Detection of vertical root fractures in intact and endodontically treated premolar teeth by designing a probabilistic neural network: an ex vivo study

Artificial Intelligence-Based Applications for Bone Fracture Detection Using Medical Images: A Systematic Review

Artificial intelligence (AI) is making notable advancements in the medical field, particularly in bone fracture detection. This systematic review compiles and assesses existing research on AI applications aimed at identifying bone fractures through medical imaging, encompassing studies from 2010 to 2023. It evaluates the performance of various AI models, such as convolutional neural networks (CNNs), in diagnosing bone fractures, highlighting their superior accuracy, sensitivity, and specificity compared to traditional diagnostic methods. Furthermore, the review explores the integration of advanced imaging techniques like 3D CT and MRI with AI algorithms, which has led to enhanced diagnostic accuracy and improved patient outcomes. The potential of Generative AI and Large Language Models (LLMs), such as OpenAI's GPT, to enhance diagnostic processes through synthetic data generation, comprehensive report creation, and clinical scenario simulation is also discussed. The review underscores the transformative impact of AI on diagnostic workflows and patient care, while also identifying research gaps and suggesting future research directions to enhance data quality, model robustness, and ethical considerations.

Artificial intelligence in endodontics: Data preparation, clinical applications, ethical considerations, limitations, and future directions

Artificial intelligence (AI) is emerging as a transformative technology in healthcare, including endodontics. A gap in knowledge exists in understanding AI's applications and limitations among endodontic experts. This comprehensive review aims to (A) elaborate on technical and ethical aspects of using data to implement AI models in endodontics; (B) elaborate on evaluation metrics; (C) review the current applications of AI in endodontics; and (D) review the limitations and barriers to real-world implementation of AI in the field of endodontics and its future potentials/directions. The article shows that AI techniques have been applied in endodontics for critical tasks such as detection of radiolucent lesions, analysis of root canal morphology, prediction of treatment outcome and post-operative pain and more. Deep learning models like convolutional neural networks demonstrate high accuracy in these applications. However, challenges remain regarding model interpretability, generalizability, and adoption into clinical practice. When thoughtfully implemented, AI has great potential to aid with diagnostics, treatment planning, clinical interventions, and education in the field of endodontics. However, concerted efforts are still needed to address limitations and to facilitate integration into clinical workflows.

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.

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

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

Artificial intelligence system for automatic maxillary sinus segmentation on cone beam computed tomography images

OBJECTIVES

The study aims to develop an artificial intelligence (AI) model based on nnU-Net v2 for automatic maxillary sinus (MS) segmentation in cone beam computed tomography (CBCT) volumes and to evaluate the performance of this model.

METHODS

In 101 CBCT scans, MS were annotated using the CranioCatch labelling software (Eskisehir, Turkey) The dataset was divided into 3 parts: 80 CBCT scans for training the model, 11 CBCT scans for model validation, and 10 CBCT scans for testing the model. The model training was conducted using the nnU-Net v2 deep learning model with a learning rate of 0.00001 for 1000 epochs. The performance of the model to automatically segment the MS on CBCT scans was assessed by several parameters, including F1-score, accuracy, sensitivity, precision, area under curve (AUC), Dice coefficient (DC), 95% Hausdorff distance (95% HD), and Intersection over Union (IoU) values.

RESULTS

F1-score, accuracy, sensitivity, precision values were found to be 0.96, 0.99, 0.96, 0.96, respectively for the successful segmentation of maxillary sinus in CBCT images. AUC, DC, 95% HD, IoU values were 0.97, 0.96, 1.19, 0.93, respectively.

CONCLUSIONS

Models based on nnU-Net v2 demonstrate the ability to segment the MS autonomously and accurately in CBCT images.

Second mesiobuccal canal segmentation with YOLOv5 architecture using cone beam computed tomography images

The objective of this study is to use a deep-learning model based on CNN architecture to detect the second mesiobuccal (MB2) canals, which are seen as a variation in maxillary molars root canals. In the current study, 922 axial sections from 153 patients' cone beam computed tomography (CBCT) images were used. The segmentation method was employed to identify the MB2 canals in maxillary molars that had not previously had endodontic treatment. Labeled images were divided into training (80%), validation (10%) and testing (10%) groups. The artificial intelligence (AI) model was trained using the You Only Look Once v5 (YOLOv5x) architecture with 500 epochs and a learning rate of 0.01. Confusion matrix and receiver-operating characteristic (ROC) analysis were used in the statistical evaluation of the results. The sensitivity of the MB2 canal segmentation model was 0.92, the precision was 0.83, and the F1 score value was 0.87. The area under the curve (AUC) in the ROC graph of the model was 0.84. The mAP value at 0.5 inter-over union (IoU) was found as 0.88. The deep-learning algorithm used showed a high success in the detection of the MB2 canal. The success of the endodontic treatment can be increased and clinicians' time can be preserved using the newly created artificial intelligence-based models to identify variations in root canal anatomy before the treatment.

Influence of bone density on the accuracy of artificial intelligence-guided implant surgery: An in vitro study

STATEMENT OF PROBLEM

Artificial intelligence (AI) has been found to be applicable in medical tests and diagnostics. However, studies on the application of AI technology in oral implantology are lacking. In addition, whether bone density affects the accuracy of guided implant surgery has not been determined.

PURPOSE

The purpose of this in vitro study was to determine the clinical reliability of an AI-assisted implant planning software program with an in vitro model. An additional goal was to determine the effect of bone density on the accuracy of static computer-assisted implant surgery (CAIS).

MATERIAL AND METHODS

Ten participants with missing mandibular left first molars were selected for analysis, and surgical fully guided templates were designed by using an AI implant planning software program. Jaw models were produced in 3 filling rate groups (group L: 25%; group M: 40%; group H: 55%, higher filling rate with representatives of the denser simulated bone density) by 3-dimensional (3D) printing. The preoperative and postoperative positions of the implants were compared by measuring the value of deviation through oral scanning. The mean 3D shoulder and apical and angular deviations were calculated for each group. The data were analyzed using 1-way ANOVA (α=.05 corrected for multiple testing by using Bonferroni-Holm adjustment).

RESULTS

The mean ±standard deviation 3D shoulder and apical and angular deviations were 0.80 ±0.32 mm, 1.43 ±0.47 mm, and 3.68 ±1.30 degrees. These values were lower than the clinical safety distance of the fully guided implant template. A significantly lower mean 3D apical deviation (1.12 ±0.33 mm, P=.023) and angular deviation (2.81 ±1.11 degrees, P=.018) were observed in group L than in group H (1.68 ±0.37 mm, 4.32 ±0.99 degrees). However, no significant differences were found among the 3 groups in 3D deviation at the shoulder (P>.05).

CONCLUSIONS

AI implant planning software program could design the ideal implant position through self-learning. The accuracy of the AI-assisted designed implant template in this study indicated its clinical reliability. Higher bone density led to increased implant deviations.

Applications of artificial intelligence in dentomaxillofacial imaging-a systematic review

BACKGROUND

Artificial intelligence (AI) technology has been increasingly developed in oral and maxillofacial imaging. The aim of this systematic review was to assess the applications and performance of the developed algorithms in different dentomaxillofacial imaging modalities.

STUDY DESIGN

A systematic search of PubMed and Scopus databases was performed. The search strategy was set as a combination of the following keywords: "Artificial Intelligence," "Machine Learning," "Deep Learning," "Neural Networks," "Head and Neck Imaging," and "Maxillofacial Imaging." Full-text screening and data extraction were independently conducted by two independent reviewers; any mismatch was resolved by discussion. The risk of bias was assessed by one reviewer and validated by another.

RESULTS

The search returned a total of 3,392 articles. After careful evaluation of the titles, abstracts, and full texts, a total number of 194 articles were included. Most studies focused on AI applications for tooth and implant classification and identification, 3-dimensional cephalometric landmark detection, lesion detection (periapical, jaws, and bone), and osteoporosis detection.

CONCLUSION

Despite the AI models' limitations, they showed promising results. Further studies are needed to explore specific applications and real-world scenarios before confidently integrating these models into dental practice.

Artificial neural network-based gene screening and immune cell infiltration analysis of osteosarcoma feature

BACKGROUND

The present study aimed to construct an artificial neural network (ANN) model that leverages characteristic genes associated with osteosarcoma (OS) to enable accurate prognostication for OS patients.

METHODS

Our research revealed 467 differentially expressed genes (DEGs) via gene expression contrast analysis, consisting of 345 downregulated genes and 122 upregulated genes. Gene Ontology (GO) enrichment analysis illuminated functions primarily encompassing T-cell activation, secretory granule lumen and antioxidant activity, among others. Through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, we discovered significant correlations between the DEGs and certain pathways, including phagosome, Staphylococcus aureus infection and human T-cell leukemia virus 1 infection. We then screened out 30 characteristic DEGs (CDEGs) based on random forest analysis and constructed the ANN model using the gene score matrix. To verify the credibility and accuracy of the ANN model, we performed internal and external validation processes, which affirmed our model's predictive capabilities.

RESULTS

The study further delved into the analysis of immune cell infiltration and its correlation with the target CDEGs, revealing disparities in the infiltration of 22 types of immune cells across different groups and their interrelationships. Moreover, we probed the expression of the two foremost CDEGs (YES1 and MFNG) in OS and normal tissues. We noted a positive relationship between the expression of YES1 and MFNG in OS tissues and the clinicopathological characteristics of OS patients.

CONCLUSIONS

Collectively, the findings of the present study validate the effectiveness of the CDEGs-based ANN model in predicting OS patients, which might facilitate early diagnosis and treatment of OS.

Artificial Intelligence in Endodontics: A Scoping Review

Artificial intelligence (AI) is transforming the diagnostic methods and treatment approaches in the constantly evolving field of endodontics. The current review discusses the recent advancements in AI; with a specific focus on convolutional and artificial neural networks. Apparently, AI models have proved to be highly beneficial in the analysis of root canal anatomy, detecting periapical lesions in early stages as well as providing accurate working-length determination. Moreover, they seem to be effective in predicting the treatment success next to identifying various conditions e.g., dental caries, pulpal inflammation, vertical root fractures, and expression of second opinions for non-surgical root canal treatments. Furthermore, AI has demonstrated an exceptional ability to recognize landmarks and lesions in cone-beam computed tomography scans with consistently high precision rates. While AI has significantly promoted the accuracy and efficiency of endodontic procedures, it is of high importance to continue validating the reliability and practicality of AI for possible widespread integration into daily clinical practice. Additionally, ethical considerations related to patient privacy, data security, and potential bias should be carefully examined to ensure the ethical and responsible implementation of AI in endodontics.

Application of Artificial Intelligence in Clinical Dentistry, a Comprehensive Review of Literature

Statement of the Problem

In recent years, the use of artificial intelligence (AI) has become increasingly popular in dentistry because it facilitates the process of diagnosis and clinical decision-making. However, AI holds multiple prominent drawbacks, which restrict its wide application today. It is necessary for dentists to be aware of AI's pros and cons before its implementation.

Purpose

Therefore, the present study was conducted to comprehensively review various applications of AI in all dental branches along with its advantages and disadvantages.

Materials and Method

For this review article, a complete query was carried out on PubMed and Google Scholar databases and the studies published during 2010-2022 were collected using the keywords "Artificial Intelligence", "Dentistry," "Machine learning," "Deep learning," and "Diagnostic System." Ultimately, 116 relevant articles focused on artificial intelligence in dentistry were selected and evaluated.

Results

In new research AI applications in detecting dental abnormalities and oral malignancies based on radiographic view and histopathological features, designing dental implants and crowns, determining tooth preparation finishing line, analyzing growth patterns, estimating biological age, predicting the viability of dental pulp stem cells, analyzing the gene expression of periapical lesions, forensic dentistry, and predicting the success rate of treatments, have been mentioned. Despite AI's benefits in clinical dentistry, three controversial challenges including ease of use, financial return on investment, and evidence of performance exist and need to be managed.

Conclusion

As evidenced by the obtained results, the most crucial progression of AI is in oral malignancies' diagnostic systems. However, AI's newest advancements in various branches of dentistry require further scientific work before being applied to clinical practice. Moreover, the immense use of AI in clinical dentistry is only achievable when its challenges are appropriately managed.

Artificial Intelligence and Its Application in Endodontics: A Review

AIM AND BACKGROUND

Artificial intelligence (AI) since it was introduced into dentistry, has become an important and valuable tool in many fields. It was applied in different specialties with different uses, for example, in diagnosis of oral cancer, periodontal disease and dental caries, and in the treatment planning and predicting the outcome of orthognathic surgeries. The aim of this comprehensive review is to report on the application and performance of AI models designed for application in the field of endodontics.

MATERIALS AND METHODS

PubMed, Web of Science, and Google Scholar were searched to collect the most relevant articles using terms, such as AI, endodontics, and dentistry. This review included 56 papers related to AI and its application in endodontics.

RESULT

The applications of AI were in detecting and diagnosing periapical lesions, assessing root fractures, working length determination, prediction for postoperative pain, studying root canal anatomy and decision-making in endodontics for retreatment. The accuracy of AI in performing these tasks can reach up to 90%.

CONCLUSION

Artificial intelligence has valuable applications in the field of modern endodontics with promising results. Larger and multicenter data sets can give external validity to the AI models.

CLINICAL SIGNIFICANCE

In the field of dentistry, AI models are specifically crafted to contribute to the diagnosis of oral diseases, ranging from common issues such as dental caries to more complex conditions like periodontal diseases and oral cancer. AI models can help in diagnosis, treatment planning, and in patient management in endodontics. Along with the modern tools like cone-beam computed tomography (CBCT), AI can be a valuable aid to the clinician. How to cite this article: Ahmed ZH, Almuharib AM, Abdulkarim AA, et al. Artificial Intelligence and Its Application in Endodontics: A Review. J Contemp Dent Pract 2023;24(11):912-917.

What is the current state of artificial intelligence applications in dentistry and orthodontics?

BACKGROUND

The use of Artificial Intelligence (AI) in the medical field has the potential to bring about significant improvements in patient care and outcomes. AI is being used in dentistry and more specifically in orthodontics through the development of diagnostic imaging tools, the development of treatment planning tools, and the development of robotic surgery. The aim of this study is to present the latest emerging AI softwares and applications in dental field to benefit from.

TYPES OF STUDIES REVIEWED

Search strategies were conducted in three electronic databases, with no date limits in the following databases up to April 30, 2023: MEDLINE, PUBMED, and GOOGLE® SCHOLAR for articles related to AI in dentistry & orthodontics. No inclusion and exclusion criteria were used for the selection of the articles. Most of the articles included (n = 79) are reviews of the literature, retro/prospective studies, systematic reviews and meta-analyses, and observational studies.

RESULTS

The use of AI in dentistry and orthodontics is a rapidly growing area of research and development, with the potential to revolutionize the field and bring about significant improvements in patient care and outcomes; this can save clinicians' chair-time and push for more individualized treatment plans. Results from the various studies reported in this review are suggestive that the accuracy of AI-based systems is quite promising and reliable.

PRACTICAL IMPLICATIONS

AI application in the healthcare field has proven to be efficient and helpful for the dentist to be more precise in diagnosis and clinical decision-making. These systems can simplify the tasks and provide results in quick time which can save dentists time and help them perform their duties more efficiently. These systems can be of greater aid and can be used as auxiliary support for dentists with lesser experience.

Developing an Artificial Intelligence Solution to Autosegment the Edentulous Mandibular Bone for Implant Planning

OBJECTIVE

 Dental implants are considered the optimum solution to replace missing teeth and restore the mouth's function and aesthetics. Surgical planning of the implant position is critical to avoid damage to vital anatomical structures; however, the manual measurement of the edentulous (toothless) bone on cone beam computed tomography (CBCT) images is time-consuming and is subject to human error. An automated process has the potential to reduce human errors and save time and costs. This study developed an artificial intelligence (AI) solution to identify and delineate edentulous alveolar bone on CBCT images before implant placement.

MATERIALS AND METHODS

 After obtaining the ethical approval, CBCT images were extracted from the database of the University Dental Hospital Sharjah based on predefined selection criteria. Manual segmentation of the edentulous span was done by three operators using ITK-SNAP software. A supervised machine learning approach was undertaken to develop a segmentation model on a "U-Net" convolutional neural network (CNN) in the Medical Open Network for Artificial Intelligence (MONAI) framework. Out of the 43 labeled cases, 33 were utilized to train the model, and 10 were used for testing the model's performance.

STATISTICAL ANALYSIS

 The degree of 3D spatial overlap between the segmentation made by human investigators and the model's segmentation was measured by the dice similarity coefficient (DSC).

RESULTS

 The sample consisted mainly of lower molars and premolars. DSC yielded an average value of 0.89 for training and 0.78 for testing. Unilateral edentulous areas, comprising 75% of the sample, resulted in a better DSC (0.91) than bilateral cases (0.73).

CONCLUSION

 Segmentation of the edentulous spans on CBCT images was successfully conducted by machine learning with good accuracy compared to manual segmentation. Unlike traditional AI object detection models that identify objects present in the image, this model identifies missing objects. Finally, challenges in data collection and labeling are discussed, together with an outlook at the prospective stages of a larger project for a complete AI solution for automated implant planning.

Machine learning techniques for periodontitis and dental caries detection: A narrative review

OBJECTIVES

In recent years, periodontitis, and dental caries have become common in humans and need to be diagnosed in the early stage to prevent severe complications and tooth loss. These dental issues are diagnosed by visual inspection, measuring pocket probing depth, and radiographs findings from experienced dentists. Though a glut of machine learning (ML) algorithms has been proposed for the automated detection of periodontitis, and dental caries, determining which ML techniques are suitable for clinical practice remains under debate. This review aims to identify the research challenges by analyzing the limitations of current methods and how to address these to obtain robust systems suitable for clinical use or point-of-care testing.

METHODS

An extensive search of the literature published from 2015 to 2022 written in English, related to the subject of study was sought by searching the electronic databases: PubMed, Institute of Electrical and Electronics Engineers (IEEE) Xplore, and ScienceDirect.

RESULTS

The initial electronic search yielded 1743 titles, and 55 studies were eventually included based on the selection criteria adopted in this review. Studies selected were on ML applications for the automatic detection of periodontitis and dental caries and related dental issues: Apical lessons, Periodontal bone loss, and Vertical root fracture.

CONCLUSION

While most of the ML-based studies use radiograph images for the detection of periodontitis and dental caries, few pieces of the literature revealed that good diagnostic accuracy could be achieved by training the ML model even with mobile photos representing the images of dental issues. Nowadays smartphones are used in every sector for different applications. Training the ML model with as many images of dental issues captured by the smartphone can achieve good accuracy, reduce the cost of clinical diagnosis, and provide user interaction.

Retrospective evaluation of the clinical utility of reconstructed computed tomography images using artificial intelligence in the oral and maxillofacial region

The aim of this study was to convert medical images stored in 3 mm slices in the picture archiving and communication system (PACS) to 1 mm slices, using artificial intelligence (AI), and to analyze the accuracy of the AI. The original 1.0 mm CT slices of the facial bone were obtained from 30 patients and reformatted to a rough CT slice of 3.0 mm. CT slices of 1.0 mm were subsequently reconstructed from those of 3.0 mm using AI. The AI and rough CT images were superimposed on the original CT images. Fourteen hard-tissue and five soft-tissue landmarks were selected for measuring the discrepancy. The overall average differences in values for the hard-tissue landmarks were 1.31 ± 0.38 mm and 0.81 ± 0.17 mm for the rough and AI CT images, respectively. The values for the soft-tissue landmarks were 1.18 ± 0.35 mm and 0.54 ± 0.17 mm for the rough and AI CT images, respectively. The differences for all the landmarks, excluding point A and pogonion, were statistically significant. Within the limitations of the study it seems that CT images reconstructed using AI might provide more accurate clinical information with a discrepancy of less than 1.0 mm.

Second Opinion for Non-Surgical Root Canal Treatment Prognosis Using Machine Learning Models

Although the association between risk factors and non-surgical root canal treatment (NSRCT) failure has been extensively studied, methods to predict the outcomes of NSRCT are in an early stage, and dentists currently make the treatment prognosis based mainly on their clinical experience. Since this involves different sources of error, we investigated the use of machine learning (ML) models as a second opinion to support the clinical decision on whether to perform NSRCT. We undertook a retrospective study of 119 confirmed and not previously treated Apical Periodontitis cases that received the same treatment by the same specialist. For each patient, we recorded the variables from a newly proposed data collection template and defined a binary outcome: Success if the lesion clears and failure otherwise. We conducted tests for detecting the association between the variables and the outcome and selected a set of variables as the initial inputs into four ML algorithms: Logistic Regression (LR), Random Forest (RF), Naive-Bayes (NB), and K Nearest Neighbors (KNN). According to our results, RF and KNN significantly improve (p-values < 0.05) the sensitivity and accuracy of the dentist's treatment prognosis. Taking our results as a proof of concept, we conclude that future randomized clinical trials are worth designing to test the clinical utility of ML models as a second opinion for NSRCT prognosis.

Artificial intelligence in diagnosing dens evaginatus on periapical radiography with limited data availability

This study aimed to develop an artificial intelligence (AI) model using deep learning techniques to diagnose dens evaginatus (DE) on periapical radiography (PA) and compare its performance with endodontist evaluations. In total, 402 PA images (138 DE and 264 normal cases) were used. A pre-trained ResNet model, which had the highest AUC of 0.878, was selected due to the small number of data. The PA images were handled in both the full (F model) and cropped (C model) models. There were no significant statistical differences between the C and F model in AI, while there were in endodontists (p = 0.753 and 0.04 in AUC, respectively). The AI model exhibited superior AUC in both the F and C models compared to endodontists. Cohen's kappa demonstrated a substantial level of agreement for the AI model (0.774 in the F model and 0.684 in C) and fair agreement for specialists. The AI's judgment was also based on the coronal pulp area on full PA, as shown by the class activation map. Therefore, these findings suggest that the AI model can improve diagnostic accuracy and support clinicians in diagnosing DE on PA, improving the long-term prognosis of the tooth.

A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health

Artificial intelligence has made substantial progress in medicine. Automated dental imaging interpretation is one of the most prolific areas of research using AI. X-ray and infrared imaging systems have enabled dental clinicians to identify dental diseases since the 1950s. However, the manual process of dental disease assessment is tedious and error-prone when diagnosed by inexperienced dentists. Thus, researchers have employed different advanced computer vision techniques, and machine- and deep-learning models for dental disease diagnoses using X-ray and near-infrared imagery. Despite the notable development of AI in dentistry, certain factors affect the performance of the proposed approaches, including limited data availability, imbalanced classes, and lack of transparency and interpretability. Hence, it is of utmost importance for the research community to formulate suitable approaches, considering the existing challenges and leveraging findings from the existing studies. Based on an extensive literature review, this survey provides a brief overview of X-ray and near-infrared imaging systems. Additionally, a comprehensive insight into challenges faced by researchers in the dental domain has been brought forth in this survey. The article further offers an amalgamative assessment of both performances and methods evaluated on public benchmarks and concludes with ethical considerations and future research avenues.

Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review

Artificial intelligence (AI) plays a more and more important role in our everyday life due to the advantages that it brings when used, such as 24/7 availability, a very low percentage of errors, ability to provide real time insights, or performing a fast analysis. AI is increasingly being used in clinical medical and dental healthcare analyses, with valuable applications, which include disease diagnosis, risk assessment, treatment planning, and drug discovery. This paper presents a narrative literature review of AI use in healthcare from a multi-disciplinary perspective, specifically in the cardiology, allergology, endocrinology, and dental fields. The paper highlights data from recent research and development efforts in AI for healthcare, as well as challenges and limitations associated with AI implementation, such as data privacy and security considerations, along with ethical and legal concerns. The regulation of responsible design, development, and use of AI in healthcare is still in early stages due to the rapid evolution of the field. However, it is our duty to carefully consider the ethical implications of implementing AI and to respond appropriately. With the potential to reshape healthcare delivery and enhance patient outcomes, AI systems continue to reveal their capabilities.

Using artificial intelligence models to evaluate envisaged points initially: A pilot study

The morphology of the finger bones in hand-wrist radiographs (HWRs) can be considered as a radiological skeletal maturity indicator, along with the other indicators. This study aims to validate the anatomical landmarks envisaged to be used for classification of the morphology of the phalanges, by developing classical neural network (NN) classifiers based on a sub-dataset of 136 HWRs. A web-based tool was developed and 22 anatomical landmarks were labeled on four region of interests (proximal (PP3), medial (MP3), distal (DP3) phalanges of the third and medial phalanx (MP5) of the fifth finger) and the epiphysis-diaphysis relationships were saved as "narrow,""equal,""capping" or "fusion" by three observers. In each region, 18 ratios and 15 angles were extracted using anatomical points. The data set is analyzed by developing two NN classifiers, without (NN-1) and with (NN-2) the 5-fold cross-validation. The performance of the models was evaluated with percentage of agreement, Cohen's (cκ) and Weighted (wκ) Kappa coefficients, precision, recall, F1-score and accuracy (statistically significance: p < 0.05). Method error was found to be in the range of cκ: 0.7-1. Overall classification performance of the models was changed between 82.14% and 89.29%. On average, performance of the NN-1 and NN-2 models were found to be 85.71% and 85.52%, respectively. The cκ and wκ of the NN-1 model were changed between -0.08 (p > 0.05) and 0.91 among regions. The average performance was found to be promising except the regions without adequate samples and the anatomical points are validated to be used in the future studies, initially.

The use of artificial intelligence and game-based learning in removable partial denture design: A comparative study

PURPOSE

The purpose of this study was to compare student performance in removable partial denture (RPD) design during a pre-clinical RPD course with and without using a recently developed computer software named AiDental. Additionally, student perceptions associated with the use of this software were assessed.

METHODS

The AiDental software consists of a learning environment containing an RPD design system that automatically designs RPDs based on the user's input. The software also contains an RPD game component that compares the user's RPD Design to an automatically generated RPD ideal design. The study was conducted in two phases. In phase one, pre-clinical second-year dental students who participated in the study were randomly divided into two groups: The AiDental group with AiDental software access (n = 36), and the conventional group without software access (n = 37). Both groups received conventional RPD instruction and practice, however, the AiDental group had additional access to the AiDental software. After 2 weeks, both groups took a mock practical test, which was collected and graded by the principal investigator (PI). The PI was blinded from group assignment and no identifying information was used in the mock practical. In phase two, all students were granted access to the AiDental software for the remainder of the pre-clinical course duration. At the conclusion of the course, all students were given a survey to evaluate their perceptions of the AiDental software. Descriptive statistics were calculated and analyzed. Variables related to perceptions of both the AiDental designer and game were assessed using Spearman's rank correlation test, the chi-square test, Fisher's exact test, and the non-parametric Wilcoxon rank-sum test as appropriate. In addition, a thematic analysis of the responses to the optional comments section was conducted using the Braun and Clarke method.

RESULTS

Phase one results showed that subjects in the AiDental group were more likely than subjects in the conventional group to receive a final grade of A or B. Phase two results showed generally favorable student perceptions towards the software, and additionally, the results showed that age was significantly negatively correlated with ease of use of the software, improving decision-making, and critical thinking relative to RPD design choices. However, no correlation between age and using the software as a reference were noted.

CONCLUSIONS

The use of AiDental's automated feedback and gamification techniques in RPD education had a positive effect on student grades and it was well-liked by students. Thus, the results suggest that AiDental has the potential to be a useful adjunct to pre-clinical teaching.

Artificial intelligence models for tooth-supported fixed and removable prosthodontics: A systematic review

STATEMENT OF PROBLEM

Artificial intelligence applications are increasing in prosthodontics. Still, the current development and performance of artificial intelligence in prosthodontic applications has not yet been systematically documented and analyzed.

PURPOSE

The purpose of this systematic review was to assess the performance of the artificial intelligence models in prosthodontics for tooth shade selection, automation of restoration design, mapping the tooth preparation finishing line, optimizing the manufacturing casting, predicting facial changes in patients with removable prostheses, and designing removable partial dentures.

MATERIAL AND METHODS

An electronic systematic review was performed in MEDLINE/PubMed, EMBASE, Web of Science, Cochrane, and Scopus. A manual search was also conducted. Studies with artificial intelligence models were selected based on 6 criteria: tooth shade selection, automated fabrication of dental restorations, mapping the finishing line of tooth preparations, optimizing the manufacturing casting process, predicting facial changes in patients with removable prostheses, and designing removable partial dentures. Two investigators independently evaluated the quality assessment of the studies by applying the Joanna Briggs Institute Critical Appraisal Checklist for Quasi-Experimental Studies (nonrandomized experimental studies). A third investigator was consulted to resolve lack of consensus.

RESULTS

A total of 36 articles were reviewed and classified into 6 groups based on the application of the artificial intelligence model. One article reported on the development of an artificial intelligence model for tooth shade selection, reporting better shade matching than with conventional visual selection; 14 articles reported on the feasibility of automated design of dental restorations using different artificial intelligence models; 1 artificial intelligence model was able to mark the margin line without manual interaction with an average accuracy ranging from 90.6% to 97.4%; 2 investigations developed artificial intelligence algorithms for optimizing the manufacturing casting process, reporting an improvement of the design process, minimizing the porosity on the cast metal, and reducing the overall manufacturing time; 1 study proposed an artificial intelligence model that was able to predict facial changes in patients using removable prostheses; and 17 investigations that developed clinical decision support, expert systems for designing removable partial dentures for clinicians and educational purposes, computer-aided learning with video interactive programs for student learning, and automated removable partial denture design.

CONCLUSIONS

Artificial intelligence models have shown the potential for providing a reliable diagnostic tool for tooth shade selection, automated restoration design, mapping the preparation finishing line, optimizing the manufacturing casting, predicting facial changes in patients with removable prostheses, and designing removable partial dentures, but they are still in development. Additional studies are needed to further develop and assess their clinical performance.

Detection of vertical root fractures by cone-beam computed tomography based on deep learning

OBJECTIVES

This study aims to evaluate the performance of ResNet models in the detection of in vitro and in vivo vertical root fractures (VRF) in Cone-beam Computed Tomography (CBCT) images.

METHODS

A CBCT image dataset consisting of 28 teeth (14 intact and 14 teeth with VRF, 1641 slices) from 14 patients, and another dataset containing 60 teeth (30 intact and 30 teeth with VRF, 3665 slices) from an in vitro model were used for the establishment of VRFconvolutional neural network (CNN) models. The most popular CNN architecture ResNet with different layers was fine-tuned for the detection of VRF. Sensitivity, specificity, accuracy, PPV (positive predictive value), NPV (negative predictive value), and AUC (the area under the receiver operating characteristic curve) of the VRF slices classified by the CNN in the test set were compared. Two oral and maxillofacial radiologists independently reviewed all the CBCT images of the test set, and intraclass correlation coefficients (ICCs) were calculated to assess the interobserver agreement for the oral maxillofacial radiologists.

RESULTS

The AUC of the models on the patient data were: 0.827(ResNet-18), 0.929(ResNet-50), and 0.882(ResNet-101). The AUC of the models on the mixed data get improved as:0.927(ResNet-18), 0.936(ResNet-50), and 0.893(ResNet-101). The maximum AUC were: 0.929 (0.908-0.950, 95% CI) and 0.936 (0.924-0.948, 95% CI) for the patient data and mixed data from ResNet-50, which is comparable to the AUC (0.937 and 0.950) for patient data and (0.915 and 0.935) for the mixed data obtained from the two oral and maxillofacial radiologists, respectively.

CONCLUSIONS

Deep-learning models showed high accuracy in the detection of VRF using CBCT images. The data obtained from the in vitro VRF model increases the data scale, which is beneficial to the training of deep-learning models.

Evaluation of the Diagnostic and Prognostic Accuracy of Artificial Intelligence in Endodontic Dentistry: A Comprehensive Review of Literature

Aim

This comprehensive review is aimed at evaluating the diagnostic and prognostic accuracy of artificial intelligence in endodontic dentistry.

Introduction

Artificial intelligence (AI) is a relatively new technology that has widespread use in dentistry. The AI technologies have primarily been used in dentistry to diagnose dental diseases, plan treatment, make clinical decisions, and predict the prognosis. AI models like convolutional neural networks (CNN) and artificial neural networks (ANN) have been used in endodontics to study root canal system anatomy, determine working length measurements, detect periapical lesions and root fractures, predict the success of retreatment procedures, and predict the viability of dental pulp stem cells. Methodology. The literature was searched in electronic databases such as Google Scholar, Medline, PubMed, Embase, Web of Science, and Scopus, published over the last four decades (January 1980 to September 15, 2021) by using keywords such as artificial intelligence, machine learning, deep learning, application, endodontics, and dentistry.

Results

The preliminary search yielded 2560 articles relevant enough to the paper's purpose. A total of 88 articles met the eligibility criteria. The majority of research on AI application in endodontics has concentrated on tracing apical foramen, verifying the working length, projection of periapical pathologies, root morphologies, and retreatment predictions and discovering the vertical root fractures.

Conclusion

In endodontics, AI displayed accuracy in terms of diagnostic and prognostic evaluations. The use of AI can help enhance the treatment plan, which in turn can lead to an increase in the success rate of endodontic treatment outcomes. The AI is used extensively in endodontics and could help in clinical applications, such as detecting root fractures, periapical pathologies, determining working length, tracing apical foramen, the morphology of root, and disease prediction.

Machine Learning in Dentistry: A Scoping Review

Machine learning (ML) is being increasingly employed in dental research and application. We aimed to systematically compile studies using ML in dentistry and assess their methodological quality, including the risk of bias and reporting standards. We evaluated studies employing ML in dentistry published from 1 January 2015 to 31 May 2021 on MEDLINE, IEEE Xplore, and arXiv. We assessed publication trends and the distribution of ML tasks (classification, object detection, semantic segmentation, instance segmentation, and generation) in different clinical fields. We appraised the risk of bias and adherence to reporting standards, using the QUADAS-2 and TRIPOD checklists, respectively. Out of 183 identified studies, 168 were included, focusing on various ML tasks and employing a broad range of ML models, input data, data sources, strategies to generate reference tests, and performance metrics. Classification tasks were most common. Forty-two different metrics were used to evaluate model performances, with accuracy, sensitivity, precision, and intersection-over-union being the most common. We observed considerable risk of bias and moderate adherence to reporting standards which hampers replication of results. A minimum (core) set of outcome and outcome metrics is necessary to facilitate comparisons across studies.

Developments and Performance of Artificial Intelligence Models Designed for Application in Endodontics: A Systematic Review

Technological advancements in health sciences have led to enormous developments in artificial intelligence (AI) models designed for application in health sectors. This article aimed at reporting on the application and performances of AI models that have been designed for application in endodontics. Renowned online databases, primarily PubMed, Scopus, Web of Science, Embase, and Cochrane and secondarily Google Scholar and the Saudi Digital Library, were accessed for articles relevant to the research question that were published from 1 January 2000 to 30 November 2022. In the last 5 years, there has been a significant increase in the number of articles reporting on AI models applied for endodontics. AI models have been developed for determining working length, vertical root fractures, root canal failures, root morphology, and thrust force and torque in canal preparation; detecting pulpal diseases; detecting and diagnosing periapical lesions; predicting postoperative pain, curative effect after treatment, and case difficulty; and segmenting pulp cavities. Most of the included studies (n = 21) were developed using convolutional neural networks. Among the included studies. datasets that were used were mostly cone-beam computed tomography images, followed by periapical radiographs and panoramic radiographs. Thirty-seven original research articles that fulfilled the eligibility criteria were critically assessed in accordance with QUADAS-2 guidelines, which revealed a low risk of bias in the patient selection domain in most of the studies (risk of bias: 90%; applicability: 70%). The certainty of the evidence was assessed using the GRADE approach. These models can be used as supplementary tools in clinical practice in order to expedite the clinical decision-making process and enhance the treatment modality and clinical operation.

Personalized dental medicine, artificial intelligence, and their relevance for dentomaxillofacial imaging

Personalized medicine refers to the tailoring of diagnostics and therapeutics to individuals based on one's biological, social, and behavioral characteristics. While personalized dental medicine is still far from being a reality, advanced artificial intelligence (AI) technologies with improved data analytic approaches are expected to integrate diverse data from the individual, setting, and system levels, which may facilitate a deeper understanding of the interaction of these multilevel data and therefore bring us closer to more personalized, predictive, preventive, and participatory dentistry, also known as P4 dentistry. In the field of dentomaxillofacial imaging, a wide range of AI applications, including several commercially available software options, have been proposed to assist dentists in the diagnosis and treatment planning of various dentomaxillofacial diseases, with performance similar or even superior to that of specialists. Notably, the impact of these dental AI applications on treatment decision, clinical and patient-reported outcomes, and cost-effectiveness has so far been assessed sparsely. Such information should be further investigated in future studies to provide patients, providers, and healthcare organizers a clearer picture of the true usefulness of AI in daily dental practice.

Artificial intelligence models for clinical usage in dentistry with a focus on dentomaxillofacial CBCT: a systematic review

This study aimed at performing a systematic review of the literature on the application of artificial intelligence (AI) in dental and maxillofacial cone beam computed tomography (CBCT) and providing comprehensive descriptions of current technical innovations to assist future researchers and dental professionals. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA) Statement was followed. The study's protocol was prospectively registered. Following databases were searched, based on MeSH and Emtree terms: PubMed/MEDLINE, Embase and Web of Science. The search strategy enrolled 1473 articles. 59 publications were included, which assessed the use of AI on CBCT images in dentistry. According to the PROBAST guidelines for study design, seven papers reported only external validation and 11 reported both model building and validation on an external dataset. 40 studies focused exclusively on model development. The AI models employed mainly used deep learning models (42 studies), while other 17 papers used conventional approaches, such as statistical-shape and active shape models, and traditional machine learning methods, such as thresholding-based methods, support vector machines, k-nearest neighbors, decision trees, and random forests. Supervised or semi-supervised learning was utilized in the majority (96.62%) of studies, and unsupervised learning was used in two (3.38%). 52 publications included studies had a high risk of bias (ROB), two papers had a low ROB, and four papers had an unclear rating. Applications based on AI have the potential to improve oral healthcare quality, promote personalized, predictive, preventative, and participatory dentistry, and expedite dental procedures.

Artificial Intelligence (AI) for Detection and Localization of Unobturated Second Mesial Buccal (MB2) Canals in Cone-Beam Computed Tomography (CBCT)

The aim of this study was to develop a deep learning model to automatically detect and segment unobturated mesial buccal 2 (MB2) canals on endodontically obturated maxillary molars depicted in CBCT studies. Fifty-seven deidentified CBCT studies of maxillary molars with clinically confirmed unobturated MB2 canals were retrieved from a dental institution radiology database. One-hundred and two maxillary molar roots with and without unobturated MB2 canals were segmented using ITK-SNAP. The data were split into training and testing samples designated to train and evaluate the performance, respectively, of a convolutional neural network (CNN), U-Net. The detection performance revealed a sensitivity of 0.8, a specificity of 1, a high PPV of 1, and a NPV of 0.83 for the testing set, along with an accuracy of 0.9. The segmentation performance of unobturated MB2 canals, assessed using the custom metric, rendered a mean value of 0.3018 for the testing set. The current AI algorithm has the potential to identify obturated and unobturated canals in endodontically treated teeth. However, the AI algorithm is still somewhat affected by metallic artifacts, variations in canal calcifications, and the applied configuration. Thus, further development is needed to improve the algorithm and validate the accuracy using external validation data sets.

The Use of Artificial Intelligence in Dentistry Practices

Artificial intelligence can be defined as "understanding human thinking and trying to develop computer processes that will produce a similar structure." Thus, it is an attempt by a programmed computer to think. According to a broader definition, artificial intelligence is a computer equipped with human intelligencespecific capacities such as acquiring information, perceiving, seeing, thinking, and making decisions. Quality demands in dental treatments have constantly been increasing in recent years. In parallel with this, using image-based methods and multimedia-supported explanation systems on the computer is becoming widespread to evaluate the available information. The use of artificial intelligence in dentistry will greatly contribute to the reduction of treatment times and the effort spent by the dentist, reduce the need for a specialist dentist, and give a new perspective to how dentistry is practiced. In this review, we aim to review the studies conducted with artificial intelligence in dentistry and to inform our dentists about the existence of this new technology.

Artificial intelligence applications in restorative dentistry: A systematic review

STATEMENT OF PROBLEM

Artificial intelligence (AI) applications are increasing in restorative procedures. However, the current development and performance of AI in restorative dentistry applications has not yet been systematically documented and analyzed.

PURPOSE

The purpose of this systematic review was to identify and evaluate the ability of AI models in restorative dentistry to diagnose dental caries and vertical tooth fracture, detect tooth preparation margins, and predict restoration failure.

MATERIAL AND METHODS

An electronic systematic review was performed in 5 databases: MEDLINE/PubMed, EMBASE, World of Science, Cochrane, and Scopus. A manual search was also conducted. Studies with AI models were selected based on 4 criteria: diagnosis of dental caries, diagnosis of vertical tooth fracture, detection of the tooth preparation finishing line, and prediction of restoration failure. Two investigators independently evaluated the quality assessment of the studies by applying the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Quasi-Experimental Studies (nonrandomized experimental studies). A third investigator was consulted to resolve lack of consensus.

RESULTS

A total of 34 articles were included in the review: 29 studies included AI techniques for the diagnosis of dental caries or the elaboration of caries and postsensitivity prediction models, 2 for the diagnosis of vertical tooth fracture, 1 for the tooth preparation finishing line location, and 2 for the prediction of the restoration failure. Among the studies reviewed, the AI models tested obtained a caries diagnosis accuracy ranging from 76% to 88.3%, sensitivity ranging from 73% to 90%, and specificity ranging from 61.5% to 93%. The caries prediction accuracy among the studies ranged from 83.6% to 97.1%. The studies reported an accuracy for the vertical tooth fracture diagnosis ranging from 88.3% to 95.7%. The article using AI models to locate the finishing line reported an accuracy ranging from 90.6% to 97.4%.

CONCLUSIONS

AI models have the potential to provide a powerful tool for assisting in the diagnosis of caries and vertical tooth fracture, detecting the tooth preparation margin, and predicting restoration failure. However, the dental applications of AI models are still in development. Further studies are required to assess the clinical performance of AI models in restorative dentistry.

Bone Fracture Detection Using Deep Supervised Learning from Radiological Images: A Paradigm Shift

Bone diseases are common and can result in various musculoskeletal conditions (MC). An estimated 1.71 billion patients suffer from musculoskeletal problems worldwide. Apart from musculoskeletal fractures, femoral neck injuries, knee osteoarthritis, and fractures are very common bone diseases, and the rate is expected to double in the next 30 years. Therefore, proper and timely diagnosis and treatment of a fractured patient are crucial. Contrastingly, missed fractures are a common prognosis failure in accidents and emergencies. This causes complications and delays in patients’ treatment and care. These days, artificial intelligence (AI) and, more specifically, deep learning (DL) are receiving significant attention to assist radiologists in bone fracture detection. DL can be widely used in medical image analysis. Some studies in traumatology and orthopaedics have shown the use and potential of DL in diagnosing fractures and diseases from radiographs. In this systematic review, we provide an overview of the use of DL in bone imaging to help radiologists to detect various abnormalities, particularly fractures. We have also discussed the challenges and problems faced in the DL-based method, and the future of DL in bone imaging.

Use and performance of artificial intelligence applications in the diagnosis of chronic apical periodontitis based on cone beam computed tomography imaging

OBJECTIVES

This study aims to investigate the diagnostic application of an artificial intelligence (AI) computer-aided diagnostic system based on a convolutional neural network algorithm in detecting chronic apical periodontitis in cone beam computed tomography (CBCT) images.

METHODS

CBCT raw data of 55 single root chronic apical pe-riodontitis taken in 2nd Dental Center of Peking University School and Hospital from 49 patients from January 2017 to December 2021 were collected, and the chronic apical periodontitis areas were identified by experienced clinicians ma-nually and segmented layer by layer in Materialise Mimics Medical Software. Deep learning of lesion characterization was conducted via AI 3D U-Net, and the network segmentation results were compared manually with the test sets in terms of intersection over union (IOU), Dice coefficient, and pixel accuracy (PA).

RESULTS

In our deep learning algorithm, the IOU for all actual true lesions in test set samples was 92.18%, and the Dice coefficient and the PA index were 95.93% and 99.27%, respectively. Lesion segmentation and volume measurements performed by humans and AI systems showed excellent agreement.

CONCLUSIONS

AI systems based on deep learning methods can be applied for detecting chronic apical periodontitis on CBCT images in clinical applications.

Deep Learning Models for Classification of Dental Diseases Using Orthopantomography X-ray OPG Images

The teeth are the most challenging material to work with in the human body. Existing methods for detecting teeth problems are characterised by low efficiency, the complexity of the experiential operation, and a higher level of user intervention. Older oral disease detection approaches were manual, time-consuming, and required a dentist to examine and evaluate the disease. To address these concerns, we propose a novel approach for detecting and classifying the four most common teeth problems: cavities, root canals, dental crowns, and broken-down root canals, based on the deep learning model. In this study, we apply the YOLOv3 deep learning model to develop an automated tool capable of diagnosing and classifying dental abnormalities, such as dental panoramic X-ray images (OPG). Due to the lack of dental disease datasets, we created the Dental X-rays dataset to detect and classify these diseases. The size of datasets used after augmentation was 1200 images. The dataset comprises dental panoramic images with dental disorders such as cavities, root canals, BDR, dental crowns, and so on. The dataset was divided into 70% training and 30% testing images. The trained model YOLOv3 was evaluated on test images after training. The experiments demonstrated that the proposed model achieved 99.33% accuracy and performed better than the existing state-of-the-art models in terms of accuracy and universality if we used our datasets on other models.

Diagnosis of in vivo vertical root fracture using deep learning on cone-beam CT images

Objectives

Evaluating the diagnostic efficiency of deep learning models to diagnose vertical root fracture in vivo on cone-beam CT (CBCT) images.

Materials and methods

The CBCT images of 276 teeth (138 VRF teeth and 138 non-VRF teeth) were enrolled and analyzed retrospectively. The diagnostic results of these teeth were confirmed by two chief radiologists. There were two experimental groups: auto-selection group and manual selection group. A total of 552 regions of interest of teeth were cropped in manual selection group and 1118 regions of interest of teeth were cropped in auto-selection group. Three deep learning networks (ResNet50, VGG19 and DenseNet169) were used for diagnosis (3:1 for training and testing). The diagnostic efficiencies (accuracy, sensitivity, specificity, and area under the curve (AUC)) of three networks were calculated in two experiment groups. Meanwhile, 552 teeth images in manual selection group were diagnosed by a radiologist. The diagnostic efficiencies of the three deep learning network models in two experiment groups and the radiologist were calculated.

Results

In manual selection group, ResNet50 presented highest accuracy and sensitivity for diagnosing VRF teeth. The accuracy, sensitivity, specificity and AUC was 97.8%, 97.0%, 98.5%, and 0.99, the radiologist presented accuracy, sensitivity, and specificity as 95.3%, 96.4 and 94.2%. In auto-selection group, ResNet50 presented highest accuracy and sensitivity for diagnosing VRF teeth, the accuracy, sensitivity, specificity and AUC was 91.4%, 92.1%, 90.7% and 0.96.

Conclusion

In manual selection group, ResNet50 presented higher diagnostic efficiency in diagnosis of in vivo VRF teeth than VGG19, DensenNet169 and radiologist with 2 years of experience. In auto-selection group, Resnet50 also presented higher diagnostic efficiency in diagnosis of in vivo VRF teeth than VGG19 and DensenNet169. This makes it a promising auxiliary diagnostic technique to screen for VRF teeth.

Withdrawal

Raveendran, R, Perumbure, S, Nath, SG. Artificial intelligence: A newer vista in dentistry. Artif. Organs. 2021; 00:1-11. https://doi.org/10.1111/aor.14128. The above article, published online on December 6, 2021 in Wiley Online Library (wileyonlinelibrary.com), has been withdrawn by agreement between the journal Editor in Chief, Vakhtang Tchantchaleishvili, and John Wiley and Sons, Inc. The withdrawal has been agreed due to an editorial office error that led to the publication of the article without peer review. The authors were unaware of this error until notified by the editorial team and did not engage in any inappropriate or suspicious publishing conduct.

Artificial Intelligence in Dentistry: Past, Present, and Future

Artificial intelligence (AI) has remarkably increased its presence and significance in a wide range of sectors, including dentistry. It can mimic the intelligence of humans to undertake complex predictions and decision-making in the healthcare sector, particularly in endodontics. The models of AI, such as convolutional neural networks and/or artificial neural networks, have shown a variety of applications in endodontics, including studying the anatomy of the root canal system, forecasting the viability of stem cells of the dental pulp, measuring working lengths, pinpointing root fractures and periapical lesions and forecasting the success of retreatment procedures. Future applications of this technology were considered in relation to scheduling, patient care, drug-drug interactions, prognostic diagnosis, and robotic endodontic surgery. In endodontics, in terms of disease detection, evaluation, and prediction, AI has demonstrated accuracy and precision. AI can aid in the advancement of endodontic diagnosis and therapy, which can enhance endodontic treatment results. However, before incorporating AI models into routine clinical operations, it is still important to further certify the cost-effectiveness, dependability, and applicability of these models.

Adopting artificial intelligence in dental education: A model for academic leadership and innovation

INTRODUCTION

The continual evolution of dental education, dental practice and the delivery of optimal oral health care is rooted in the practice of leadership. This paper explores opportunities and challenges facing dental education with a specific focus on incorporating the use of artificial intelligence (AI).

METHODS

Using the model in Bolman and Deal's Reframing Organizations, the Four Frames model serves as a road map for building infrastructure within dental schools for the adoption of AI.

CONCLUSION

AI can complement and boost human tasks and have a far-reaching impact in academia and health care. Its adoption could enhance educational experiences and the delivery of care, and support current functions and future innovation. The framework suggested in this paper, while specific to AI, could be adapted and applied to a myriad of innovations and new organizational ideals and goals within institutions of dental education.

Performance of Artificial Intelligence Models Designed for Diagnosis, Treatment Planning and Predicting Prognosis of Orthognathic Surgery (OGS)—A Scoping Review

The technological advancements in the field of medical science have led to an escalation in the development of artificial intelligence (AI) applications, which are being extensively used in health sciences. This scoping review aims to outline the application and performance of artificial intelligence models used for diagnosing, treatment planning and predicting the prognosis of orthognathic surgery (OGS). Data for this paper was searched through renowned electronic databases such as PubMed, Google Scholar, Scopus, Web of science, Embase and Cochrane for articles related to the research topic that have been published between January 2000 and February 2022. Eighteen articles that met the eligibility criteria were critically analyzed based on QUADAS-2 guidelines and the certainty of evidence of the included studies was assessed using the GRADE approach. AI has been applied for predicting the post-operative facial profiles and facial symmetry, deciding on the need for OGS, predicting perioperative blood loss, planning OGS, segmentation of maxillofacial structures for OGS, and differential diagnosis of OGS. AI models have proven to be efficient and have outperformed the conventional methods. These models are reported to be reliable and reproducible, hence they can be very useful for less experienced practitioners in clinical decision making and in achieving better clinical outcomes.

Potential and impact of artificial intelligence algorithms in dento-maxillofacial radiology

OBJECTIVES

Novel artificial intelligence (AI) learning algorithms in dento-maxillofacial radiology (DMFR) are continuously being developed and improved using advanced convolutional neural networks. This review provides an overview of the potential and impact of AI algorithms in DMFR.

MATERIALS AND METHODS

A narrative review was conducted on the literature on AI algorithms in DMFR.

RESULTS

In the field of DMFR, AI algorithms were mainly proposed for (1) automated detection of dental caries, periapical pathologies, root fracture, periodontal/peri-implant bone loss, and maxillofacial cysts/tumors; (2) classification of mandibular third molars, skeletal malocclusion, and dental implant systems; (3) localization of cephalometric landmarks; and (4) improvement of image quality. Data insufficiency, overfitting, and the lack of interpretability are the main issues in the development and use of image-based AI algorithms. Several strategies have been suggested to address these issues, such as data augmentation, transfer learning, semi-supervised training, few-shot learning, and gradient-weighted class activation mapping.

CONCLUSIONS

Further integration of relevant AI algorithms into one fully automatic end-to-end intelligent system for possible multi-disciplinary applications is very likely to be a field of increased interest in the future.

CLINICAL RELEVANCE

This review provides dental practitioners and researchers with a comprehensive understanding of the current development, performance, issues, and prospects of image-based AI algorithms in DMFR.

Vertical root fractures in root treated teeth-current status and future trends

Vertical root fracture (VRF) is a common reason for the extraction of root filled teeth. The accurate diagnosis of VRF may be challenging due to the absence of clinical signs, whilst conventional radiographic assessment is often inconclusive. However, an understanding of the aetiology of VRFs, and more importantly, the key predisposing factors, is crucial in identifying teeth that may be susceptible. Thorough clinical examination with magnification and co-axial lighting is essential in identifying VRFs, and although CBCT is unable to reliably detect VRFs per se, the pattern of bone loss typically associated with VRF can be fully appreciated, and therefore, increases the probability of correct diagnosis and management. The prevalence of VRFs in root filled teeth is significantly greater than in teeth with vital pulps, demonstrating that the combination of loss of structural integrity, presence of pre-existing fractures and biochemical effects of loss of vitality, are highly relevant. Careful assessment of the occlusal scheme, presence of deflective contacts and identification of parafunctional habits is imperative in both preventing and managing VRFs. Furthermore, anatomical factors such as root canal morphology, may predispose certain teeth to VRF. The influence of access cavity design and root canal instrumentation protocols should be considered although the impact of these on the fracture resistance of root filled teeth is not clearly validated. The post-endodontic restoration of root filled teeth should be expedient and considerate to the residual tooth structure. Posts should be placed 'passively' and excessive 'post-space' preparation should be avoided. This narrative review aims to present the aetiology, potential predisposing factors, histopathology, diagnosis and management of VRF and present perspectives for future research. Currently, there are limited options other than extraction for the management of VRF, although root resection may be considered in multi-rooted teeth. Innovative techniques to 'repair' VRFs using both orthograde and surgical approaches require further research and validation. The prevention of VRFs is critical; identifying susceptible teeth, utilizing conservative endodontic procedures, together with expedient and appropriate post-endodontic restorative procedures is paramount to reducing the incidence of terminal VRFs.

Augmented, Virtual and Mixed Reality in Dentistry: A Narrative Review on the Existing Platforms and Future Challenges

The recent advancements in digital technologies have led to exponential progress in dentistry. This narrative review aims to summarize the applications of Augmented Reality, Virtual Reality and Mixed Reality in dentistry and describes future challenges in digitalization, such as Artificial Intelligence and Robotics. Augmented Reality, Virtual Reality and Mixed Reality represent effective tools in the educational technology, as they can enhance students’ learning and clinical training. Augmented Reality and Virtual Reality and can also be useful aids during clinical practice. Augmented Reality can be used to add digital data to real life clinical data. Clinicians can apply Virtual Reality for a digital wax-up that provides a pre-visualization of the final post treatment result. In addition, both these technologies may also be employed to eradicate dental phobia in patients and further enhance patient’s education. Similarly, they can be used to enhance communication between the dentist, patient, and technician. Artificial Intelligence and Robotics can also improve clinical practice. Artificial Intelligence is currently developed to improve dental diagnosis and provide more precise prognoses of dental diseases, whereas Robotics may be used to assist in daily practice.

Current applications and development of artificial intelligence for digital dental radiography

In the last few years, artificial intelligence (AI) research has been rapidly developing and emerging in the field of dental and maxillofacial radiology. Dental radiography, which is commonly used in daily practices, provides an incredibly rich resource for AI development and attracted many researchers to develop its application for various purposes. This study reviewed the applicability of AI for dental radiography from the current studies. Online searches on PubMed and IEEE Xplore databases, up to December 2020, and subsequent manual searches were performed. Then, we categorized the application of AI according to similarity of the following purposes: diagnosis of dental caries, periapical pathologies, and periodontal bone loss; cyst and tumor classification; cephalometric analysis; screening of osteoporosis; tooth recognition and forensic odontology; dental implant system recognition; and image quality enhancement. Current development of AI methodology in each aforementioned application were subsequently discussed. Although most of the reviewed studies demonstrated a great potential of AI application for dental radiography, further development is still needed before implementation in clinical routine due to several challenges and limitations, such as lack of datasets size justification and unstandardized reporting format. Considering the current limitations and challenges, future AI research in dental radiography should follow standardized reporting formats in order to align the research designs and enhance the impact of AI development globally.

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.

A Deep Learning Approach to Segment and Classify C-Shaped Canal Morphologies in Mandibular Second Molars Using Cone-beam Computed Tomography

INTRODUCTION

The identification of C-shaped root canal anatomy on radiographic images affects clinical decision making and treatment. The aims of this study were to develop a deep learning (DL) model to classify C-shaped canal anatomy in mandibular second molars from cone-beam computed tomographic (CBCT) volumes and to compare the performance of 3 different architectures.

METHODS

U-Net, residual U-Net, and Xception U-Net architectures were used for image segmentation and classification of C-shaped anatomies. Model training and validation were performed on 100 of a total of 135 available limited field of view CBCT images containing mandibular molars with C-shaped anatomy. Thirty-five CBCT images were used for testing. Voxel-matching accuracy of the automated labeling of the C-shaped anatomy was assessed with the Dice index. The mean sensitivity of predicting the correct C-shape subcategory was calculated based on detection accuracy. One-way analysis of variance and post hoc Tukey honestly significant difference tests were used for statistical evaluation.

RESULTS

The mean Dice coefficients were 0.768 ± 0.0349 for Xception U-Net, 0.736 ± 0.0297 for residual U-Net, and 0.660 ± 0.0354 for U-Net on the test data set. The performance of the 3 models was significantly different overall (analysis of variance, P = .000779). Both Xception U-Net (Q = 7.23, P = .00070) and residual U-Net (Q = 5.09, P = .00951) performed significantly better than U-Net (post hoc Tukey honestly significant difference test). The mean sensitivity values were 0.786 ± 0.0378 for Xception U-Net, 0.746 ± 0.0391 for residual U-Net, and 0.720 ± 0.0495 for U-Net. The mean positive predictive values were 77.6% ± 0.1998% for U-Net, 78.2% ± 0.0.1971% for residual U-Net, and 80.0% ± 0.1098% for Xception U-Net. The addition of contrast-limited adaptive histogram equalization had improved overall architecture efficacy by a mean of 4.6% (P < .0001).

CONCLUSIONS

DL may aid in the detection and classification of C-shaped canal anatomy.

Micro-Computed Tomography-Guided Artificial Intelligence for Pulp Cavity and Tooth Segmentation on Cone-beam Computed Tomography

INTRODUCTION

This study proposes a novel data pipeline based on micro-computed tomographic (micro-CT) data for training the U-Net network to realize the automatic and accurate segmentation of the pulp cavity and tooth on cone-beam computed tomographic (CBCT) images.

METHODS

We collected CBCT data and micro-CT data of 30 teeth. CBCT data were processed and transformed into small field of view and high-resolution CBCT images of each tooth. Twenty-five sets were randomly assigned to the training set and the remaining 5 sets to the test set. We used 2 data pipelines for U-Net network training: one manually labeled by an endodontic specialist as the control group and one processed from the micro-CT data as the experimental group. The 3-dimensional models constructed using micro-CT data in the test set were taken as the ground truth. The Dice similarity coefficient, precision rate, recall rate, average symmetric surface distance, Hausdorff distance, and morphologic analysis were used for performance evaluation.

RESULTS

The segmentation accuracy of the experimental group measured by the Dice similarity coefficient, precision rate, recall rate, average symmetric surface distance, and Hausdorff distance were 96.20% ± 0.58%, 97.31% ± 0.38%, 95.11% ± 0.97%, 0.09 ± 0.01 mm, and 1.54 ± 0.51 mm in the tooth and 86.75% ± 2.42%, 84.45% ± 7.77%, 89.94% ± 4.56%, 0.08 ± 0.02 mm, and 1.99 ± 0.67 mm in the pulp cavity, respectively, which were better than the control group. Morphologic analysis suggested the segmentation results of the experimental group were better than those of the control group.

CONCLUSIONS

This study proposed an automatic and accurate approach for tooth and pulp cavity segmentation on CBCT images, which can be applied in research and clinical tasks.

Artificial Intelligence in Endodontics: Current Applications and Future Directions

INTRODUCTION

Artificial intelligence (AI) has the potential to replicate human intelligence to perform prediction and complex decision making in health care and has significantly increased its presence and relevance in various tasks and applications in dentistry, especially endodontics. The aim of this review was to discuss the current endodontic applications of AI and potential future directions.

METHODS

Articles that have addressed the applications of AI in endodontics were evaluated for information pertinent to include in this narrative review.

RESULTS

AI models (eg, convolutional neural networks and/or artificial neural networks) have demonstrated various applications in endodontics such as studying root canal system anatomy, detecting periapical lesions and root fractures, determining working length measurements, predicting the viability of dental pulp stem cells, and predicting the success of retreatment procedures. The future of this technology was discussed in light of helping with scheduling, treating patients, drug-drug interactions, diagnosis with prognostic values, and robotic-assisted endodontic surgery.

CONCLUSIONS

AI demonstrated accuracy and precision in terms of detection, determination, and disease prediction in endodontics. AI can contribute to the improvement of diagnosis and treatment that can lead to an increase in the success of endodontic treatment outcomes. However, it is still necessary to further verify the reliability, applicability, and cost-effectiveness of AI models before transferring these models into day-to-day clinical practice.