known for helping dentists and patients achieve a satisfactory result when a restorative treatment must be realized. The objective of this study is to conduct a neural network-based computerized facial analysis using Python programming language in order to valuate its efficacy in facial point detection.

METHODS

The neural network was trained to identify the main facial and dental points: smile line, lips, size and for of the teeth, etc. A facial analysis was carried out using AI. A descriptive analysis was made with calculation of the mean and standard deviation (SD) of the precision and accuracy in each group. Analysis of variance (ANOVA) was used for the comparison of means between groups.

RESULTS

At the intersecting point between dentistry and technology, advances in artificial intelligence (AI) are producing a change in the way modern dentistry is performed. The present study evidenced lesser variability in the execution times of the neural network compared with the DSD system. This indicates that the neural network affords more consistent and predictable results, representing a significant advantage in terms of time and efficacy.

CONCLUSION

The neural network is significantly more efficient and consistent in performing facial analyses than the conventional DSD system. The neural network reduces the time needed to complete the analysis and shows lesser variability in its execution times.

Automated orofacial virtual patient creation: A proof of concept

OBJECTIVES

To (1) construct a virtual patient (VP) using facial scan, intraoral scan, and low-dose computed tomography scab based on an Artificial intelligence (AI)-approach, (2) quantitatively compare it with AI-refined and semi-automatic registration, and (3) qualitatively evaluate user satisfaction when using virtual patient as a communication tool in clinical practice.

MATERIALS AND METHODS

A dataset of 20 facial scans, intraoral scans, and low-dose computed tomography scans was imported into the Virtual Patient Creator platform to create an automated virtual patient. The accuracy of the virtual patients created using different approaches was further analyzed in the Mimics software. The accuracy (% of corrections required), consistency, and time efficiency of the AI-driven virtual patient registration were then compared with the AI-refined and semi-automatic registration (clinical reference). User satisfaction was assessed through a survey of 35 dentists and 25 laypersons who rated the virtual patient's realism and usefulness for treatment planning and communication on a 5-point scale.

RESULTS

The accuracy for AI-driven, AI-refined, and semi-automatic registration virtual patient was 85 %, 85 %, and 100 % for the upper and middle thirds of the face, and 30 %, 30 %, and 35 % for the lower third. Registration consistency was 1, 1 and 0.99, and the average time was 26.5, 30.8, and 385 s, respectively (18-fold time reduction with AI). The inferior facial third exhibited the highest registration mismatch between facial scan and computed tomography. User satisfaction with the virtual patient was consistently high among both dentists and laypersons, with most responses indicating very high satisfaction regarding realism and usefulness as a communication tool.

CONCLUSION

The AI-driven registration can provide clinically accurate, fast, and consistent virtual patient creation using facial scans, intraoral scans, and low-dose computed tomography scans, enabling interpersonal communication.

CLINICAL SIGNIFICANCE

Using AI for automated segmentation and registration of maxillofacial structures leads to clinically efficient and accurate VP creation, opening the doors for its widespread use in diagnosis, treatment planning, and interprofessional and professional-patient communication.

Deep learning in the overall process of implant prosthodontics: A state-of-the-art review

Artificial intelligence represented by deep learning has attracted attention in the field of dental implant restoration. It is widely used in surgical image analysis, implant plan design, prosthesis shape design, and prognosis judgment. This article mainly describes the research progress of deep learning in the whole process of dental implant prosthodontics. It analyzes the limitations of current research, and looks forward to the future development direction.

Attitudes and Perceptions of Australian Dentists and Dental Students Towards Applications of Artificial Intelligence in Dentistry: A Survey

INTRODUCTION

As artificial intelligence (AI) rapidly evolves in dentistry, understanding dentists' and dental students' perspectives is key. This survey evaluated Australian dentists' and students' attitudes and perceptions of AI in dentistry.

METHODS

An online questionnaire developed on Qualtrics was distributed among registered Australian dentists and students enrolled in accredited Australian dental or oral health programmes. Descriptive and bivariate analyses were used to examine the demographic variables and participant attitudes.

RESULTS

177 responses were received, and 155 complete responses were used in data analysis. 54.8% were aware of dental AI applications, but 70.3% could not name a specific AI software. A majority (91.6%) viewed AI as a supportive tool, with 69% believing that it would be beneficial in clinical tasks and 35.6% expecting it to perform similarly to an average specialist. 40% anticipated that dental AI would be routinely used in the next 5-10 years, with more dental students expecting this short-term integration. Concerns included job displacement, inflexibility in patient care, and mistrust of AI's accuracy. Attitudes towards AI were influenced by age, gender, clinical experience and technological proficiency.

CONCLUSIONS

The survey underscores the potential of AI to revolutionise dental care, enhancing clinical workflows and decision-making. However, challenges like trust in AI and ethical concerns remain. It is recommended that practising dentists receive hands-on training with AI tools and continuing dental education programmes. Integrating AI into dental curricula and fostering interdisciplinary teaching and research collaborations between computer science and dentistry is necessary to prepare graduates to use AI effectively and responsibly.

Artificial Intelligence in Dental Education: Opportunities and Challenges of Large Language Models and Multimodal Foundation Models

Unlabelled

Instructional and clinical technologies have been transforming dental education. With the emergence of artificial intelligence (AI), the opportunities of using AI in education has increased. With the recent advancement of generative AI, large language models (LLMs) and foundation models gained attention with their capabilities in natural language understanding and generation as well as combining multiple types of data, such as text, images, and audio. A common example has been ChatGPT, which is based on a powerful LLM-the GPT model. This paper discusses the potential benefits and challenges of incorporating LLMs in dental education, focusing on periodontal charting with a use case to outline capabilities of LLMs. LLMs can provide personalized feedback, generate case scenarios, and create educational content to contribute to the quality of dental education. However, challenges, limitations, and risks exist, including bias and inaccuracy in the content created, privacy and security concerns, and the risk of overreliance. With guidance and oversight, and by effectively and ethically integrating LLMs, dental education can incorporate engaging and personalized learning experiences for students toward readiness for real-life clinical practice.

Evaluation of Attitudes and Perceptions in Students About the Use of Artificial Intelligence in Craniomaxillofacial Surgery

Developments in technology have created great changes in the field of medicine and dentistry. Artificial intelligence technology is one of the most important innovations that caused this change. This study aimed to evaluate the opinions of dentistry students regarding the use of artificial intelligence in dentistry and craniomaxillofacial surgery. Two hundred ninety-six dentistry students between the ages of 19 and 30 participated in the study. Participants submitted the survey by e-mail examining the student's opinions and attitudes regarding the use of artificial intelligence in dentistry and craniomaxillofacial surgery. Respondents' anonymity was ensured. 47.30% (n: 140) of the students participating in the study are fourth-year students, and 52.70% (n: 156) are fifth-year students. While 48.98% (n: 145) of the participants have knowledge about the uses of artificial intelligence in daily life, 28.37% (n: 84) of the students have knowledge about robotic surgery. While ~74% of the participants think that artificial intelligence will improve the field of dentistry and craniomaxillofacial surgery, it has been observed that they are not worried about these applications replacing dentists in the future. It was determined that there was no statistically significant difference between fourth-year and fifth-year students in their knowledge levels about the areas of use of artificial intelligence (P=0.548). Students' opinions show that 74% agree that artificial intelligence will lead to major advances in the field of dentistry and craniomaxillofacial surgery. This shows the relationship between dentists and artificial intelligence points to a bright future.

Mid-infrared passive spectroscopic imaging for visualizing tooth quality

Although the measurement of tooth quality is necessary for precise prediction of caries formation, typical measurement methods include tooth-hardness measurements and absorption spectroscopy, which generally use infrared light irradiation. These methods are destructive or invasive, and obtaining two-dimensional information in the oral cavity is difficult. Mid-infrared emissions from the surface of an object reflect intrinsic vibrations of molecules in the object. In this study, a mid-infrared passive spectroscopic imaging system was developed using an inexpensive uncooled microbolometer array sensor with an optimized multi-slit, which eliminated the cancellation of interference intensities between two adjacent emission points, to obtain two-dimensional information from an object without external infrared light irradiation. First, the feasibility of obtaining two-dimensional information on tooth quality using the proposed system was examined, and emission spectra attributed to phosphate ions in hydroxyapatite (HAp), the main component of enamel, were successfully obtained from bovine teeth. Further, the hardness of bovine teeth was measured, and a correlation (R2 = 0.8067) between the Vickers hardness and peak area ratio of phosphate ions assigned to the crystalline and amorphous phases of a tooth was established. Additionally, tooth-hardness visualization in a non-contact manner was demonstrated as two-dimensional information using the obtained regression equation.

Impact of Artificial Intelligence in Endodontics: Precision, Predictions, and Prospects

Artificial intelligence (AI) has become increasingly prevalent and significant across many industries, including the dental field. AI has shown accuracy and precision in detecting, evaluating, and predicting diseases. It can imitate human intelligence to carry out sophisticated predictions and decision-making in the health-care industry, especially in endodontics. AI models have demonstrated a wide range of applications in the field of endodontics. These include examining the anatomy of the root canal system, predicting the survival of dental pulp stem cells, gauging working lengths, identifying per apical lesions and root fractures, and predicting the outcome of retreatment treatments. Future uses of this technology were discussed in terms of robotic endodontic surgery, drug-drug interactions, patient care, scheduling, and prognostic diagnosis.

Applications of Machine Learning in Periodontology and Implantology: A Comprehensive Review

Machine learning (ML) has led to significant advances in dentistry, easing the workload of professionals and improving the performance of various medical processes. The fields of periodontology and implantology can profit from these advances for tasks such as determining periodontally compromised teeth, assisting doctors in the implant planning process, determining types of implants, or predicting the occurrence of peri-implantitis. The current paper provides an overview of recent ML techniques applied in periodontology and implantology, aiming to identify popular models for different medical tasks, to assess the impact of the training data on the success of the automatic algorithms and to highlight advantages and disadvantages of various approaches. 48 original research papers, published between 2016 and 2023, were selected and divided into four classes: periodontology, implant planning, implant brands and types, and success of dental implants. These papers were analyzed in terms of aim, technical details, characteristics of training and testing data, results, and medical observations. The purpose of this paper is not to provide an exhaustive survey, but to show representative methods from recent literature that highlight the advantages and disadvantages of various approaches, as well as the potential of applying machine learning in dentistry.

Artificial intelligence applications in dentistry: A bibliometric review with an emphasis on computational research trends within the field

BACKGROUND

The aim of this study was to understand the trends regarding the use of artificial intelligence in dentistry through a bibliometric review.

TYPES OF STUDIES REVIEWED

The authors performed a literature search on Web of Science. They collected the following data: articles-number and density of citations, year, key words, language, document type, study design, and theme (main objective, diagnostic method, and specialties); journals-impact factor; authors-country, continent, and institution. The authors used Visualization of Similarities Viewer software (Leiden University) to analyze the data and Spearman test for correlation analysis.

RESULTS

After selection, 1,478 articles were included. The number of citations ranged from 0 through 327. The articles were published from 1984 through 2024. Most articles were characterized as proof of concept (979). Definition and classification of structures and diseases was the most common theme (550 articles). There was an emphasis on radiology (333 articles) and radiographic-based diagnostic methods (715 articles). China was the country with the most articles (251), and Asia was the continent with the most articles (871). The Charité-University of Medicine Berlin was the institution with the most articles (42), and the author with the most articles was Schwendicke (53).

PRACTICAL IMPLICATIONS

Artificial intelligence is an important clinical tool to facilitate diagnosis and provide automation in various processes.

Artificial Intelligence for Predicting the Aesthetic Component of the Index of Orthodontic Treatment Need

The aesthetic component (AC) of the Index of Orthodontic Treatment Need (IOTN) is internationally recognized as a reliable and valid method for assessing aesthetic treatment need. The objective of this study is to use artificial intelligence (AI) to automate the AC assessment. A total of 1009 pre-treatment frontal intraoral photos with overjet values were collected. Each photo was graded by an experienced calibration clinician. The AI was trained using the intraoral images, overjet, and two other approaches. For Scheme 1, the training data were AC 1-10. For Scheme 2, the training data were either the two groups AC 1-5 and AC 6-10 or the three groups AC 1-4, AC 5-7, and AC 8-10. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were measured for all approaches. The performance was tested without overjet values as input. The intra-rater reliability for the grader, using kappa, was 0.84 (95% CI 0.76-0.93). Scheme 1 had 77% sensitivity, 88% specificity, 82% accuracy, 89% PPV, and 75% NPV in predicting the binary groups. All other schemes offered poor tradeoffs. Findings after omitting overjet and dataset supplementation results were mixed, depending upon perspective. We have developed deep learning-based algorithms that can predict treatment need based on IOTN-AC reference standards; this provides an adjunct to clinical assessment of dental aesthetics.

Deep Convolutional Neural Network for Automated Staging of Periodontal Bone Loss Severity on Bite-wing Radiographs: An Eigen-CAM Explainability Mapping Approach

Periodontal disease is a significant global oral health problem. Radiographic staging is critical in determining periodontitis severity and treatment requirements. This study aims to automatically stage periodontal bone loss using a deep learning approach using bite-wing images. A total of 1752 bite-wing images were used for the study. Radiological examinations were classified into 4 groups. Healthy (normal), no bone loss; stage I (mild destruction), bone loss in the coronal third (< 15%); stage II (moderate destruction), bone loss is in the coronal third and from 15 to 33% (15-33%); stage III-IV (severe destruction), bone loss extending from the middle third to the apical third with furcation destruction (> 33%). All images were converted to 512 × 400 dimensions using bilinear interpolation. The data was divided into 80% training validation and 20% testing. The classification module of the YOLOv8 deep learning model was used for the artificial intelligence-based classification of the images. Based on four class results, it was trained using fivefold cross-validation after transfer learning and fine tuning. After the training, 20% of test data, which the system had never seen, were analyzed using the artificial intelligence weights obtained in each cross-validation. Training and test results were calculated with average accuracy, precision, recall, and F1-score performance metrics. Test images were analyzed with Eigen-CAM explainability heat maps. In the classification of bite-wing images as healthy, mild destruction, moderate destruction, and severe destruction, training performance results were 86.100% accuracy, 84.790% precision, 82.350% recall, and 84.411% F1-score, and test performance results were 83.446% accuracy, 81.742% precision, 80.883% recall, and 81.090% F1-score. The deep learning model gave successful results in staging periodontal bone loss in bite-wing images. Classification scores were relatively high for normal (no bone loss) and severe bone loss in bite-wing images, as they are more clearly visible than mild and moderate damage.

Application of artificial intelligence in dental crown prosthesis: a scoping review

BACKGROUND

In recent years, artificial intelligence (AI) has made remarkable advancements and achieved significant accomplishments across the entire field of dentistry. Notably, efforts to apply AI in prosthodontics are continually progressing. This scoping review aims to present the applications and performance of AI in dental crown prostheses and related topics.

METHODS

We conducted a literature search of PubMed, Scopus, Web of Science, Google Scholar, and IEEE Xplore databases from January 2010 to January 2024. The included articles addressed the application of AI in various aspects of dental crown treatment, including fabrication, assessment, and prognosis.

RESULTS

The initial electronic literature search yielded 393 records, which were reduced to 315 after eliminating duplicate references. The application of inclusion criteria led to analysis of 12 eligible publications in the qualitative review. The AI-based applications included in this review were related to detection of dental crown finish line, evaluation of AI-based color matching, evaluation of crown preparation, evaluation of dental crown designed by AI, identification of a dental crown in an intraoral photo, and prediction of debonding probability.

CONCLUSIONS

AI has the potential to increase efficiency in processes such as fabricating and evaluating dental crowns, with a high level of accuracy reported in most of the analyzed studies. However, a significant number of studies focused on designing crowns using AI-based software, and these studies had a small number of patients and did not always present their algorithms. Standardized protocols for reporting and evaluating AI studies are needed to increase the evidence and effectiveness.

Advancing maxillofacial prosthodontics by using pre-trained convolutional neural networks: Image-based classification of the maxilla

PURPOSE

The study aimed to compare the performance of four pre-trained convolutional neural networks in recognizing seven distinct prosthodontic scenarios involving the maxilla, as a preliminary step in developing an artificial intelligence (AI)-powered prosthesis design system.

MATERIALS AND METHODS

Seven distinct classes, including cleft palate, dentulous maxillectomy, edentulous maxillectomy, reconstructed maxillectomy, completely dentulous, partially edentulous, and completely edentulous, were considered for recognition. Utilizing transfer learning and fine-tuned hyperparameters, four AI models (VGG16, Inception-ResNet-V2, DenseNet-201, and Xception) were employed. The dataset, consisting of 3541 preprocessed intraoral occlusal images, was divided into training, validation, and test sets. Model performance metrics encompassed accuracy, precision, recall, F1 score, area under the receiver operating characteristic curve (AUC), and confusion matrix.

RESULTS

VGG16, Inception-ResNet-V2, DenseNet-201, and Xception demonstrated comparable performance, with maximum test accuracies of 0.92, 0.90, 0.94, and 0.95, respectively. Xception and DenseNet-201 slightly outperformed the other models, particularly compared with InceptionResNet-V2. Precision, recall, and F1 scores exceeded 90% for most classes in Xception and DenseNet-201 and the average AUC values for all models ranged between 0.98 and 1.00.

CONCLUSIONS

While DenseNet-201 and Xception demonstrated superior performance, all models consistently achieved diagnostic accuracy exceeding 90%, highlighting their potential in dental image analysis. This AI application could help work assignments based on difficulty levels and enable the development of an automated diagnosis system at patient admission. It also facilitates prosthesis designing by integrating necessary prosthesis morphology, oral function, and treatment difficulty. Furthermore, it tackles dataset size challenges in model optimization, providing valuable insights for future research.

Outcome measurements and quality of randomized controlled clinical trials of tooth-supported fixed dental prostheses: A systematic review and qualitative analysis

STATEMENT OF PROBLEM

The lack of consensus regarding a standardized set of outcome measurements and noncompliance with current reporting guidelines in clinical trials of tooth-supported fixed dental prostheses (FDPs) hamper interstudy comparability, compromise scientific evidence, and waste research effort and resources in prosthetic dentistry.

PURPOSE

The primary objective of this systematic review was to identify all primary and secondary outcome measurements assessed in randomized controlled trials (RCTs) of tooth-supported FDPs. Secondary objectives were to assess their methodological quality by using the Cochrane Collaboration's risk of bias tool (RoB, v2.0) and their reporting quality by means of a standardized 16-item CONSORT assessment tool through published reports.

MATERIAL AND METHODS

An electronic search was conducted in MEDLINE, EMBASE, and Cochrane library to identify all RCT-related articles published in the past 10 years. Differences in RoB were tested with the Pearson chi-squared test, and those in CONSORT score with the Student t test.

RESULTS

A total of 64 RCTs from 79 publications were deemed eligible. The diversity of outcome measures used in the field is apparent. Twenty percent of the included studies had a low RoB, 79% showed some concerns, and 1% had a high RoB. The mean ±standard deviation CONSORT compliance score was 22.56 ±3.17. Trials adhered to the CONSORT statement reported lower RoB than those that did not adhere (P<.001). RCTs with a low RoB reported more comprehensive adherence to CONSORT guidelines than those with some concerns (MD 4 [95% CI 1.52-6.48]; P=.004).

CONCLUSIONS

A standardized core outcome reporting set in clinical research on tooth-supported FDPs remains evident. Adherence to the CONSORT statement continues to be low, with some RoB concerns that can be improved.

Comparison of the Efficacy of Artificial Intelligence-Powered Software in Crown Design: An In Vitro Study

INTRODUCTION AND AIMS

Artificial intelligence (AI) has been adopted in the field of dental restoration. This study aimed to evaluate the time efficiency and morphological accuracy of crowns designed by two AI-powered software programs in comparison with conventional computer-aided design software.

METHODS

A total of 33 clinically adapted posterior crowns were involved in the standard group. AI Automate (AA) and AI Dentbird Crown (AD) used two AI-powered design software programs, while the computer-aided experienced and computer-aided novice employed the Exocad DentalCAD software. Time efficiency between the AI-powered groups and computer-aided groups was evaluated by assessing the elapsed time. Morphological accuracy was assessed by means of three-dimensional geometric calculations, with the root-mean-square error compared against the standard group. Statistical analysis was conducted via the Kruskal-Wallis test (α = 0.05).

RESULTS

The time efficiency of the AI-powered groups was significantly higher than that of the computer-aided groups (P < .01). Moreover, the working time for both AA and AD groups was only one-quarter of that for the computer-aided novice group. Four groups significantly differed in morphological accuracy for occlusal and distal surfaces (P < .05). The AD group performed lower accuracy than the other three groups on the occlusal surfaces (P < .001) and the computer-aided experienced group was superior to the AA group in terms of accuracy on the distal surfaces (P = .029). However, morphological accuracy showed no significant difference among the four groups for mesial surfaces and margin lines (P > .05).

CONCLUSION

AI-powered software enhanced the efficiency of crown design but failed to excel at morphological accuracy compared with experienced technicians using computer-aided software. AI-powered software requires further research and extensive deep learning to improve the morphological accuracy and stability of the crown design.

Periodontal disease and emerging point-of-care technologies for its diagnosis

Periodontal disease (PD), a chronic inflammatory disorder that damages the tooth and its supporting components, is a common global oral health problem. Understanding the intricacies of these disorders, from gingivitis to severe PD, is critical for efficient treatment, diagnosis, and prevention in dental care. Periodontal biosensors and biomarkers are critical in improving oral health diagnostic skills. Clinicians may accomplish early identification, tailored therapy, and efficient tracking of periodontal diseases by using these technologies, ushering in a new age of accurate oral healthcare. Traditional periodontitis diagnostic methods frequently rely on physical probing and visual examinations, necessitating the development of point-of-care (POC) devices. As periodontal disorders necessitate more precise and rapid diagnosis, incorporating novel innovations in biosensors and biomarkers becomes increasingly crucial. These innovations improve our capacity to diagnose, monitor, and adapt periodontal therapies, bringing in the next phase of customized and effective dental healthcare. The review discusses the characteristics and stages of PD, clinical treatment techniques, prominent biomarkers and infection-associated factors that may be employed to determine PD, biomedical sensing, and POC appliances that have been created so far to diagnose stages of PD and its progression profile, as well as predicting future developments in this field.

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.

Recent Advances in Digital Technology in Implant Dentistry

Digital technology has emerged as a transformative tool in dental implantation, profoundly enhancing accuracy and effectiveness across multiple facets, such as diagnosis, preoperative treatment planning, surgical procedures, and restoration delivery. The multiple integration of radiographic data and intraoral data, sometimes with facial scan data or electronic facebow through virtual planning software, enables comprehensive 3-dimensional visualization of the hard and soft tissue and the position of future restoration, resulting in heightened diagnostic precision. In virtual surgery design, the incorporation of both prosthetic arrangement and individual anatomical details enables the virtual execution of critical procedures (e.g., implant placement, extended applications, etc.) through analysis of cross-sectional images and the reconstruction of 3-dimensional surface models. After verification, the utilization of digital technology including templates, navigation, combined techniques, and implant robots achieved seamless transfer of the virtual treatment plan to the actual surgical sites, ultimately leading to enhanced surgical outcomes with highly improved accuracy. In restoration delivery, digital techniques for impression, shade matching, and prosthesis fabrication have advanced, enabling seamless digital data conversion and efficient communication among clinicians and technicians. Compared with clinical medicine, artificial intelligence (AI) technology in dental implantology primarily focuses on diagnosis and prediction. AI-supported preoperative planning and surgery remain in developmental phases, impeded by the complexity of clinical cases and ethical considerations, thereby constraining widespread adoption.

Artificial intelligence-assisted full-mouth radiograph mounting in dental education

OBJECTIVES

With the increasing prevalence of artificial intelligence (AI) and the significant research gap in the application of AI within dentistry, this study aimed to (1) evaluate the efficiency and accuracy of dental students in full-mouth radiograph series (FMS) mounting with and without AI assistance, and (2) assess dental students' perceptions of AI in clinical education to address the impact of AI in dental education.

METHODS

An AI-based interface for mounting radiographs on FMS templates was designed and implemented in the study. Forty third-year dental students were randomly assigned to control and test groups. The control group manually mounted FMS radiographs, while the test group reviewed AI-pre-mounted radiographs for adjustments. Students' performance in efficiency and accuracy was evaluated. Pre- and post-study surveys were conducted to gauge students' confidence levels and opinions regarding the usefulness of the AI-assisted program.

RESULTS

The test group (using AI) demonstrated significantly faster radiograph mounting times than the control group (manual) (p < 0.05). Accuracy was lower in the test groups, when comparing AI-assisted and manual mounting of FMS (p < 0.01). Self-confidence and confidence in AI were consistent between the control and test groups, both before and after the study.

CONCLUSION

Students with AI presented with a decreased accuracy in FMS radiograph mounting. Therefore, AI automation could potentially have negative impacts in a learning environment with inexperienced clinicians.

Revolutionizing Dentistry: The Applications of Artificial Intelligence in Dental Health Care

Artificial intelligence (AI) is transforming the landscape of health care, and dentistry is no exception. This article explores the various applications of AI in dentistry, showcasing how this advanced technology is revolutionizing diagnosis, treatment, and patient care. From enabling early detection of oral diseases to enhancing the precision of dental procedures, AI is driving the industry toward more efficient and effective dental healthcare services. This article delves into the specific ways in which AI is being integrated into dental practices, highlighting its potential to improve patient outcomes and advance the field of dentistry.

Scoping Review Shows the Dynamics and Complexities Inherent to the Notion of "Responsibility" in Artificial Intelligence within the Healthcare Context

The increasing integration of artificial intelligence (AI) in healthcare presents a host of ethical, legal, social, and political challenges involving various stakeholders. These challenges prompt various studies proposing frameworks and guidelines to tackle these issues, emphasizing distinct phases of AI development, deployment, and oversight. As a result, the notion of responsible AI has become widespread, incorporating ethical principles such as transparency, fairness, responsibility, and privacy. This paper explores the existing literature on AI use in healthcare to examine how it addresses, defines, and discusses the concept of responsibility. We conducted a scoping review of literature related to AI responsibility in healthcare, searching databases and reference lists between January 2017 and January 2022 for terms related to "responsibility" and "AI in healthcare", and their derivatives. Following screening, 136 articles were included. Data were grouped into four thematic categories: (1) the variety of terminology used to describe and address responsibility; (2) principles and concepts associated with responsibility; (3) stakeholders' responsibilities in AI clinical development, use, and deployment; and (4) recommendations for addressing responsibility concerns. The results show the lack of a clear definition of AI responsibility in healthcare and highlight the importance of ensuring responsible development and implementation of AI in healthcare. Further research is necessary to clarify this notion to contribute to developing frameworks regarding the type of responsibility (ethical/moral/professional, legal, and causal) of various stakeholders involved in the AI lifecycle.

Leveraging Large Language Models in the delivery of post-operative dental care: a comparison between an embedded GPT model and ChatGPT

OBJECTIVE

This study underscores the transformative role of Artificial Intelligence (AI) in healthcare, particularly the promising applications of Large Language Models (LLMs) in the delivery of post-operative dental care. The aim is to evaluate the performance of an embedded GPT model and its comparison with ChatGPT-3.5 turbo. The assessment focuses on aspects like response accuracy, clarity, relevance, and up-to-date knowledge in addressing patient concerns and facilitating informed decision-making.

MATERIAL AND METHODS

An embedded GPT model, employing GPT-3.5-16k, was crafted via GPT-trainer to answer postoperative questions in four dental specialties including Operative Dentistry & Endodontics, Periodontics, Oral & Maxillofacial Surgery, and Prosthodontics. The generated responses were validated by thirty-six dental experts, nine from each specialty, employing a Likert scale, providing comprehensive insights into the embedded GPT model's performance and its comparison with GPT3.5 turbo. For content validation, a quantitative Content Validity Index (CVI) was used. The CVI was calculated both at the item level (I-CVI) and scale level (S-CVI/Ave). To adjust I-CVI for chance agreement, a modified kappa statistic (K*) was computed.

RESULTS

The overall content validity of responses generated via embedded GPT model and ChatGPT was 65.62% and 61.87% respectively. Moreover, the embedded GPT model revealed a superior performance surpassing ChatGPT with an accuracy of 62.5% and clarity of 72.5%. In contrast, the responses generated via ChatGPT achieved slightly lower scores, with an accuracy of 52.5% and clarity of 67.5%. However, both models performed equally well in terms of relevance and up-to-date knowledge.

CONCLUSION

In conclusion, embedded GPT model showed better results as compared to ChatGPT in providing post-operative dental care emphasizing the benefits of embedding and prompt engineering, paving the way for future advancements in healthcare applications.

Scoring facial attractiveness with deep convolutional neural networks: How training on standardized images reduces the bias of facial expressions

OBJECTIVE

In many medical disciplines, facial attractiveness is part of the diagnosis, yet its scoring might be confounded by facial expressions. The intent was to apply deep convolutional neural networks (CNN) to identify how facial expressions affect facial attractiveness and to explore whether a dedicated training of the CNN is able to reduce the bias of facial expressions.

MATERIALS AND METHODS

Frontal facial images (n = 840) of 40 female participants (mean age 24.5 years) were taken adapting a neutral facial expression and the six universal facial expressions. Facial attractiveness was computed by means of a face detector, deep convolutional neural networks, standard support vector regression for facial beauty, visual regularized collaborative filtering and a regression technique for handling visual queries without rating history. CNN was first trained on random facial photographs from a dating website and then further trained on the Chicago Face Database (CFD) to increase its suitability to medical conditions. Both algorithms scored every image for attractiveness.

RESULTS

Facial expressions affect facial attractiveness scores significantly. Scores from CNN additionally trained on CFD had less variability between the expressions (range 54.3-60.9 compared to range: 32.6-49.5) and less variance within the scores (P ≤ .05), but also caused a shift in the ranking of the expressions' facial attractiveness.

CONCLUSION

Facial expressions confound attractiveness scores. Training on norming images generated scores less susceptible to distortion, but more difficult to interpret. Scoring facial attractiveness based on CNN seems promising, but AI solutions must be developed on CNN trained to recognize facial expressions as distractors.

The performance of artificial intelligence models in generating responses to general orthodontic questions: ChatGPT vs Google Bard

INTRODUCTION

This study aimed to evaluate and compare the performance of 2 artificial intelligence (AI) models, Chat Generative Pretrained Transformer-3.5 (ChatGPT-3.5; OpenAI, San Francisco, Calif) and Google Bidirectional Encoder Representations from Transformers (Google Bard; Bard Experiment, Google, Mountain View, Calif), in terms of response accuracy, completeness, generation time, and response length when answering general orthodontic questions.

METHODS

A team of orthodontic specialists developed a set of 100 questions in 10 orthodontic domains. One author submitted the questions to both ChatGPT and Google Bard. The AI-generated responses from both models were randomly assigned into 2 forms and sent to 5 blinded and independent assessors. The quality of AI-generated responses was evaluated using a newly developed tool for accuracy of information and completeness. In addition, response generation time and length were recorded.

RESULTS

The accuracy and completeness of responses were high in both AI models. The median accuracy score was 9 (interquartile range [IQR]: 8-9) for ChatGPT and 8 (IQR: 8-9) for Google Bard (Median difference: 1; P <0.001). The median completeness score was similar in both models, with 8 (IQR: 8-9) for ChatGPT and 8 (IQR: 7-9) for Google Bard. The odds of accuracy and completeness were higher by 31% and 23% in ChatGPT than in Google Bard. Google Bard's response generation time was significantly shorter than that of ChatGPT by 10.4 second/question. However, both models were similar in terms of response length generation.

CONCLUSIONS

Both ChatGPT and Google Bard generated responses were rated with a high level of accuracy and completeness to the posed general orthodontic questions. However, acquiring answers was generally faster using the Google Bard model.

The Application of artificial intelligence in restorative Dentistry: A narrative review of current research

This review explores the transformative impact of artificial intelligence (AI) on restorative dentistry. By discussing the diagnostic processes, treatment planning, image analysis, prosthodontics, and material/biomaterial research, this study highlights the role of AI in optimizing precision and efficiency. It emphasizes personalized material selection, accelerated biomaterial research, and AI-enabled clinical workflows for enhanced patient outcomes. The review concludes with insights into the challenges, ethical considerations, and future trends, emphasizing the collaborative efforts needed for continued innovation in AI-driven restorative dentistry.

Psychometric Properties of General Oral Health Assessment Index Across Ages: COSMIN Systematic Review

OBJECTIVES

To systematically review the psychometric properties of the Geriatric Oral Health Assessment Index (GOHAI) across age groups using the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) methodology.

METHODS

Data: English peer-reviewed articles reporting studies of the development, translation, or validation of GOHAI.

SOURCES

PubMed, Web of Science, and EMBASE from Jan 1990 until December 31, 2023. Methodological evaluation: based on COSMIN methodology. The results are presented overall and for 4 age groups (≥60 years, all ages, <60 years, ≤45 years). Structural validity was summarized qualitatively. Internal consistency and reliability were synthesized via random-effects meta-analysis of T-transformed Cronbach α values, and Fisher's Z transformed correlation coefficients. Construct validity and responsiveness were assessed using effect sizes.

RESULTS

Four hundred ninety-seven records were identified, 72 underwent full-text assessment, resulting in 60 included reports. Structural validity was inconsistent across all age groups and overall. Internal consistency was sufficient with overall α = 0.81, and high evidence quality. Test-retest reliability was consistently sufficient across age groups with overall r = 0.84. For construct validity 361 hypotheses were assessed (37.4% for convergent-, 62.6% for known-groups validity). The percentage of confirmed hypotheses in ≥60-years, all ages, <60-years and ≤45-years were 75.5%, 66.7%, 78.9%, and 88.9%, respectively. Responsiveness was not assessed in the <60-years and ≤45-years age groups, leading to indeterminate overall rating with very low evidence quality.

CONCLUSIONS

This review affirms that GOHAI has sufficient psychometric properties as an oral health-related quality of life instrument in various age groups, but its responsiveness is scarcely researched and its utility for individual-level follow-up is limited. The measurement properties of oral health-related quality of life tools must be scrutinized in the changing demands of personalized and value-based dental care. (PROSPERO registration: CRD42022384132).

Artificial intelligence vs. semi-automated segmentation for assessment of dental periapical lesion volume index score: A cone-beam CT study

INTRODUCTION

Cone beam computed tomography periapical volume index (CBCTPAVI) is a categorisation tool to assess periapical lesion size in three-dimensions and predict treatment outcomes. This index was determined using a time-consuming semi-automatic segmentation technique. This study compared artificial intelligence (AI) with semi-automated segmentation to determine AI's ability to accurately determine CBCTPAVI score.

METHODS

CBCTPAVI scores for 500 tooth roots were determined using both the semi-automatic segmentation technique in three-dimensional imaging analysis software (Mimics Research™) and AI (Diagnocat™). A confusion matrix was created to compare the CBCTPAVI score by the AI with the semi-automatic segmentation technique. Evaluation metrics, precision, recall, F1-score (2×precision×recallprecision+recall), and overall accuracy were determined.

RESULTS

In 84.4 % (n = 422) of cases the AI classified CBCTPAVI score the same as the semi-automated technique. AI was unable to classify any lesion as index 1 or 2, due to its limitation in small volume measurement. When lesions classified as index 1 and 2 by the semi-automatic segmentation technique were excluded, the AI demonstrated levels of precision, recall and F1-score, all above 0.85, for indices 0, 3-6; and accuracy over 90 %.

CONCLUSIONS

Diagnocat™ with its ability to determine CBCTPAVI score in approximately 2 min following upload of the CBCT could be an excellent and efficient tool to facilitate better monitoring and assessment of periapical lesions in everyday clinical practice and/or radiographic reporting. However, to assess three-dimensional healing of smaller lesions (with scores 1 and 2), further advancements in AI technologies are needed.

Classification of Caries Based on CBCT: A Deep Learning Network Interpretability Study

This study aimed to create a caries classification scheme based on cone-beam computed tomography (CBCT) and develop two deep learning models to improve caries classification accuracy. A total of 2713 axial slices were obtained from CBCT images of 204 carious teeth. Both classification models were trained and tested using the same pretrained classification networks on the dataset, including ResNet50_vd, MobileNetV3_large_ssld, and ResNet50_vd_ssld. The first model was used directly to classify the original images (direct classification model). The second model incorporated a presegmentation step for interpretation (interpretable classification model). Performance evaluation metrics including accuracy, precision, recall, and F1 score were calculated. The Local Interpretable Model-agnostic Explanations (LIME) method was employed to elucidate the decision-making process of the two models. In addition, a minimum distance between caries and pulp was introduced for determining the treatment strategies for type II carious teeth. The direct model that utilized the ResNet50_vd_ssld network achieved top accuracy, precision, recall, and F1 score of 0.700, 0.786, 0.606, and 0.616, respectively. Conversely, the interpretable model consistently yielded metrics surpassing 0.917, irrespective of the network employed. The LIME algorithm confirmed the interpretability of the classification models by identifying key image features for caries classification. Evaluation of treatment strategies for type II carious teeth revealed a significant negative correlation (p < 0.01) with the minimum distance. These results demonstrated that the CBCT-based caries classification scheme and the two classification models appeared to be acceptable tools for the diagnosis and categorization of dental caries.

The innovation of AI-based software in oral diseases: clinical-histopathological correlation diagnostic accuracy primary study

BACKGROUND

Machine learning (ML) through artificial intelligence (AI) could provide clinicians and oral pathologists to advance diagnostic problems in the field of potentially malignant lesions, oral cancer, periodontal diseases, salivary gland disease, oral infections, immune-mediated disease, and others. AI can detect micro-features beyond human eyes and provide solution in critical diagnostic cases.

OBJECTIVE

The objective of this study was developing a software with all needed feeding data to act as AI-based program to diagnose oral diseases. So our research question was: Can we develop a Computer-Aided Software for accurate diagnosis of oral diseases based on clinical and histopathological data inputs?

METHOD

The study sample included clinical images, patient symptoms, radiographic images, histopathological images and texts for the oral diseases of interest in the current study (premalignant lesions, oral cancer, salivary gland neoplasms, immune mediated oral mucosal lesions, oral reactive lesions) total oral diseases enrolled in this study was 28 diseases retrieved from the archives of oral maxillofacial pathology department. Total 11,200 texts and 3000 images (2800 images were used for training data to the program and 100 images were used as test data to the program and 100 cases for calculating accuracy, sensitivity& specificity).

RESULTS

The correct diagnosis rates for group 1 (software users), group 2 (microscopic users) and group 3 (hybrid) were 87%, 90.6, 95% respectively. The reliability for inter-observer value was done by calculating Cronbach's alpha and interclass correlation coefficient. The test revealed for group 1, 2 and 3 the following values respectively 0.934, 0.712 & 0.703. All groups showed acceptable reliability especially for Diagnosis Oral Diseases Software (DODS) that revealed higher reliability value than other groups. However, The accuracy, sensitivity & specificity of this software was lower than those of oral pathologists (master's degree).

CONCLUSION

The correct diagnosis rate of DODS was comparable to oral pathologists using standard microscopic examination. The DODS program could be utilized as diagnostic guidance tool with high reliability & accuracy.

Evaluation of different machine learning algorithms for extraction decision in orthodontic treatment

INTRODUCTION

The extraction decision significantly affects the treatment process and outcome. Therefore, it is crucial to make this decision with a more objective and standardized method. The objectives of this study were (1) to identify the best-performing model among seven machine learning (ML) models, which will standardize the extraction decision and serve as a guide for inexperienced clinicians, and (2) to determine the important variables for the extraction decision.

METHODS

This study included 1000 patients who received orthodontic treatment with or without extraction (500 extraction and 500 non-extraction). The success criteria of the study were the decisions made by the four experienced orthodontists. Seven ML models were trained using 36 variables; including demographic information, cephalometric and model measurements. First, the extraction decision was performed, and then the extraction type was identified. Accuracy and area under the curve (AUC) of the receiver operating characteristics (ROC) curve were used to measure the success of ML models.

RESULTS

The Stacking Classifier model, which consists of Gradient Boosted Trees, Support Vector Machine, and Random Forest models, showed the highest performance in extraction decision with 91.2% AUC. The most important features determining extraction decision were maxillary and mandibular arch length discrepancy, Wits Appraisal, and ANS-Me length. Likewise, the Stacking Classifier showed the highest performance with 76.3% accuracy in extraction type decisions. The most important variables for the extraction type decision were mandibular arch length discrepancy, Class I molar relationship, cephalometric overbite, Wits Appraisal, and L1-NB distance.

CONCLUSION

The Stacking Classifier model exhibited the best performance for the extraction decision. While ML models showed a high performance in extraction decision, they could not able to achieve the same level of performance in extraction type decision.

AI-Assisted Detection of Interproximal, Occlusal, and Secondary Caries on Bite-Wing Radiographs: A Single-Shot Deep Learning Approach

Tooth decay is a common oral disease worldwide, but errors in diagnosis can often be made in dental clinics, which can lead to a delay in treatment. This study aims to use artificial intelligence (AI) for the automated detection and localization of secondary, occlusal, and interproximal (D1, D2, D3) caries types on bite-wing radiographs. The eight hundred and sixty bite-wing radiographs were collected from the School of Dentistry database. Pre-processing and data augmentation operations were performed. Interproximal (D1, D2, D3), secondary, and occlusal caries on bite-wing radiographs were annotated by two oral radiologists. The data were split into 80% for training, 10% for validation, and 10% for testing. The AI-based training process was conducted using the YOLOv8 algorithm. A clinical decision support system interface was designed using the Python PyQT5 library, allowing for the use of dental caries detection without the need for complex programming procedures. In the test images, the average precision, average sensitivity, and average F1 score values for secondary, occlusal, and interproximal caries were obtained as 0.977, 0.932, and 0.954, respectively. The AI-based dental caries detection system yielded highly successful results in the test, receiving full approval from dentists for clinical use. YOLOv8 has the potential to increase sensitivity and reliability while reducing the burden on dentists and can prevent diagnostic errors in dental clinics.

Towards clinically applicable automated mandibular canal segmentation on CBCT

OBJECTIVES

To develop a deep learning-based system for precise, robust, and fully automated segmentation of the mandibular canal on cone beam computed tomography (CBCT) images.

METHODS

The system was developed on 536 CBCT scans (training set: 376, validation set: 80, testing set: 80) from one center and validated on an external dataset of 89 CBCT scans from 3 centers. Each scan was annotated using a multi-stage annotation method and refined by oral and maxillofacial radiologists. We proposed a three-step strategy for the mandibular canal segmentation: extraction of the region of interest based on 2D U-Net, global segmentation of the mandibular canal, and segmentation refinement based on 3D U-Net.

RESULTS

The system consistently achieved accurate mandibular canal segmentation in the internal set (Dice similarity coefficient [DSC], 0.952; intersection over union [IoU], 0.912; average symmetric surface distance [ASSD], 0.046 mm; 95% Hausdorff distance [HD95], 0.325 mm) and the external set (DSC, 0.960; IoU, 0.924; ASSD, 0.040 mm; HD95, 0.288 mm).

CONCLUSIONS

These results demonstrated the potential clinical application of this AI system in facilitating clinical workflows related to mandibular canal localization.

CLINICAL SIGNIFICANCE

Accurate delineation of the mandibular canal on CBCT images is critical for implant placement, mandibular third molar extraction, and orthognathic surgery. This AI system enables accurate segmentation across different models, which could contribute to more efficient and precise dental automation systems.

Assessing the performance of Bing Chat artificial intelligence: Dental exams, clinical guidelines, and patients' frequent questions

OBJECTIVES

Bing Chat is a large language model artificial intelligence (AI) with online search and text generating capabilities. This study assessed its performance within the scope of dentistry in: (a) tackling exam questions for dental students, (ii) providing guidelines for dental practitioners, and (iii) answering patients' frequently asked questions. We discuss the potential of clinical tutoring, common patient communication and impact on academia.

METHODS

With the aim of assessing AI's performance in dental exams, Bing Chat was presented with 532 multiple-choice questions and awarded scores based on its answers. In evaluating guidelines for clinicians, a further set of 15 questions, each with 2 follow-up questions on clinical protocols, was presented to the AI. The answers were assessed by 4 reviewers using electronic visual analog scale. In evaluating answers to patients' frequently asked questions, another list of 15 common questions was included in the session, with respective outputs assessed.

RESULTS

Bing Chat correctly answered 383 out of 532 multiple-choice questions in dental exam part, achieving a score of 71.99 %. As for outlining clinical protocols for practitioners, the overall assessment score was 81.05 %. In answering patients' frequently asked questions, Bing Chat achieved an overall mean score of 83.8 %. The assessments demonstrated low inter-rater reliability.

CONCLUSIONS

The overall performance of Bing Chat was above the regularly adopted passing scores, particularly in answering patient's frequently asked questions. The generated content may have biased sources. These results suggest the importance of raising clinicians' awareness of AI's benefits and risks, as well as timely adaptations of dental education curricula, and safeguarding its use in dentistry and healthcare in general.

CLINICAL SIGNIFICANCE

Bing Chat AI performed above the passing threshold in three categories, and thus demonstrated potential for educational assistance, clinical tutoring, and answering patients' questions. We recommend popularizing its benefits and risks among students and clinicians, while maintaining awareness of possible false information.

Artificial Intelligence in Adult and Pediatric Dentistry: A Narrative Review

Artificial intelligence (AI) has been recently introduced into clinical dentistry, and it has assisted professionals in analyzing medical data with unprecedented speed and an accuracy level comparable to humans. With the help of AI, meaningful information can be extracted from dental databases, especially dental radiographs, to devise machine learning (a subset of AI) models. This study focuses on models that can diagnose and assist with clinical conditions such as oral cancers, early childhood caries, deciduous teeth numbering, periodontal bone loss, cysts, peri-implantitis, osteoporosis, locating minor apical foramen, orthodontic landmark identification, temporomandibular joint disorders, and more. The aim of the authors was to outline by means of a review the state-of-the-art applications of AI technologies in several dental subfields and to discuss the efficacy of machine learning algorithms, especially convolutional neural networks (CNNs), among different types of patients, such as pediatric cases, that were neglected by previous reviews. They performed an electronic search in PubMed, Google Scholar, Scopus, and Medline to locate relevant articles. They concluded that even though clinicians encounter challenges in implementing AI technologies, such as data management, limited processing capabilities, and biased outcomes, they have observed positive results, such as decreased diagnosis costs and time, as well as early cancer detection. Thus, further research and development should be considered to address the existing complications.

Automated facial landmark measurement using machine learning: A feasibility study

STATEMENT OF PROBLEM

Information regarding facial landmark measurement using machine learning (ML) techniques in prosthodontics is lacking.

PURPOSE

The objective of this study was to evaluate and compare the reliability, validity, and accuracy of facial anthropological measurements using both manual and ML landmark detection techniques.

MATERIAL AND METHODS

Two-dimensional (2D) frontal full-face photographs of 50 men and 50 women were made. The interpupillary width (IPW), interlateral canthus width (LCW), intermedial canthus width (MCW), interalar width (IAW), and intercommissural width (ICW) were measured on 2D digital images using manual and ML methods. The automated measurements were recorded using a programming language (Python), and a convolutional neural network (CNN) model was trained to detect human facial landmarks. The obtained data from the manual and ML methods were analyzed using intraclass correlation coefficients (ICCs), the paired sample t test, Bland-Altman plots, and the Pearson correlation analysis (α=.05).

RESULTS

Intrarater and interrater reliability values were greater than 0.90, indicating excellent reliability. The mean difference between the manual and ML measurements of IPW, MCW, IAW, and ICW was 0.02 mm, while it was 0.01 mm for LCW. No statistically significant differences were found between the measurements obtained by the manual and ML methods (P>.05). Highly significant positive correlations (P<.001) were obtained between the results of the manual and ML methods: (r=0.996[IPW], r=0.977[LCW], r=0.944[MCW], r=0.965[IAW], and r=0.997[ICW]).

CONCLUSIONS

In the field of prosthodontics, the use of ML methods provides a reliable alternative to manual digital techniques for carrying out facial anthropometric measurements.

The Quality of AI-Generated Dental Caries Multiple Choice Questions: A Comparative Analysis of ChatGPT and Google Bard Language Models

Statement of problem

AI technology presents a variety of benefits and challenges for educators.

Purpose

To investigate whether ChatGPT and Google Bard (now is named Gemini) are valuable resources for generating multiple-choice questions for educators of dental caries.

Material and methods

A book on dental caries was used. Sixteen paragraphs were extracted by an expert consultant based on applicability and potential for developing multiple-choice questions. ChatGPT and Bard language models were used to produce multiple-choice questions based on this input, and 64 questions were generated. Three dental specialists assessed the relevance, accuracy, and complexity of the generated questions. The questions were qualitatively evaluated using cognitive learning objectives and item writing flaws. Paired sample t-tests and two-way analysis of variance (ANOVA) were used to compare the generated multiple-choice questions and answers between ChatGPT and Bard.

Results

There were no significant differences between the questions generated by ChatGPT and Bard. Moreover, the analysis of variance found no significant differences in question quality. Bard-generated questions tended to have higher cognitive levels than those of ChatGPT. Format error was predominant in ChatGPT-generated questions. Finally, Bard exhibited more absolute terms than ChatGPT.

Conclusions

ChatGPT and Bard could generate questions related to dental caries, mainly at the cognitive level of knowledge and comprehension.

Clinical significance

Language models are crucial for generating subject-specific questions used in quizzes, tests, and education. By using these models, educators can save time and focus on lesson preparation and student engagement instead of solely focusing on assessment creation. Additionally, language models are adept at generating numerous questions, making them particularly valuable for large-scale exams. However, educators must carefully review and adapt the questions to ensure they align with their learning goals.

Knowledge and Perception of Artificial Intelligence among Faculty Members and Students at Batterjee Medical College

Background

Mounting research suggests that artificial intelligence (AI) is one of the innovations that aid in the patient's diagnosis and treatment, but unfortunately limited research has been conducted in this regard in the Kingdom of Saudi Arabia (KSA). Hence, this study aimed to assess the level of knowledge and awareness of AI among faculty members and medicine students in one of the premier medical colleges in KSA.

Methods

A cross-sectional descriptive study was conducted at Batterjee Medical College (BMC), Jeddah (KSA), from November 2022 to April 2023.

Result

A total of 131 participants contributed to our study, of which three were excluded due to incomplete responses, thereby giving a response rate of 98%.

Conclusion

85.4% of the respondents believe that AI has a positive impact on the healthcare system and physicians in general. Hence, there should be a mandatory course in medical schools that can prepare future doctors to diagnose patients more accurately, make predictions about patients' future health, and recommend better treatments.

Students' perceptions towards the ethical considerations of using artificial intelligence algorithms in clinical decision-making

Aim The present study aimed to explore the perceptions of dental students regarding the ethical considerations associated with the use of artificial intelligence (AI) algorithms in clinical decision-making.Methods All the undergraduate clinical-year dental students were invited to take part in the study. A validated online questionnaire which consisted of 21 closed-ended questions (five-point Likert scales) was distributed to the students to evaluate their perceptions on the topic. Mean perception scores of the students from different years were analysed using a one-way ANOVA test, while independent t-tests were used to compare the scores between sexes.Results In total, 165 students participated in the present study. The mean age of the respondents was 23.3 (± 1.38) years and the majority were female, Chinese students. Respondents showed positive perceptions throughout all three domains. Uniform and comparable perceptions were seen across various academic years and sexes, with female respondents expressing stronger agreement regarding patient consent and privacy prioritisation.Conclusion Undergraduate clinical dental students generally showed positive perceptions regarding the ethical considerations associated with the integration of AI algorithms in clinical decision-making. It is essential to address these ethical considerations to ensure that AI benefits patient outcomes while upholding fundamental ethical principles and patient-centred care.

A cascading learning method with SegFormer for radiographic measurement of periodontal bone loss

OBJECTIVE

Marginal alveolar bone loss is one of the key features of periodontitis and can be observed via panoramic radiographs. This study aimed to establish a cascading learning method with deep learning (DL) for precise radiographic bone loss (RBL) measurements at specific tooth positions.

MATERIALS AND METHODS

Through the design of two tasks for tooth position recognition and tooth semantic segmentation using the SegFormer model, specific tooth's crown, intrabony portion, and suprabony portion of the roots were obtained. The RBL was subsequently measured by length through these three areas using the principal component analysis (PCA) principal axis.

RESULTS

The average intersection over union (IoU) for the tooth position recognition task was 0.8906, with an F1-score of 0.9338. The average IoU for the tooth semantic segmentation task was 0.8465, with an F1-score of 0.9138. When the two tasks were combined, the average IoU was 0.7889, with an F1-score of 0.8674. The correlation coefficient between the RBL prediction results based on the PCA principal axis and the clinicians' measurements exceeded 0.85. Compared to those of the other two methods, the average precision of the predicted RBL was 0.7722, the average sensitivity was 0.7416, and the average F1-score was 0.7444.

CONCLUSIONS

The method for predicting RBL using DL and PCA produced promising results, offering rapid and reliable auxiliary information for future periodontal disease diagnosis.

CLINICAL RELEVANCE

Precise RBL measurements are important for periodontal diagnosis. The proposed RBL-SF can measure RBL at specific tooth positions and assign the bone loss stage. The ability of the RBL-SF to measure RBL at specific tooth positions can guide clinicians to a certain extent in the accurate diagnosis of periodontitis.

Applications of artificial intelligence in the utilisation of imaging modalities in dentistry: A systematic review and meta-analysis of in-vitro studies

Background

In the past, dentistry heavily relied on manual image analysis and diagnostic procedures, which could be time-consuming and prone to human error. The advent of artificial intelligence (AI) has brought transformative potential to the field, promising enhanced accuracy and efficiency in various dental imaging tasks. This systematic review and meta-analysis aimed to comprehensively evaluate the applications of AI in dental imaging modalities, focusing on in-vitro studies.

Methods

A systematic literature search was conducted, in accordance with the PRISMA guidelines. The following databases were systematically searched: PubMed/MEDLINE, Embase, Web of Science, Scopus, IEEE Xplore, Cochrane Library, CINAHL (Cumulative Index to Nursing and Allied Health Literature), and Google Scholar. The meta-analysis employed fixed-effects models to assess AI accuracy, calculating odds ratios (OR) for true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), and negative predictive value (NPV) with 95 % confidence intervals (CI). Heterogeneity and overall effect tests were applied to ensure the reliability of the findings.

Results

9 studies were selected that encompassed various objectives, such as tooth segmentation and classification, caries detection, maxillofacial bone segmentation, and 3D surface model creation. AI techniques included convolutional neural networks (CNNs), deep learning algorithms, and AI-driven tools. Imaging parameters assessed in these studies were specific to the respective dental tasks. The analysis of combined ORs indicated higher odds of accurate dental image assessments, highlighting the potential for AI to improve TPR, TNR, PPV, and NPV. The studies collectively revealed a statistically significant overall effect in favor of AI in dental imaging applications.

Conclusion

In summary, this systematic review and meta-analysis underscore the transformative impact of AI on dental imaging. AI has the potential to revolutionize the field by enhancing accuracy, efficiency, and time savings in various dental tasks. While further research in clinical settings is needed to validate these findings and address study limitations, the future implications of integrating AI into dental practice hold great promise for advancing patient care and the field of dentistry.

Research and application of artificial intelligence in dentistry from lower-middle income countries - a scoping review

Artificial intelligence (AI) has been integrated into dentistry for improvement of current dental practice. While many studies have explored the utilization of AI in various fields, the potential of AI in dentistry, particularly in low-middle income countries (LMICs) remains understudied. This scoping review aimed to study the existing literature on the applications of artificial intelligence in dentistry in low-middle income countries. A comprehensive search strategy was applied utilizing three major databases: PubMed, Scopus, and EBSCO Dentistry & Oral Sciences Source. The search strategy included keywords related to AI, Dentistry, and LMICs. The initial search yielded a total of 1587, out of which 25 articles were included in this review. Our findings demonstrated that limited studies have been carried out in LMICs in terms of AI and dentistry. Most of the studies were related to Orthodontics. In addition gaps in literature were noted such as cost utility and patient experience were not mentioned in the included studies.

Deep learning-based tooth segmentation methods in medical imaging: A review

Deep learning approaches for tooth segmentation employ convolutional neural networks (CNNs) or Transformers to derive tooth feature maps from extensive training datasets. Tooth segmentation serves as a critical prerequisite for clinical dental analysis and surgical procedures, enabling dentists to comprehensively assess oral conditions and subsequently diagnose pathologies. Over the past decade, deep learning has experienced significant advancements, with researchers introducing efficient models such as U-Net, Mask R-CNN, and Segmentation Transformer (SETR). Building upon these frameworks, scholars have proposed numerous enhancement and optimization modules to attain superior tooth segmentation performance. This paper discusses the deep learning methods of tooth segmentation on dental panoramic radiographs (DPRs), cone-beam computed tomography (CBCT) images, intro oral scan (IOS) models, and others. Finally, we outline performance-enhancing techniques and suggest potential avenues for ongoing research. Numerous challenges remain, including data annotation and model generalization limitations. This paper offers insights for future tooth segmentation studies, potentially facilitating broader clinical adoption.

Deep-learning performance in identifying and classifying dental implant systems from dental imaging: a systematic review and meta-analysis

Deep learning (DL) offers promising performance in computer vision tasks and is highly suitable for dental image recognition and analysis. We evaluated the accuracy of DL algorithms in identifying and classifying dental implant systems (DISs) using dental imaging. In this systematic review and meta-analysis, we explored the MEDLINE/PubMed, Scopus, Embase, and Google Scholar databases and identified studies published between January 2011 and March 2022. Studies conducted on DL approaches for DIS identification or classification were included, and the accuracy of the DL models was evaluated using panoramic and periapical radiographic images. The quality of the selected studies was assessed using QUADAS-2. This review was registered with PROSPERO (CRDCRD42022309624). From 1,293 identified records, 9 studies were included in this systematic review and meta-analysis. The DL-based implant classification accuracy was no less than 70.75% (95% confidence interval [CI], 65.6%-75.9%) and no higher than 98.19 (95% CI, 97.8%-98.5%). The weighted accuracy was calculated, and the pooled sample size was 46,645, with an overall accuracy of 92.16% (95% CI, 90.8%-93.5%). The risk of bias and applicability concerns were judged as high for most studies, mainly regarding data selection and reference standards. DL models showed high accuracy in identifying and classifying DISs using panoramic and periapical radiographic images. Therefore, DL models are promising prospects for use as decision aids and decision-making tools; however, there are limitations with respect to their application in actual clinical practice.

Assessing the impact of occlusal plane rotation on facial aesthetics in orthodontic treatment: a machine learning approach

BACKGROUND

Adequate occlusal plane (OP) rotation through orthodontic therapy enables satisfying profile improvements for patients who are disturbed by their maxillomandibular imbalance but reluctant to surgery. The study aims to quantify profile improvements that OP rotation could produce in orthodontic treatment and whether the efficacy differs among skeletal types via machine learning.

MATERIALS AND METHODS

Cephalometric radiographs of 903 patients were marked and analyzed by trained orthodontists with assistance of Uceph, a commercial software which use artificial intelligence to perform the cephalometrics analysis. Back-propagation artificial neural network (BP-ANN) models were then trained based on collected samples to fit the relationship among maxillomandibular structural indicators, SN-OP and P-A Face Height ratio (FHR), Facial Angle (FA). After corroborating the precision and reliability of the models by T-test and Bland-Altman analysis, simulation strategy and matrix computation were combined to predict the consequent changes of FHR, FA to OP rotation. Linear regression and statistical approaches were then applied for coefficient calculation and differences comparison.

RESULTS

The regression scores calculating the similarity between predicted and true values reached 0.916 and 0.908 in FHR, FA models respectively, and almost all pairs were in 95% CI of Bland-Altman analysis, confirming the effectiveness of our models. Matrix simulation was used to ascertain the efficacy of OP control in aesthetic improvements. Intriguingly, though FHR change rate appeared to be constant across groups, in FA models, hypodivergent group displayed more sensitive changes to SN-OP than normodivergent, hypodivergent group, and Class III group significantly showed larger changes than Class I and II.

CONCLUSIONS

Rotation of OP could yield differently to facial aesthetic improvements as more efficient in hypodivergent groups vertically and Class III groups sagittally.

Applying machine learning to assess the morphology of sculpted teeth

Background/purpose

Producing tooth crowns through dental technology is a basic function of dentistry. The morphology of tooth crowns is the most important parameter for evaluating its acceptability. The procedures were divided into four steps: tooth collection, scanning skills, use of mathematical methods and software, and machine learning calculation.

Materials and methods

Dental plaster rods were prepared. The effective data collected were to classify 121 teeth (15th tooth position), 342 teeth (16th tooth position), 69 teeth (21st tooth position), and 89 teeth (43rd tooth position), for a total of 621 teeth. The procedures are divided into four steps: tooth collection, scanning skills, use of mathematical methods and software, and machine learning calculation.

Results

The area under the curve (AUC) value was 0, 0.5, and 0.72 in this study. The precision rate and recall rate of micro-averaging/macro-averaging were 0.75/0.73 and 0.75/0.72. If we took a newly carved tooth picture into the program, the current effectiveness of machine learning was about 70%-75% to evaluate the quality of tooth morphology. Through the calculation and analysis of the two different concepts of micro-average/macro-average and AUC, similar values could be obtained.

Conclusion

This study established a set of procedures that can judge the quality of hand-carved plaster sticks and teeth, and the accuracy rate is about 70%-75%. It is expected that this process can be used to assist dental technicians in judging the pros and cons of hand-carved plaster sticks and teeth, so as to help dental technicians to learn the tooth morphology more effectively.

Evaluation of the efficiency, trueness, and clinical application of novel artificial intelligence design for dental crown prostheses

OBJECTIVE

The unique structure of human teeth limits dental repair to custom-made solutions. The production process requires a lot of time and manpower. At present, artificial intelligence (AI) has begun to be used in the medical field and improve efficiency. This study attempted to design a variety of dental restorations using AI and evaluate their clinical applicability.

METHODS

Using inlay and crown restoration types commonly used in dental standard models, we compared differences in artificial wax-up carving (wax-up), artificial digital designs (digital) and AI designs (AI). The AI system was designed using computer calculations, and the other two methods were designed by humans. Restorations were made by 3D printing resin material. Image evaluations were compared with cone beam computed tomography (CBCT) by calculating the root mean squared error.

RESULTS

Surface truth results showed that AI (68.4 µm) and digital-designed crowns (51.0 µm) had better reproducibility. Using AI for the crown reduced the time spent by 400% (compared to digital) and 900% (compared to wax-up). Optical microscopic and CBCT images showed that AI and digital designs had close margin gaps (p < 0.05). The margin gap of the crown showed that the wax-up group was 4.1 and 4.3 times greater than those of the AI and digital crowns, respectively. Therefore, the utilization of artificial intelligence can assist in the production of dental restorations, thereby enhancing both production efficiency and accuracy.

SIGNIFICANCE

It is expected that the development of AI can contribute to the reproducibility, efficiency, and goodness of fit of dental restorations.

Clinically Oriented CBCT Periapical Lesion Evaluation via 3D CNN Algorithm

Apical periodontitis (AP) is one of the most prevalent disorders in dentistry. However, it can be underdiagnosed in asymptomatic patients. In addition, the perioperative evaluation of 3-dimensional (3D) lesion volume is of great clinical relevance, but the required slice-by-slice manual delineation method is time- and labor-intensive. Here, for quickly and accurately detecting and segmenting periapical lesions (PALs) associated with AP on cone beam computed tomography (CBCT) images, we proposed and geographically validated a novel 3D deep convolutional neural network algorithm, named PAL-Net. On the internal 5-fold cross-validation set, our PAL-Net achieved an area under the receiver operating characteristic curve (AUC) of 0.98. The algorithm also improved the diagnostic performance of dentists with varying levels of experience, as evidenced by their enhanced average AUC values (junior dentists: 0.89-0.94; senior dentists: 0.91-0.93), and significantly reduced the diagnostic time (junior dentists: 69.3 min faster; senior dentists: 32.4 min faster). Moreover, our PAL-Net achieved an average Dice similarity coefficient over 0.87 (0.85-0.88), which is superior or comparable to that of other existing state-of-the-art PAL segmentation algorithms. Furthermore, we validated the generalizability of the PAL-Net system using multiple external data sets from Central, East, and North China, showing that our PAL-Net has strong robustness. Our PAL-Net can help improve the diagnostic performance and speed of dentists working from CBCT images, provide clinically relevant volume information to dentists, and can potentially be applied in dental clinics, especially without expert-level dentists or radiologists.

Applying artificial intelligence to detect and analyse oral and maxillofacial bone loss-A scoping review

Radiographic evaluation of bone changes is one of the main tools in the diagnosis of many oral and maxillofacial diseases. However, this approach to assessment has limitations in accuracy, inconsistency and comparatively low diagnostic efficiency. Recently, artificial intelligence (AI)-based algorithms like deep learning networks have been introduced as a solution to overcome these challenges. Based on recent studies, AI can improve the detection accuracy of an expert clinician for periapical pathology, periodontal diseases and their prognostication, as well as peri-implant bone loss. Also, AI has been successfully used to detect and diagnose oral and maxillofacial lesions with a high predictive value. This study aims to review the current evidence on artificial intelligence applications in the detection and analysis of bone loss in the oral and maxillofacial regions.