Promises and perils of artificial intelligence in dentistry

Artificial intelligence (AI) in restorative dentistry: current trends and future prospects

BACKGROUND

Artificial intelligence (AI) holds immense potential in revolutionizing restorative dentistry, offering transformative solutions for diagnostic, prognostic, and treatment planning tasks. Traditional restorative dentistry faces challenges such as clinical variability, resource limitations, and the need for data-driven diagnostic accuracy. AI's ability to address these issues by providing consistent, precise, and data-driven solutions is gaining significant attention. This comprehensive literature review explores AI applications in caries detection, endodontics, dental restorations, tooth surface loss, tooth shade determination, and regenerative dentistry. While this review focuses on restorative dentistry, AI's transformative impact extends to orthodontics, prosthodontics, implantology, and dental biomaterials, showcasing its versatility across various dental specialties. Emerging trends such as AI-powered robotic systems, virtual assistants, and multi-modal data integration are paving the way for groundbreaking innovations in restorative dentistry.

METHODS

Methodologically, a systematic approach was employed, focusing on English-language studies published between 2020-2025(January), resulting in 63 peer-reviewed publications for analysis. Studies in caries detection, pedodontics, dental restorations, endodontics, tooth surface loss, and tooth shade determination highlighted AI trends and advancements. Inclusion criteria focused on AI applications in restorative dentistry, and publication timeframe. PRISMA guidelines were followed to ensure transparency in study selection, emphasizing on accuracy metrics and clinical relevance. The study selection process was carefully documented, and a flowchart of the stages, including identification, screening, eligibility, and inclusion, is shown in Fig. 1 to provide further clarity and reproducibility in the selection process.

RESULTS

The review identified significant advancements in AI-driven solutions across multiple domains of restorative dentistry. Notable studies demonstrated AI's ability to achieve high diagnostic accuracy, such as up to 95% accuracy in caries detection, and its capacity to improve treatment planning efficiency, thus reducing patient chair time. Predictive analytics for personalized treatments was another area where AI has shown substantial promise.

CONCLUSION

The review discussed trends, challenges, and future research directions in AI-driven dentistry, highlighting the transformative potential of AI in optimizing dental care. Key challenges include data privacy concerns, algorithmic bias, interpretability of AI decision-making processes, and the need for standardized AI training programs in dental education. Further research should focus on integrating AI with emerging technologies like 3D printing for personalized restorations, and developing AI training programs for dental professionals.

CLINICAL SIGNIFICANCE

The integration of AI into restorative dentistry offers precision-driven solutions for improved patient outcomes. By enabling faster diagnostics, personalized treatment approaches, and preventive care strategies, AI can significantly enhance patient-centered care and clinical efficiency. This review contributes to advancing the understanding and implementation of AI in dental practice by synthesizing key findings, identifying trends, and addressing challenges.

Integrating Machine Learning and Deep Learning for Predicting Non-Surgical Root Canal Treatment Outcomes Using Two-Dimensional Periapical Radiographs

Background/Objectives: In a previous study, we utilized categorical variables and machine learning (ML) algorithms to predict the success of non-surgical root canal treatments (NSRCTs) in apical periodontitis (AP), classifying the outcome as either success (healed) or failure (not healed). Given the importance of radiographic imaging in diagnosis, the present study evaluates the efficacy of deep learning (DL) in predicting NSRCT outcomes using two-dimensional (2D) periapical radiographs, comparing its performance with ML models. Methods: The DL model was trained and validated using leave-one-out cross-validation (LOOCV). Its output was incorporated into the set of categorical variables, and the ML study was reproduced using backward stepwise selection (BSS). The chi-square test was applied to assess the association between this new variable and NSRCT outcomes. Finally, after identifying the best-performing method from the ML study reproduction, statistical comparisons were conducted between this method, clinical professionals, and the image-based model using Fisher's exact test. Results: The association study yielded a p-value of 0.000000127, highlighting the predictive capability of 2D radiographs. After incorporating the DL-based predictive variable, the ML algorithm that demonstrated the best performance was logistic regression (LR), differing from the previous study, where random forest (RF) was the top performer. When comparing the deep learning-logistic regression (DL-LR) model with the clinician's prognosis (DP), DL-LR showed superior performance with a statistically significant difference (p-value < 0.05) in sensitivity, NPV, and accuracy. The same trend was observed in the DL vs. DP comparison. However, no statistically significant differences were found in the comparisons of RF vs. DL-LR, RF vs. DL, or DL vs. DL-LR. Conclusions: The findings of this study suggest that image-based artificial intelligence models exhibit superior predictive capability compared with those relying exclusively on categorical data. Moreover, they outperform clinician prognosis.

Augmented intelligence in oral and maxillofacial radiology: a systematic review

BACKGROUND

Artificial intelligence (AI) is transforming diagnostic imaging in dentistry. This systematic review evaluates existing literature on augmented intelligence in dentomaxillofacial radiology, focusing on its influence on human collaboration in interpreting dental imaging.

STUDY DESIGN

A literature search across seven databases and gray literature was conducted. Studies evaluating clinician performance with AI-assistance were included, while reviews, surveys, and case reports were excluded. The QUADAS-2 tool assessed the risk of bias.

RESULTS

Sixteen studies assessed the influence of AI on radiographic interpretation. AI-assisted caries detection consistently improved accuracy, sensitivity, and specificity. Detection of apical pathoses and jaw lesion segmentation improved accuracy, reducing diagnostic time. Cephalometric landmark identification showed increased accuracy, particularly for students. Soft tissue calcification detection improved accuracy, but sensitivity decreased. Overall, augmented intelligence enhanced interobserver agreement and reduced diagnostic variability, with general dentists and students showing the greatest gains.

CONCLUSIONS

Augmented intelligence enhances dental radiographic interpretation by improving tasks, particularly for less experienced clinicians, and positively influences clinical decision-making. However, AI performance remains inconsistent in challenging cases involving complex pathoses or varied imaging conditions. While it complements rather than replaces clinicians, further validation of AI's generalizability and reliability using larger, diverse datasets is necessary.

AI Efficiency in Dentistry: Comparing Artificial Intelligence Systems with Human Practitioners in Assessing Several Periodontal Parameters

Artificial intelligence (AI) is increasingly used in healthcare, including dental and periodontal diagnostics, due to its ability to analyze complex datasets with speed and precision. Backgrounds and Objectives: This study aimed to evaluate the reliability of AI-assisted dental-periodontal diagnoses compared to diagnoses made by senior specialists, specialists, and general dentists. Material and Methods: A comparative study was conducted involving 60 practitioners divided into three groups-general dentists, specialists, and senior specialists-along with an AI diagnostic system (Planmeca Romexis 6.4.7.software). Participants evaluated six high-quality panoramic radiographic images representing various dental and periodontal conditions. Diagnoses were compared against a reference "gold standard" validated by a dental imaging expert and senior clinician. A statistical analysis was performed using SPSS 26.0, applying chi-square tests, ANOVA, and Bonferroni correction to ensure robust results. Results: AI's consistency in identifying subtle conditions was comparable to that of senior specialists, while general dentists showed greater variability in their evaluations. The key findings revealed that AI and senior specialists consistently demonstrated the highest performance in detecting attachment loss and alveolar bone loss, with AI achieving a mean score of 6.12 in identifying teeth with attachment loss, compared to 5.43 for senior specialists, 4.58 for specialists, and 3.65 for general dentists. The ANOVA highlighted statistically significant differences between groups, particularly in the detection of attachment loss on the maxillary arch (F = 3.820, p = 0.014). Additionally, AI showed high consistency in detecting alveolar bone loss, with comparable performance to senior specialists. Conclusions: AI systems exhibit significant potential as reliable tools for dental and periodontal assessment, complementing the expertise of human practitioners. However, further validation in clinical settings is necessary to address limitations such as algorithmic bias and atypical cases. AI integration in dentistry can enhance diagnostic precision and patient outcomes while reducing variability in clinical assessments.

Deep learning algorithms for detecting fractured instruments in root canals

BACKGROUND

Identifying fractured endodontic instruments (FEIs) in periapical radiographs (PAs) is a critical yet challenging aspect of root canal treatment (RCT) due to anatomical complexities and overlapping structures. Deep learning (DL) models offer potential solutions, yet their comparative performance in this domain remains underexplored.

METHODS

A dataset of 700 annotated PAs, including 381 teeth with FEIs, was divided into training, validation, and test sets (60/20/20 split). Five DL models-DenseNet201, EfficientNet B0, ResNet-18, VGG-19, and MaxVit-T-were trained using transfer learning and data augmentation techniques. Performance was evaluated using accuracy, AUC and MCC. Statistical analysis included the Friedman test with post-hoc corrections.

RESULTS

DenseNet201 achieved the highest AUC (0.900) and MCC (0.810), outperforming other models in FEI detection. ResNet-18 demonstrated robust results, while EfficientNet B0 and VGG-19 provided moderate performance. MaxVit-T underperformed, with metrics near random guessing. Statistical analysis revealed significant differences among models (p < 0.05), but pairwise comparisons were not significant.

CONCLUSIONS

DenseNet201's superior performance highlights its clinical potential for FEI detection, while ResNet-18 offers a balance between accuracy and computational efficiency. The findings highlight the need for model-task alignment and optimization in medical imaging applications.

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.

Acceptability of artificial intelligence in dental radiology among patients in India: are we ready for this revolution?

OBJECTIVE

In recent times, artificial Intelligence (AI) has gained popularity in medical as well as dental radiology. Studies have been conducted among medical and dental students and professionals about the knowledge and understanding towards AI. The aim of this study was to investigate the perceptions and acceptability of AI in dental radiology among a group of Indian patients seeking dental treatment.

METHODS

A cross-sectional research was planned with a validated questionnaire, containing ten close ended questions amongst 1562 patients. Their sociodemographic characters, opinions and attitudes regarding AI and feasibility of acceptance of AI-based dental radiological diagnosis among patients was evaluated. The study sample was divided in various groups on the basis of their age; group-1(16-30 years), group-2(31-45 years) and group-3(>45 years), educational status and urban/rural background. Statistical analysis was done by Chi-square test with significance value set at p< 0.005.

RESULTS-

The participants possessed impressive knowledge about AI. Patients' awareness, attitudes and acceptability towards AI for dental radiographic diagnosis were substantially influenced by age, education level and residential background. Although many of them, especially the urban and more educated participants believed that AI could be more accurate, they preferred the human judgement. Overall, a negative attitude in terms of acceptability of AI in dental radiology was observed in this study.

CONCLUSIONS

Participants opined that AI should only be used as an auxiliary tool and valued clinical judgment over AI in ambiguous situations. It is recommended that this promising technological advancement can be used for initial screening in dental radiology.

Using AI in Optimizing Oral and Dental Diagnoses-A Narrative Review

Artificial intelligence (AI) is revolutionizing the field of oral and dental healthcare by offering innovative tools and techniques for optimizing diagnosis, treatment planning, and patient management. This narrative review explores the current applications of AI in dentistry, focusing on its role in enhancing diagnostic accuracy and efficiency. AI technologies, such as machine learning, deep learning, and computer vision, are increasingly being integrated into dental practice to analyze clinical images, identify pathological conditions, and predict disease progression. By utilizing AI algorithms, dental professionals can detect issues like caries, periodontal disease and oral cancer at an earlier stage, thus improving patient outcomes.

Understanding Occlusion and Temporomandibular Joint Function Using Deep Learning and Predictive Modeling

OBJECTIVES

Advancements in artificial intelligence (AI)-driven predictive modeling in dentistry are outpacing the clinical translation of research findings. Predictive modeling uses statistical methods to anticipate norms related to TMJ dynamics, complementing imaging modalities like cone beam computed tomography (CBCT) and magnetic resonance imaging (MRI). Deep learning, a subset of AI, helps quantify and analyze complex hierarchical relationships in occlusion and TMJ function. This narrative review explores the application of predictive modeling and deep learning to identify clinical trends and associations related to occlusion and TMJ function.

RESULTS

Debates persist regarding best practices for managing occlusal factors in temporomandibular joint (TMJ) function analysis while interpreting and quantifying findings related to the TMJ and occlusion and mitigating biases remain challenging. Data generated from noninvasive chairside tools such as jaw trackers, video tracking, and 3D scanners with virtual articulators offer unique insights by predicting variations in dynamic jaw movement, TMJ, and occlusion. The predictions help us understand the highly individualized norms surrounding TMJ function that are often required to address temporomandibular disorders (TMDs) in general practice.

CONCLUSIONS

Normal TMJ function, occlusion, and the appropriate management of TMDs are complex and continue to attract ongoing debate. This review examines how predictive modeling and artificial intelligence aid in understanding occlusion and TMJ function and provides insights into complex dental conditions such as TMDs that may improve diagnosis and treatment outcomes with noninvasive techniques.

Australian Dentists' Knowledge of the Consequences of Interpretive Errors in Dental Radiographs and Potential Mitigation Measures

OBJECTIVES

Dental radiographs, typically taken and interpreted by dentists, are essential for diagnosis and effective treatment planning. Interpretive errors in dental radiographs, stemming from failures of visual and cognitive processes, can affect both patients and clinicians. This survey aimed to assess the dental practitioners' perceptions of the consequences of these errors and potential measures to minimize them.

MATERIALS AND METHODS

This online anonymized survey assessed Australian dental practitioners' perceptions of the consequences of these errors and potential mitigation measures using ranking, Likert scale, and open-ended questions. The data were analyzed using descriptive statistics and bivariate analysis.

RESULTS

Participants identified undertreatment (72%) and legal implications (82%) as the most significant consequences of interpretive errors, whereas severe harm to patients was deemed the least likely. Dental practitioners placed a greater emphasis on maintaining a high level of competence and the well-being of their patients. Utilizing high-quality images (63.9%) and appropriate radiographs (59.7%) were identified as the most effective measures to minimize interpretive errors. Participants showed hesitancy regarding the reliance on machine learning as a clinical decision-making tool.

CONCLUSIONS

The survey provides valuable practical insights into the consequences and targeted measures to minimize the occurrence of interpretive errors. Efforts to minimize interpretive errors should address patient safety and practitioners' concerns about professional reputation and business viability. The study also suggests further research into the role of machine learning algorithms in reducing interpretive errors in dentistry.

Can deep learning identify humans by automatically constructing a database with dental panoramic radiographs?

The aim of this study was to propose a novel method to identify individuals by recognizing dentition change, along with human identification process using deep learning. Recent and past images of adults aged 20-49 years with more than two dental panoramic radiographs (DPRs) were assumed as postmortem (PM) and antemortem (AM) images, respectively. The dataset contained 1,029 paired PM-AM DPRs from 2000 to 2020. After constructing a database of AM dentition, the degree of similarity was calculated and sorted in descending order. The matched rank of AM identical to an unknown PM was measured by extracting candidate groups (CGs). The percentage of rank was calculated as the success rate, and similarity scores were compared based on imaging time intervals. The matched AM images were ranked in the CG with success rates of 83.2%, 72.1%, and 59.4% in the imaging time interval for extracting the top 20.0%, 10.0%, and 5.0%, respectively. The success rates depended on sex, and were higher for women than for men: the success rates for the extraction of the top 20.0%, 10.0%, and 5.0% were 97.2%, 81.1%, and 66.5%, respectively, for women and 71.3%, 64.0%, and 52.0%, respectively, for men. The similarity score differed significantly between groups based on the imaging time interval of 17.7 years. This study showed outstanding performance of convolutional neural network using dental panoramic radiographs in effectively reducing the size of AM CG in identifying humans.

Application of 3D neural networks and explainable AI to classify ICDAS detection system on mandibular molars

STATEMENT OF PROBLEM

Considerable variations exist in cavity preparation methods and approaches. Whether the extent and depth of cavity preparation because of the extent of caries affects the overall accuracy of training deep learning models remains unexplored.

PURPOSE

The purpose of this study was to investigate the difference in 3-dimensionsal (3D) model cavity preparations after International Caries Detection and Assessment System (ICDAS) classification performed by different practitioners and the subsequent influence on the ability of a deep learning model to predict cavity classification.

MATERIAL AND METHODS

Two operators prepared 56 restorative cavities on simulated mandibular first molars according to 4 ICDAS classifications, followed by 3D scanning and computer-aided design processing. The surface area, virtual volume, Hausdorff distance (HD), and Dice Similarity Coefficients were computed. Multivariate analysis of variance was used to assess cavity size and operator proficiency interactions, and 1-way ANOVA was used to evaluate HD differences across 4 cavity classifications (α=.05). The 3D convolutional neural network (CNN) predicted the ICDAS class, and Saliency Maps explained the decisions of the models.

RESULTS

Operator 1 exhibited a cavity preparation surface area of 360.55 ±15.39 mm2, and operator 2 recorded 355.24 ±10.79 mm2. Volumetric differences showed operator 1 with 440.41 ±35.29 mm3 and operator 2 with 441.01 ±35.37 mm3. Significant interactions (F=2.31, P=.01) between cavity size and operator proficiency were observed. A minimal 0.13 ±0.097 mm variation was noted in overlapping preparations by the 2 operators. The 3D CNN model achieved an accuracy of 94.44% in classifying the ICDAS classes with a 66.67% accuracy when differentiating cavities prepared by the 2 operators.

CONCLUSIONS

Operator performance discrepancies were evident in the occlusal cavity floor, primarily due to varying cavity depths. Deep learning effectively classified cavity depths from 3D intraoral scans and was less affected by preparation quality or operator skills.

The Use of Artificial Intelligence in Caries Detection: A Review

Advancements in artificial intelligence (AI) have significantly impacted the field of dentistry, particularly in diagnostic imaging for caries detection. This review critically examines the current state of AI applications in caries detection, focusing on the performance and accuracy of various AI techniques. We evaluated 40 studies from the past 23 years, carefully selected for their relevance and quality. Our analysis highlights the potential of AI, especially convolutional neural networks (CNNs), to improve diagnostic accuracy and efficiency in detecting dental caries. The findings underscore the transformative potential of AI in clinical dental practice.

The Role and Applications of Artificial Intelligence in Dental Implant Planning: A Systematic Review

Artificial intelligence (AI) is revolutionizing dentistry, offering new opportunities to improve the precision and efficiency of implantology. This literature review aims to evaluate the current evidence on the use of AI in implant planning assessment. The analysis was conducted through PubMed and Scopus search engines, using a combination of relevant keywords, including "artificial intelligence implantology", "AI implant planning", "AI dental implant", and "implantology artificial intelligence". Selected articles were carefully reviewed to identify studies reporting data on the effectiveness of AI in implant planning. The results of the literature review indicate a growing interest in the application of AI in implant planning, with evidence suggesting an improvement in precision and predictability compared to traditional methods. The summary of the obtained findings by the included studies represents the latest AI developments in implant planning, demonstrating its application for the automated detection of bones, the maxillary sinus, neuronal structure, and teeth. However, some disadvantages were also identified, including the need for high-quality training data and the lack of standardization in protocols. In conclusion, the use of AI in implant planning presents promising prospects for improving clinical outcomes and optimizing patient management. However, further research is needed to fully understand its potential and address the challenges associated with its implementation in clinical practice.

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.

Data-driven prediction model for periodontal disease based on correlational feature analysis and clinical validation

Objectives

This study aimed to investigate the performance and reliability of data-driven models employing correlational feature analysis and clinical validation for predicting periodontal disease.

Methods

The 7th Korea National Health and Nutrition Examination Survey (n = 10,654) was used for correlation analysis to identify significant risk factors for periodontitis. Periodontal prediction models were developed with the selected factors and database, followed by internal validation with 5-fold cross-validation and 1000 bootstrap resampling. External validation was conducted with clinical data (n = 120) collected through self-reported questionnaires, clinical periodontal parameters, and radiographic image analysis. Predictive performance was assessed for logistics regression, support vector machine, random forest, XGBoost, and neural network algorithms using the area under the receiver operating characteristic curves (AUC) and other performance metrics.

Results

Correlation analysis identified 16 features from over 1000 potential risk factors for periodontitis. The best data-driven model (XGBoost) showed AUC values of 0.823 and 0.796 for internal and external validations, respectively. Modeling with clinical data revealed those same measures to be 0.836 and 0.649, respectively. In addition, the data-driven model could predict other clinical periodontal parameters including severe bone loss (AUC = 0.813), gingival bleeding (AUC = 0.694), and tooth loss (AUC = 0.734). A patient case study about prognostic predictions revealed that the probability of periodontitis can be reduced by 6.0 % (stop smoking) and 0.6 % (stop drinking) on average.

Conclusions

Data-driven models for predicting periodontitis and other periodontal parameters were developed from 16 risk factors, demonstrating enhanced prediction performance and reproducibility in internal-external validations.

Primary care dentistry: Past, present and future

This article examines the past, present and future of primary care dentistry. It provides a historical background of primary care dentistry and describes stages of its evolution. It further reviews the purpose and mission of contemporary primary care dentistry and outlines a vision for the development of primary care dentistry in the future. The type and extent of innovations and technological advances that have impacted - and improved - primary care dentistry revolutionising clinical activities, ranging from early computerised tomography to modern digital systems and workflows are summarised. A discussion of current scientific evidence base pertinent to primary care dentistry highlighting the need for 'effectiveness' rather than 'efficacy' studies is included in order to provide research data pertinent to the primary care dentistry setting where most dental patients receive most of their care most of the time.

Artificial intelligence for treatment planning and soft tissue outcome prediction of orthognathic treatment: A systematic review

BACKGROUND

The accuracy of artificial intelligence (AI) in treatment planning and outcome prediction in orthognathic treatment (OGT) has not been systematically reviewed.

OBJECTIVES

To determine the accuracy of AI in treatment planning and soft tissue outcome prediction in OGT.

DESIGN

Systematic review.

DATA SOURCES

Unrestricted search of indexed databases and reference lists of included studies.

DATA SELECTION

Clinical studies that addressed the focused question 'Is AI useful for treatment planning and soft tissue outcome prediction in OGT?' were included.

DATA EXTRACTION

Study screening, selection and data extraction were performed independently by two authors. The risk of bias (RoB) was assessed using the Cochrane Collaboration's RoB and ROBINS-I tools for randomised and non-randomised clinical studies, respectively.

DATA SYNTHESIS

Eight clinical studies (seven retrospective cohort studies and one randomised controlled study) were included. Four studies assessed the role of AI for treatment decision making; and four studies assessed the accuracy of AI in soft tissue outcome prediction after OGT. In four studies, the level of agreement between AI and non-AI decision making was found to be clinically acceptable (at least 90%). In four studies, it was shown that AI can be used for soft tissue outcome prediction after OGT; however, predictions were not clinically acceptable for the lip and chin areas. All studies had a low to moderate RoB.

LIMITATIONS

Due to high methodological inconsistencies among the included studies, it was not possible to conduct a meta-analysis and reporting biases assessment.

CONCLUSION

AI can be a useful aid to traditional treatment planning by facilitating clinical treatment decision making and providing a visualisation tool for soft tissue outcome prediction in OGT.

REGISTRATION

PROSPERO CRD42022366864.

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.

Perceptions in Digital Smile Design: Assessing Laypeople and Dental Professionals' Preferences Using an Artificial-Intelligence-Based Application

Digital Smile Design (DSD) is used in many fields of dentistry. This prospective observational study assessed laypeople's and dental professionals' perceptions of a DSD application. SmileCloud, an online DSD platform, was used to create two different designs for three patients; after that, the participants, in a 30-question online illustrated survey, were asked about the most attractive design and other features of the smile. Dentists' and laypeople's perceptions about specific DSD features were assessed. The Kolmogorov-Smirnov normality test was used. Descriptive and crosstab analyses compared the respondents' opinions for each statement. Chi-square tests were used to determine the relationship between the questions and any association with age, gender, and profession. The test results were rated as significant at a p-value < 0.05. A total of 520 participants (dental professionals, students, dental technicians, and laypeople) were enrolled. The statistically significant features were self-esteem related to appearance (p = 0.05), facial and smile symmetry (p = 0.42, p < 0.0001), tooth color (p = 0.012), and symmetry of gums (p < 0.001). For each patient, the design with dominant round upper incisors and perfect symmetry was preferred (p < 0.001). Digital pre-visualization benefits diagnosis and enriches treatment planning. The dentist-dental technician-patient team should be involved in the decision-making process of pre-visualization.

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.

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.

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.

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.

Current Progress and Challenges of Using Artificial Intelligence in Clinical Dentistry-A Narrative Review

The concept of machines learning and acting like humans is what is meant by the phrase "artificial intelligence" (AI). Several branches of dentistry are increasingly relying on artificial intelligence (AI) tools. The literature usually focuses on AI models. These AI models have been used to detect and diagnose a wide range of conditions, including, but not limited to, dental caries, vertical root fractures, apical lesions, diseases of the salivary glands, maxillary sinusitis, maxillofacial cysts, cervical lymph node metastasis, osteoporosis, cancerous lesions, alveolar bone loss, the need for orthodontic extractions or treatments, cephalometric analysis, age and gender determination, and more. The primary contemporary applications of AI in the dental field are in undergraduate teaching and research. Before these methods can be used in everyday dentistry, however, the underlying technology and user interfaces need to be refined.

Revolutionising dental technologies: a qualitative study on dental technicians' perceptions of Artificial intelligence integration

BACKGROUND

The integration of artificial intelligence (AI) in dentistry has the potential to revolutionise the field of dental technologies. However, dental technicians' views on the use of AI in dental technology are still sparse in the literature. This qualitative study aimed to explore the perceptions of dental technicians regarding the use of AI in their dental laboratory practice.

METHODS

Twelve dental technicians with at least five years of professional experience and currently working in Malaysia agreed to participate in the one-to-one in-depth online interviews. Interviews were recorded, transcribed verbatim and translated. Thematic analysis was conducted to identify patterns, themes, and categories within the interview transcripts.

RESULTS

The analysis revealed two key themes: "Perceived Benefits of AI" and "Concerns and Challenges". Dental technicians recognised the enhanced efficiency, productivity, accuracy, and precision that AI can bring to dental laboratories. They also acknowledged the streamlined workflow and improved communication facilitated by AI systems. However, concerns were raised regarding job security, professional identity, ethical considerations, and the need for adequate training and support.

CONCLUSION

This research sheds light on the potential benefits and challenges associated with the integration of AI in dental laboratory practices. Understanding these perceptions and addressing the challenges can support the effective integration of AI in dental laboratories and contribute to the growing body of literature on AI in healthcare.

Artificial intelligence: is it more accurate than endodontists in root canal therapy?

DATA SOURCES

The following databases were electronically searched (up to 20 March 2022): PubMed, Scopus, Google Scholar, and Cochrane Library. This was followed by hand-searching the reference lists of the included articles. The search was restricted to articles published in English. The aim of this study was to evaluate the effectiveness of artificial intelligence in identifying, analyzing, and interpreting radiographic features related to endodontic therapy.

STUDY SELECTION

The selection criteria were limited to trials evaluating the effectiveness of artificial intelligence in identifying, analyzing, and interpreting radiographic features related to endodontic therapy.

TYPES OF STUDIES

Clinical, ex-vivo, and in-vitro trials.

TYPES OF RADIOGRAPHIC IMAGES

Two-dimensional intra-oral imaging (bitewings and/or periapicals), panoramic radiographs (PRs), and cone beam computed tomography (CBCT).

EXCLUSION CRITERIA

1) Case reports, letters, and commentaries; 2) Reviews, conferences, and books; 3) Inaccessible reports.

DATA EXTRACTION AND SYNTHESIS

The titles and abstracts of the results of the searches were screened by two authors against the inclusion criteria. The full text of any potentially relevant abstract and title were retrieved for more comprehensive assessment. The risk of bias was assessed initially by two examiners and then by two authors. Any discrepancies were resolved through discussion and consensus.

RESULTS

Out of the 1131 articles which were identified in the initial search, 30 were considered relevant, and only 24 articles were eventually included. The exclusion of the six articles was related to the absence of appropriate clinical or radiological data. Meta-analysis was not performed due to high heterogeneity. Various degrees of bias were detected in more than 58% of the included studies.

CONCLUSIONS

Although most of the included studies were biased, the authors concluded that the use of artificial intelligence can be an effective alternative in identifying, analyzing and interpreting radiographic features related to root canal therapy.

Attitude, perception and barriers of dental professionals towards artificial intelligence

Aim

To know attitudes, perceptions and barriers towards the use of Artificial Intelligence (AI) in dentistry in India among undergraduate and postgraduate students.

Methodology

A questionnaire-based cross-sectional study was conducted among participants pursuing graduation and postgraduation. The questionnaire consisted of 23 close-ended and 2 open-ended questions divided into various sections of attitude, perception and barriers. The data was analysed using Statistical Package for Social Sciences (SPSS) version 24.0.

Result

Out of 937 responses, 55.2% responded that they get information about AI from social media platforms. 51.3% of respondents have basic knowledge about the use of AI in dentistry. 59.6% agreed that AI can be used as a "definitive diagnostic tool" in the diagnosis of diseases. 66.5% agreed that AI can be used for radiographic diagnosis of tooth caries. 71.3% stated that AI can be used as a "treatment planning tool" in dentistry. 55.7% stated that AI should be part of undergraduate dental training.

Conclusion

This study concluded that both dental students are aware of the concept of AI. Participants were positive when asked if AI can increase the efficiency of diagnosis, prognosis and treatment planning procedures as well as in managing patient data. Both participants believed that the barriers to the introduction of AI in dentistry are a lack of technical resources and a lack of training personnel in college.

The use of artificial intelligence to aid in oral hygiene education: A scoping review

OBJECTIVES

Artificial intelligence (AI) has the potential to aid in constant, non-invasive monitoring of daily oral hygiene practices, potentially on behalf of a dentist or healthcare provider. This review summarises the evidence around the use of AI in the context of oral hygiene education.

DATA & SOURCES

This scoping review was developed according to the Joanna Briggs Institute scoping review protocol guidelines and the PRISMA-ScR guidelines. Publications that involved the use of AI for oral hygiene education in any population and setting were included. A systematic electronic database search (MEDLINE via PubMed, EMBASE, Web of Science, Scopus, Cochrane Library, and IEEE Xplore, arXiv, Proquest, Google Scholar, ClinicalTrials.gov, and PROSPERO) up to, and including 4 February 2023 was carried out. Citation searching from the full-text of included publications was also performed.

RESULTS

Of the 3215 publications screened, 20 were selected for qualitative synthesis. These were broadly divided into two categories of AI-assisted feedback: (1) synchronous and (2) asynchronous monitoring. There is a lack of high-quality studies, scarce reflection on possible ethical concerns on AI, and of studies comparing qualitative feedback to quantitative clinical outcomes with a control. Barriers to adoption of AI technologies, patient privacy, and specific areas for improvement were identified.

CONCLUSION

Within the limitations of this study, the use of AI to modify oral hygiene behaviour is promising. Further work is required in generating higher quality intra-oral images for dental biofilm detection, and in developing more personalised feedback for users.

CLINICAL SIGNIFICANCE

This is the first review to map out the available literature on AI in providing oral hygiene education. It may be useful to dental researchers in appraising AI-assisted technologies in the context of oral health.

Patients' perspectives on the use of artificial intelligence in dentistry: a regional survey

The use of artificial intelligence (AI) in dentistry is rapidly evolving and could play a major role in a variety of dental fields. This study assessed patients' perceptions and expectations regarding AI use in dentistry. An 18-item questionnaire survey focused on demographics, expectancy, accountability, trust, interaction, advantages and disadvantages was responded to by 330 patients; 265 completed questionnaires were included in this study. Frequencies and differences between age groups were analysed using a two-sided chi-squared or Fisher's exact tests with Monte Carlo approximation. Patients' perceived top three disadvantages of AI use in dentistry were (1) the impact on workforce needs (37.7%), (2) new challenges on doctor-patient relationships (36.2%) and (3) increased dental care costs (31.7%). Major expected advantages were improved diagnostic confidence (60.8%), time reduction (48.3%) and more personalised and evidencebased disease management (43.0%). Most patients expected AI to be part of the dental workflow in 1-5 (42.3%) or 5-10 (46.8%) years. Older patients (> 35 years) expected higher AI performance standards than younger patients (18-35 years) (p < 0.05). Overall, patients showed a positive attitude towards AI in dentistry. Understanding patients' perceptions may allow professionals to shape AI-driven dentistry in the future.

Prediction of chemotherapy-related complications in pediatric oncology patients: artificial intelligence and machine learning implementations

Although the overall incidence of pediatric oncological diseases tends to increase over the years, it is among the rare diseases of the pediatric population. The diagnosis, treatment, and healthcare management of this group of diseases are important. Prevention of treatment-related complications is vital for patients, particularly in the pediatric population. Nowadays, the use of artificial intelligence and machine learning technologies in the management of oncological diseases is becoming increasingly important. With the advancement of software technologies, improvements have been made in the early diagnosis of risk groups in oncological diseases, in radiology, pathology, and imaging technologies, in cancer staging and management. In addition, these technologies can be used to predict the outcome in chemotherapy treatment of oncological diseases. In this context, this study identifies artificial intelligence and machine learning methods used in the prediction of complications due to chemotherapeutic agents used in childhood cancer treatment. For this purpose, the concepts of artificial intelligence and machine learning are explained in this review. A general framework for the use of machine learning in healthcare and pediatric oncology has been drawn and examples of studies conducted on this topic in pediatric oncology have been given. IMPACT: Artificial intelligence and machine learning are advanced tools that can be used to predict chemotherapy-related complications. Algorithms can assist clinicians' decision-making processes in the management of complications. Although studies are using these methods, there is a need to increase the number of studies on artificial intelligence applications in pediatric clinics.

Neural network system for analyzing statistical factors of patients for predicting the survival of dental implants

Implants are now the standard method of replacing missing or damaged teeth. Despite the improving technologies for the manufacture of implants and the introduction of new protocols for diagnosing, planning, and performing implant placement operations, the percentage of complications in the early postoperative period remains quite high. In this regard, there is a need to develop new methods for preliminary assessment of the patient's condition to predict the success of single implant survival. The intensive development of artificial intelligence technologies and the increase in the amount of digital information that is available for analysis make it relevant to develop systems based on neural networks for auxiliary diagnostics and forecasting. Systems based on artificial intelligence in the field of dental implantology can become one of the methods for forming a second opinion based on mathematical decision making and forecasting. The actual clinical evaluation of a particular case and further treatment are carried out by the dentist, and AI-based systems can become an integral part of additional diagnostics. The article proposes an artificial intelligence system for analyzing various patient statistics to predict the success of single implant survival. As the topology of the neural network, the most optimal linear neural network architectures were developed. The one-hot encoding method was used as a preprocessing method for statistical data. The novelty of the proposed system lies in the developed optimal neural network architecture designed to recognize the collected and digitized database of various patient factors based on the description of the case histories. The accuracy of recognition of statistical factors of patients for predicting the success of single implants in the proposed system was 94.48%. The proposed neural network system makes it possible to achieve higher recognition accuracy than similar neural network prediction systems due to the analysis of a large number of statistical factors of patients. The use of the proposed system based on artificial intelligence will allow the implantologist to pay attention to the insignificant factors affecting the quality of the installation and the further survival of the implant, and reduce the percentage of complications at all stages of treatment. However, the developed system is not a medical device and cannot independently diagnose patients. At this point, the neural network system for analyzing the statistical factors of patients can predict a positive or negative outcome of a single dental implant operation and cannot be used as a full-fledged tool for supporting medical decision-making.

Automatic detection of the mental foramen for estimating mandibular cortical width in dental panoramic radiographs: the seventh survey of the Tromsø Study (Tromsø7) in 2015–2016

Objective

To apply deep learning to a data set of dental panoramic radiographs to detect the mental foramen for automatic assessment of the mandibular cortical width.

Methods

Data from the seventh survey of the Tromsø Study (Tromsø7) were used. The data set contained 5197 randomly chosen dental panoramic radiographs. Four pretrained object detectors were tested. We randomly chose 80% of the data for training and 20% for testing. Models were trained using GeForce RTX 2080 Ti with 11 GB GPU memory (NVIDIA Corporation, Santa Clara, CA, USA). Python programming language version 3.7 was used for analysis.

Results

The EfficientDet-D0 model showed the highest average precision of 0.30. When the threshold to regard a prediction as correct (intersection over union) was set to 0.5, the average precision was 0.79. The RetinaNet model achieved the lowest average precision of 0.23, and the precision was 0.64 when the intersection over union was set to 0.5. The procedure to estimate mandibular cortical width showed acceptable results. Of 100 random images, the algorithm produced an output 93 times, 20 of which were not visually satisfactory.

Conclusions

EfficientDet-D0 effectively detected the mental foramen. Methods for estimating bone quality are important in radiology and require further development.

Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis

This literature research had two main objectives. The first objective was to quantify how frequently artificial intelligence (AI) was utilized in dental literature from 2011 until 2021. The second objective was to distinguish the focus of such publications; in particular, dental field and topic. The main inclusion criterium was an original article or review in English focused on dental utilization of AI. All other types of publications or non-dental or non-AI-focused were excluded. The information sources were Web of Science, PubMed, Scopus, and Google Scholar, queried on 19 April 2022. The search string was “artificial intelligence” AND (dental OR dentistry OR tooth OR teeth OR dentofacial OR maxillofacial OR orofacial OR orthodontics OR endodontics OR periodontics OR prosthodontics). Following the removal of duplicates, all remaining publications were returned by searches and were screened by three independent operators to minimize the risk of bias. The analysis of 2011–2021 publications identified 4413 records, from which 1497 were finally selected and calculated according to the year of publication. The results confirmed a historically unprecedented boom in AI dental publications, with an average increase of 21.6% per year over the last decade and a 34.9% increase per year over the last 5 years. In the achievement of the second objective, qualitative assessment of dental AI publications since 2021 identified 1717 records, with 497 papers finally selected. The results of this assessment indicated the relative proportions of focal topics, as follows: radiology 26.36%, orthodontics 18.31%, general scope 17.10%, restorative 12.09%, surgery 11.87% and education 5.63%. The review confirms that the current use of artificial intelligence in dentistry is concentrated mainly around the evaluation of digital diagnostic methods, especially radiology; however, its implementation is expected to gradually penetrate all parts of the profession.

The Contemporary Management of Cleft Lip and Palate and the Role of Artificial Intelligence: A Review

Introduction:

Cleft management is an exhaustive process for the patient, the orthodontist, and the caregiver. In recent decades, a wide number of challenges have been addressed with the inclusion of various dental specialties for the detection, diagnosis, and treatment of orofacial clefts. The orthodontist plays a very pivotal role during the overall management of children with cleft lip and palate as they need to make critical decisions for when to intervene orthodontically and at what stage to set priorities for individual treatment goals.

Objectives:

The objectives of this study were to provide an in-depth review of the evolving role of various disciplines focusing on orthodontics in the management of cleft cases.

Methods:

A general search was carried out to identify the published data on cleft lip and cleft palate management on PubMed and Scopus until the 1st of June 2021 using keywords such as cleft lip, cleft palate, cleft orthodontics, naso-alveolar molding, and surgical cleft orthodontics. The related literature was then reviewed and analyzed.

Results:

With improvements in 3D modeling, CT scans of patients can be used to construct precise 3D models, and these can be utilized to demonstrate various clinical issues related to clefts. The orthodontist has a major role in the various stages and steps, follow-up, treatment care, and outcome assessment. With the advent of technological advancements and artificial intelligence, the role is only going to evolve and expand further in the management of the cleft lip and palate. Diagnostic techniques utilizing artificial intelligence to detect cleft during the prenatal period have also been tested and have been shown to have a high rate of accuracy. The evolution of distraction osteogenesis came into the limelight as a revolutionary modality for cleft treatment. Computer-assisted orthognathic surgery is a widely used modality for reshaping the osseous defects of the maxilla in patients with congenital clefts. With the development of additional modalities such as aligners, patients that need to undergo complex orthognathic surgeries can also be treated with aligners without compromising the outcomes.

Conclusion:

The cleft lip and palate can be managed by a multi-disciplinary team. Orthodontics has an important role in the overall management of a cleft affected individual as they must make critical decisions regarding orthodontic interventions as well as set priorities for each treatment goal. With the advent of technological advancements and artificial intelligence, the diagnosis and management of the cleft lip and palate have become simplified.

Diagnosis of Tooth Prognosis Using Artificial Intelligence

The accurate diagnosis of individual tooth prognosis has to be determined comprehensively in consideration of the broader treatment plan. The objective of this study was to establish an effective artificial intelligence (AI)-based module for an accurate tooth prognosis decision based on the Harvard School of Dental Medicine (HSDM) comprehensive treatment planning curriculum (CTPC). The tooth prognosis of 2359 teeth from 94 cases was evaluated with 1 to 5 levels (1—Hopeless, 5—Good condition for long term) by two groups (Model-A with 16, and Model-B with 13 examiners) based on 17 clinical determining factors selected from the HSDM-CTPC. Three AI machine-learning methods including gradient boosting classifier, decision tree classifier, and random forest classifier were used to create an algorithm. These three methods were evaluated against the gold standard data determined by consensus of three experienced prosthodontists, and their accuracy was analyzed. The decision tree classifier indicated the highest accuracy at 0.8413 (Model-A) and 0.7523 (Model-B). Accuracy with the gradient boosting classifier and the random forest classifier was 0.6896, 0.6687, and 0.8413, 0.7523, respectively. Overall, the decision tree classifier had the best accuracy among the three methods. The study contributes to the implementation of AI in the decision-making process of tooth prognosis in consideration of the treatment plan.

Artificial Intelligence in the Diagnosis of Oral Diseases: Applications and Pitfalls

Background: Machine learning (ML) is a key component of artificial intelligence (AI). The terms machine learning, artificial intelligence, and deep learning are erroneously used interchangeably as they appear as monolithic nebulous entities. This technology offers immense possibilities and opportunities to advance diagnostics in the field of medicine and dentistry. This necessitates a deep understanding of AI and its essential components, such as machine learning (ML), artificial neural networks (ANN), and deep learning (DP). Aim: This review aims to enlighten clinicians regarding AI and its applications in the diagnosis of oral diseases, along with the prospects and challenges involved. Review results: AI has been used in the diagnosis of various oral diseases, such as dental caries, maxillary sinus diseases, periodontal diseases, salivary gland diseases, TMJ disorders, and oral cancer through clinical data and diagnostic images. Larger data sets would enable AI to predict the occurrence of precancerous conditions. They can aid in population-wide surveillance and decide on referrals to specialists. AI can efficiently detect microfeatures beyond the human eye and augment its predictive power in critical diagnosis. Conclusion: Although studies have recognized the benefit of AI, the use of artificial intelligence and machine learning has not been integrated into routine dentistry. AI is still in the research phase. The coming decade will see immense changes in diagnosis and healthcare built on the back of this research. Clinical significance: This paper reviews the various applications of AI in dentistry and illuminates the shortcomings faced while dealing with AI research and suggests ways to tackle them. Overcoming these pitfalls will aid in integrating AI seamlessly into dentistry.

Artificial Intelligence: A New Diagnostic Software in Dentistry: A Preliminary Performance Diagnostic Study

Background: Artificial intelligence (AI) has taken hold in public health because more and more people are looking to make a diagnosis using technology that allows them to work faster and more accurately, reducing costs and the number of medical errors. Methods: In the present study, 120 panoramic X-rays (OPGs) were randomly selected from the Department of Oral and Maxillofacial Sciences of Sapienza University of Rome, Italy. The OPGs were acquired and analyzed using Apox, which takes a panoramic X-rayand automatically returns the dental formula, the presence of dental implants, prosthetic crowns, fillings and root remnants. A descriptive analysis was performed presenting the categorical variables as absolute and relative frequencies. Results: In total, the number of true positive (TP) values was 2.195 (19.06%); true negative (TN), 8.908 (77.34%); false positive (FP), 132 (1.15%); and false negative (FN), 283 (2.46%). The overall sensitivity was 0.89, while the overall specificity was 0.98. Conclusions: The present study shows the latest achievements in dentistry, analyzing the application and credibility of a new diagnostic method to improve the work of dentists and the patients’ care.

Critical Analysis of Artificial Intelligence in Endodontics: A Scoping Review

INTRODUCTION

Artificial intelligence (AI) comprises computational models that mimic the human brain to perform various diagnostic tasks in clinical practice. The aim of this scoping review was to systematically analyze the AI algorithms and models used in endodontics and identify the source quality and type of evidence.

METHODS

A literature search was conducted in October 2020 to identify the relevant literature in English language in the 4 major health sciences databases, ie, MEDLINE, Dentistry & Oral Science, CINAHL Plus, and Cochrane Library. Our review questions were the following: what are the different AI algorithms and models used in endodontics?, what are the datasets being used?, what type of performance metrics were reported?, and what diagnostic performance measures were used?. The quality of the included studies was evaluated by a modified Quality Assessment of Studies of Diagnostic Accuracy risk (QUADAS) tool.

RESULTS

Out of 300 studies, 12 articles met our inclusion criteria and were subjected to final analysis. Among the included studies, 6 studies focused on periapical pathology, and 3 studies investigated vertical root fractures. Most studies (n = 10) used neural networks, among which convolutional neural networks were commonly used. The datasets that were mostly studied were radiographs. Out of 12 studies, only 3 studies achieved a high score according to the modified QUADAS tool.

CONCLUSIONS

AI models had acceptable performance, ie, accuracy >90% in executing various diagnostic tasks. The scientific reporting of AI-related research is irregular. The endodontic community needs to implement recommended guidelines to improve the weaknesses in the current planning and reporting of AI-related research to improve its scientific vigor.