Deep Learning for the Radiographic Detection of Periodontal Bone Loss

An AI-assisted explainable mTMCNN architecture for detection of mandibular third molar presence from panoramic radiography

OBJECTIVE

This study aimed to design and systematically evaluate an architecture, proposed as the Explainable Mandibular Third Molar Convolutional Neural Network (E-mTMCNN), for detecting the presence of mandibular third molars (m-M3) in panoramic radiography (PR). The proposed architecture seeks to enhance the accuracy of early detection and improve clinical decision-making and treatment planning in dentistry.

METHODS

A new dataset, named the Mandibular Third Molar (m-TM) dataset, was developed through expert labeling of raw PR images from the UESB dataset. This dataset was subsequently made publicly accessible to support further research. Several advanced image preprocessing techniques, including Gaussian filtering, gamma correction, and data augmentation, were applied to improve image quality. Various Deep learning (DL) based Convolutional Neural Network (CNN) architectures were trained and validated using Transfer Learning (TL) methodologies. Among these, the E-mTMCNN, leveraging the GoogLeNet architecture, achieved the highest performance metrics. To ensure transparency in the model's decision-making process, Local Interpretable Model-Agnostic Explanations (LIME) were integrated as an eXplainable Artificial Intelligence (XAI) approach. Clinical reliability and applicability were assessed through an expert survey conducted among specialized dentists using a decision support system based on the E-mTMCNN.

RESULTS

The E-mTMCNN architecture demonstrated a classification accuracy of 87.02%, with a sensitivity of 75%, specificity of 94.73%, precision of 77.68%, an F1 score of 75.51%, and an area under the curve (AUC) of 87.01%. The integration of LIME provided visual explanations of the model's decision-making rationale, reinforcing the robustness of the proposed architecture. Results from the expert survey indicated high clinical acceptance and confidence in the reliability of the system.

CONCLUSION

The findings demonstrate that the E-mTMCNN architecture effectively detects the presence of m-M3 in PRs, outperforming current state-of-the-art methodologies. The proposed architecture shows considerable potential for integration into computer-aided diagnostic systems, advancing early detection capabilities and enhancing the precision of treatment planning in dental practice.

Evaluation by dental professionals of an artificial intelligence-based application to measure alveolar bone loss

BACKGROUND

Several commercial programs incorporate artificial intelligence in diagnosis, but very few dental professionals have been surveyed regarding its acceptability and usability. Furthermore, few have explored how these advances might be incorporated into routine practice.

METHODS

Our team developed and implemented a deep learning (DL) model employing semantic segmentation neural networks and object detection networks to precisely identify alveolar bone crestal levels (ABCLs) and cemento-enamel junctions (CEJs) to measure change in alveolar crestal height (ACH). The model was trained and validated using a 550 bitewing radiograph dataset curated by an oral radiologist, setting a gold standard for ACH measurements. A twenty-question survey was created to compare the accuracy and efficiency of manual X-ray examination versus the application and to assess the acceptability and usability of the application.

RESULTS

In total, 56 different dental professionals classified severe (ACH > 5 mm) vs. non-severe (ACH ≤ 5 mm) periodontal bone loss on 35 calculable ACH measures. Dental professionals accurately identified between 35-87% of teeth with severe periodontal disease, whereas the artificial intelligence (AI) application achieved an 82-87% accuracy rate. Among the 65 participants who completed the acceptability and usability survey, more than half the participants (52%) were from an academic setting. Only 21% of participants reported that they already used automated or AI-based software in their practice to assist in reading of X-rays. The majority, 57%, stated that they only approximate when measuring bone levels and only 9% stated that they measure with a ruler. The survey indicated that 84% of participants agreed or strongly agreed with the AI application measurement of ACH. Furthermore, 56% of participants agreed that AI would be helpful in their professional setting.

CONCLUSION

Overall, the study demonstrates that an AI application for detecting alveolar bone has high acceptability among dental professionals and may provide benefits in time saving and increased clinical accuracy.

Detection of periodontal bone loss and periodontitis from 2D dental radiographs via machine learning and deep learning: systematic review employing APPRAISE-AI and meta-analysis

OBJECTIVES

Periodontitis is a serious periodontal infection that damages the soft tissues and bone around teeth and is linked to systemic conditions. Accurate diagnosis and staging, complemented by radiographic evaluation, are vital. This systematic review (PROSPERO ID: CRD42023480552) explores artificial intelligence (AI) applications in assessing alveolar bone loss and periodontitis on dental panoramic and periapical radiographs.

METHODS

Five databases (Medline, Embase, Scopus, Web of Science, and Cochrane's Library) were searched from January 1990 to January 2024. Keywords related to "artificial intelligence", "Periodontal bone loss/Periodontitis", and "Dental radiographs" were used. Risk of bias and quality assessment of included papers were performed according to the APPRAISE-AI Tool for Quantitative Evaluation of AI Studies for Clinical Decision Support. Meta analysis was carried out via the "metaprop" command in R V3.6.1.

RESULTS

Thirty articles were included in the review, where 10 papers were eligible for meta-analysis. Based on quality scores from the APPRAISE-AI critical appraisal tool of the 30 papers, 1 (3.3%) were of very low quality (score < 40), 3 (10.0%) were of low quality (40 ≤ score < 50), 19 (63.3%) were of intermediate quality (50 ≤ score < 60), and 7 (23.3%) were of high quality (60 ≤ score < 80). No papers were of very high quality (score ≥ 80). Meta-analysis indicated that model performance was generally good, eg, sensitivity 87% (95% CI, 80%-93%), specificity 76% (95% CI, 69%-81%), and accuracy 84% (95% CI, 75%-91%).

CONCLUSION

Deep learning shows much promise in evaluating periodontal bone levels, although there was some variation in performance. AI studies can lack transparency and reporting standards could be improved. Our systematic review critically assesses the application of deep learning models in detecting alveolar bone loss on dental radiographs using the APPRAISE-AI tool, highlighting their efficacy and identifying areas for improvement, thus advancing the practice of clinical radiology.

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

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

Artificial Intelligence-Related Dental Research: Bibliometric and Altmetric Analysis

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Deep Learning for Staging Periodontitis Using Panoramic Radiographs

OBJECTIVES

Utilizing a deep learning approach is an emerging trend to improve the efficiency of periodontitis diagnosis and classification. This study aimed to use an object detection model to automatically annotate the anatomic structure and subsequently classify the stages of radiographic bone loss (RBL).

MATERIALS AND METHODS

In all, 558 panoramic radiographs were cropped to 7359 pieces of individual teeth. The detection performance of the model was assessed using mean average precision (mAP), root mean squared error (RMSE). The classification performance was evaluated using accuracy, precision, recall, and F1 score. Additionally, receiver operating characteristic (ROC) curves and confusion matrices were presented, and the area under the ROC curve (AUC) was calculated.

RESULTS

The mAP was 0.88 when the difference between the ground truth and prediction was 10 pixels, and 0.99 when the difference was 25 pixels. For all images, the mean RMSE was 7.30 pixels. Overall, the accuracy, precision, recall, F1 score, and micro-average AUC of the prediction were 0.72, 0.76, 0.64, 0.68, and 0.79, respectively.

CONCLUSIONS

The current model is reliable in assisting with the detection and staging of radiographic bone levels.

Applications of Artificial Intelligence in Dental Medicine: A Critical Review

INTRODUCTION

Artificial intelligence (AI), including its subfields of machine learning and deep learning, is a branch of computer science and engineering focused on creating machines capable of tasks requiring human-like intelligence, such as visual perception, decision-making, and natural language processing. AI applications have become increasingly prevalent in dental medicine, generating high expectations as well as raising ethical and practical concerns.

METHODS

This critical review evaluates the current applications of AI in dentistry, identifying key perspectives, challenges, and limitations in ongoing AI research.

RESULTS

AI models have been applied across various dental specialties, supporting diagnosis, treatment planning, and decision-making, while also reducing the burden of repetitive tasks and optimizing clinical workflows. However, ethical complexities and methodological limitations, such as inconsistent data quality, bias risk, lack of transparency, and limited clinical validation, undermine the quality of AI studies and hinder the effective integration of AI into routine dental practice.

CONCLUSIONS

To improve AI research, studies must adhere to standardized methodological and ethical guidelines, particularly in data collection, while ensuring transparency, privacy, and accountability. Developing a comprehensive framework for producing robust, reproducible AI research and clinically validated technologies will facilitate the seamless integration of AI into clinical practice, benefiting both clinicians and patients by improving dental care.

Artificial intelligence-powered innovations in periodontal diagnosis: a new era in dental healthcare

Background

The aging population is increasingly affected by periodontal disease, a condition often overlooked due to its asymptomatic nature. Despite its silent onset, periodontitis is linked to various systemic conditions, contributing to severe complications and a reduced quality of life. With over a billion people globally affected, periodontal diseases present a significant public health challenge. Current diagnostic methods, including clinical exams and radiographs, have limitations, emphasizing the need for more accurate detection methods. This study aims to develop AI-driven models to enhance diagnostic precision and consistency in detecting periodontal disease.

Methods

We analyzed 2,000 panoramic radiographs using image processing techniques. The YOLOv8 model segmented teeth, identified the cemento-enamel junction (CEJ), and quantified alveolar bone loss to assess stages of periodontitis.

Results

The teeth segmentation model achieved an accuracy of 97%, while the CEJ and alveolar bone segmentation models reached 98%. The AI system demonstrated outstanding performance, with 94.4% accuracy and perfect sensitivity (100%), surpassing periodontists who achieved 91.1% accuracy and 90.6% sensitivity. General practitioners (GPs) benefitted from AI assistance, reaching 86.7% accuracy and 85.9% sensitivity, further improving diagnostic outcomes.

Conclusions

This study highlights that AI models can effectively detect periodontal bone loss from panoramic radiographs, outperforming current diagnostic methods. The integration of AI into periodontal care offers faster, more accurate, and comprehensive treatment, ultimately improving patient outcomes and alleviating healthcare burdens.

Precision Medicine Assessment of the Radiographic Defect Angle of the Intrabony Defect in Periodontal Lesions by Deep Learning of Bitewing Radiographs

In dental diagnosis, evaluating the severity of periodontal disease by analyzing the radiographic defect angle of the intrabony defect is essential for effective treatment planning. However, dentists often rely on clinical examinations and manual analysis, which can be time-consuming and labor-intensive. Due to the high recurrence rate of periodontal disease after treatment, accurately evaluating the radiographic defect angle of the intrabony defect is vital for implementing targeted interventions, which can improve treatment outcomes and reduce recurrence. This study aims to streamline clinical practices and enhance patient care in managing periodontal disease by determining its severity based on the analysis of the radiographic defect angle of the intrabony defect. In this approach, radiographic defect angles of the intrabony defect greater than 37 degrees are classified as severe, while those less than 37 degrees are considered mild. This study employed a series of novel image enhancement techniques to significantly improve diagnostic accuracy. Before enhancement, the maximum accuracy was 78.85%, which increased to 95.12% following enhancement. YOLOv8 detects the affected tooth, and its mAP can reach 95.5%, with a precision reach of 94.32%. This approach assists dentists in swiftly assessing the extent of periodontal erosion, enabling timely and appropriate treatment. These techniques reduce diagnostic time and improve healthcare quality.

Advanced AI-assisted panoramic radiograph analysis for periodontal prognostication and alveolar bone loss detection

Background

Periodontitis is a chronic inflammatory disease affecting the gingival tissues and supporting structures of the teeth, often leading to tooth loss. The condition begins with the accumulation of dental plaque, which initiates an immune response. Current radiographic methods for assessing alveolar bone loss are subjective, time-consuming, and labor-intensive. This study aims to develop an AI-driven model using Convolutional Neural Networks (CNNs) to accurately assess alveolar bone loss and provide individualized periodontal prognoses from panoramic radiographs.

Methods

A total of 2,000 panoramic radiographs were collected using the same device, based on the periodontal diagnosis codes from the HOSxP Program. Image enhancement techniques were applied, and an AI model based on YOLOv8 was developed to segment teeth, identify the cemento-enamel junction (CEJ), and assess alveolar bone levels. The model quantified bone loss and classified prognoses for each tooth.

Results

The teeth segmentation model achieved 97% accuracy, 90% sensitivity, 96% specificity, and an F1 score of 0.80. The CEJ and bone level segmentation model showed superior results with 98% accuracy, 100% sensitivity, 98% specificity, and an F1 score of 0.90. These findings confirm the models' effectiveness in analyzing panoramic radiographs for periodontal bone loss detection and prognostication.

Conclusion

This AI model offers a state-of-the-art approach for assessing alveolar bone loss and predicting individualized periodontal prognoses. It provides a faster, more accurate, and less labor-intensive alternative to current methods, demonstrating its potential for improving periodontal diagnosis and patient outcomes.

Periodontitis diagnosis: A review of current and future trends in artificial intelligence

BACKGROUND

Artificial intelligence (AI) acts as the state-of-the-art in periodontitis diagnosis in dentistry. Current diagnostic challenges include errors due to a lack of experienced dentists, limited time for radiograph analysis, and mandatory reporting, impacting care quality, cost, and efficiency.

OBJECTIVE

This review aims to evaluate the current and future trends in AI for diagnosing periodontitis.

METHODS

A thorough literature review was conducted following PRISMA guidelines. We searched databases including PubMed, Scopus, Wiley Online Library, and ScienceDirect for studies published between January 2018 and December 2023. Keywords used in the search included "artificial intelligence," "panoramic radiograph," "periodontitis," "periodontal disease," and "diagnosis."

RESULTS

The review included 12 studies from an initial 211 records. These studies used advanced models, particularly convolutional neural networks (CNNs), demonstrating accuracy rates for periodontal bone loss detection ranging from 0.76 to 0.98. Methodologies included deep learning hybrid methods, automated identification systems, and machine learning classifiers, enhancing diagnostic precision and efficiency.

CONCLUSIONS

Integrating AI innovations in periodontitis diagnosis enhances diagnostic accuracy and efficiency, providing a robust alternative to conventional methods. These technologies offer quicker, less labor-intensive, and more precise alternatives to classical approaches. Future research should focus on improving AI model reliability and generalizability to ensure widespread clinical adoption.

Clinical knowledge-guided hybrid classification network for automatic periodontal disease diagnosis in X-ray image

Accurate classification of periodontal disease through panoramic X-ray images carries immense clinical importance for effective diagnosis and treatment. Recent methodologies attempt to classify periodontal diseases from X-ray images by estimating bone loss within these images, supervised by manual radiographic annotations for segmentation or keypoint detection. However, these annotations often lack consistency with the clinical gold standard of probing measurements, potentially causing measurement inaccuracy and leading to unstable classifications. Additionally, the diagnosis of periodontal disease necessitates exceptional sensitivity. To address these challenges, we introduce HC-Net, an innovative hybrid classification framework devised for accurately classifying periodontal disease from X-ray images. This framework comprises three main components: tooth-level classification, patient-level classification, and a learnable adaptive noisy-OR gate. In the tooth-level classification, we initially employ instance segmentation to individually identify each tooth, followed by tooth-level periodontal disease classification. For patient-level classification, we utilize a multi-task strategy to concurrently learn patient-level classification and a Classification Activation Map (CAM) that signifies the confidence of local lesion areas within the panoramic X-ray image. Eventually, our adaptive noisy-OR gate acquires a hybrid classification by amalgamating predictions from both levels. In particular, we incorporate clinical knowledge into the workflows used by professional dentists, targeting the enhanced handling of sensitivity of periodontal disease diagnosis. Extensive empirical testing on a dataset amassed from real-world clinics demonstrates that our proposed HC-Net achieves unparalleled performance in periodontal disease classification, exhibiting substantial potential for practical application.

Limitations of panoramic radiographs in predicting mandibular wisdom tooth extraction and the potential of deep learning models to overcome them

Surgeons routinely interpret preoperative radiographic images for estimating the shape and position of the tooth prior to performing tooth extraction. In this study, we aimed to predict the difficulty of lower wisdom tooth extraction using only panoramic radiographs. Difficulty was evaluated using the modified Parant score. Two oral surgeons (a specialist and a clinical resident) predicted the difficulty level of the test data. This study also aimed to evaluate the performance of a deep learning model in predicting the necessity for tooth separation or bone removal during wisdom tooth extraction. Two convolutional neural networks (AlexNet and VGG-16) were created and trained using panoramic X-ray images. Both surgeons interpreted the same images and classified them into three groups. The accuracies for humans were 54.4% for both surgeons, 57.7% for AlexNet, and 54.4% for VGG-16. These results indicate that accurately predict the difficulty of wisdom teeth extraction using panoramic radiographs alone is challenging. However, AlexNet and VGG-16 had sensitivities of more than 90% for crown and root separation. The predictive ability of our proposed model is equivalent to that of an oral surgery specialist, and a recall value > 90% makes it suitable for screening in clinical settings.

Observational Diagnostics: The Building Block of AI-Powered Visual Aid for Dental Practitioners

Artificial intelligence (AI) has gained significant traction in medical image analysis, including dentistry, aiding clinicians in making timely and accurate diagnoses. Radiographs, such as orthopantomograms (OPGs) and intraoral radiographs, along with clinical photographs, are the primary imaging modalities employed for AI-powered analysis in the dental field. In this review, we discuss the most recent research and product developments concerning the clinical application of AI as a visual aid in dentistry and introduce the concept of Observational Diagnostics (ODs) as a structured method to standardise image analysis. ODs serve as foundational elements for AI-driven diagnostic aids and have the potential to improve the consistency and reliability of diagnostic data used in treatment planning. We provide illustrative examples to demonstrate how ODs not only represent a significant advancement towards more precise diagnostic aids but also provide the basis for the generation of evidence-based treatment recommendations. These OD-based algorithms have been integrated into chairside AI applications to streamline clinical workflows to improve consistency, accuracy, and efficiency.

Applications of artificial intelligence in regenerative dentistry: promoting stem cell therapy and the scaffold development

Tissue repair represents a critical concern within the domain of dentistry. On a daily basis, countless individuals seek dental clinic services due to inadequate dental care. Many of the treatments that patients receive have unfavorable side effects. The employment of innovative methodologies, including gene therapy, tissue engineering, and stem cell (SCs) applications for regenerative purposes, has garnered significant interest over the past years. In recent times, artificial intelligence, particularly neural networks, has emerged as a topic of considerable attention among many medical professionals. Artificial intelligence possesses the capability to analyze data patterns through learning algorithms. Research opportunities in the rapidly expanding field of health sciences have been made possible by the use of artificial intelligence (AI) technologies. Though its uses are not restricted to these situations, artificial intelligence (AI) has the potential to improve and accelerate many aspects of regenerative medicine research and development, especially when working with complicated patterns. This review article is to investigate how artificial intelligence might be used to enhance regenerative processes in dentistry by using scaffolds and stem cells, in light of the continuous advances in artificial intelligence in the fields of medicine and tissue regeneration. It highlights the difficulties that still exist in this developing sector and explores the possible uses of AI with a particular emphasis on dentistry practices.

Application of artificial intelligence-based detection of furcation involvement in mandibular first molar using cone beam tomography images- a preliminary study

BACKGROUND

Radiographs play a key role in diagnosis of periodontal diseases. Deep learning models have been explored for image analysis in periodontal diseases. However, there is lacuna of research in the deep learning model-based detection of furcation involvements [FI]. The objective of this study was to determine the accuracy of deep learning model in the detection of FI in axial CBCT images.

METHODOLOGY

We obtained initial dataset 285 axial CBCT images among which 143 were normal (without FI) and 142 were abnormal (with FI). Data augmentation technique was used to create 600(300 normal and 300 abnormal) images by using 200 images from the training dataset. Remaining 85(43 normal and 42 abnormal) images were kept for testing of model. ResNet101V2 with transfer learning was used employed for the analysis of images.

RESULTS

Training accuracy of model is 98%, valid accuracy is 97% and test accuracy is 91%. The precision and F1 score were 0.98 and 0.98 respectively. The Area under curve (AUC) was reported at 0.98. The test loss was reported at 0.2170.

CONCLUSION

The deep learning model (ResNet101V2) can accurately detect the FI in axial CBCT images. However, since our study was preliminary in nature and carried out with relatively smaller dataset, a study with larger dataset will further confirm the accuracy of deep learning models.

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

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

Automated detection and labeling of posterior teeth in dental bitewing X-rays using deep learning

Standardized tooth numbering is crucial in dentistry for accurate recordkeeping, targeted procedures, and effective communication in both clinical and forensic contexts. However, conventional manual methods are prone to errors, time-consuming, and susceptible to inconsistencies. This study presents an artificial intelligence (AI)-powered system that uses a deep learning-based object detection approach to automate tooth numbering in bitewing radiographs (BRs). The system follows the widely accepted FDI two-digit notation system and employs a state-of-the-art YOLO architecture. This one-stage model provides fast inference by simultaneously performing object detection and classification. A comprehensive dataset of 3000 adult digital BRs was used for training and evaluation, covering various scenarios to improve the robustness of the tooth numbering approach. Performance was assessed based on precision, recall, and mean average precision (mAP). The proposed method showcases the potential of AI-powered systems utilizing sophisticated YOLO architectures to automatically detect and label teeth in dental X-rays. It achieved impressive results, demonstrating a precision of 0.99 and 0.963, recall of 0.995 and 0.965, and mAP of 0.99 and 0.963 for tooth detecting and tooth numbering, respectively. With an average inference time of 303 ms per BR when using a central processing unit (CPU) and 9.1 ms when using a graphics processing unit (GPU), the system seamlessly integrates into clinical workflows without sacrificing efficiency. This results in significant time savings for dental professionals while maintaining productivity in fast-paced clinical environments.

Mapping the Use of Artificial Intelligence-Based Image Analysis for Clinical Decision-Making in Dentistry: A Scoping Review

OBJECTIVES

Artificial intelligence (AI) is an emerging field in dentistry. AI is gradually being integrated into dentistry to improve clinical dental practice. The aims of this scoping review were to investigate the application of AI in image analysis for decision-making in clinical dentistry and identify trends and research gaps in the current literature.

MATERIAL AND METHODS

This review followed the guidelines provided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). An electronic literature search was performed through PubMed and Scopus. After removing duplicates, a preliminary screening based on titles and abstracts was performed. A full-text review and analysis were performed according to predefined inclusion criteria, and data were extracted from eligible articles.

RESULTS

Of the 1334 articles returned, 276 met the inclusion criteria (consisting of 601,122 images in total) and were included in the qualitative synthesis. Most of the included studies utilized convolutional neural networks (CNNs) on dental radiographs such as orthopantomograms (OPGs) and intraoral radiographs (bitewings and periapicals). AI was applied across all fields of dentistry - particularly oral medicine, oral surgery, and orthodontics - for direct clinical inference and segmentation. AI-based image analysis was use in several components of the clinical decision-making process, including diagnosis, detection or classification, prediction, and management.

CONCLUSIONS

A variety of machine learning and deep learning techniques are being used for dental image analysis to assist clinicians in making accurate diagnoses and choosing appropriate interventions in a timely manner.

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.

Radiographical diagnostic competences of dental students using various feedback methods and integrating an artificial intelligence application-A randomized clinical trial

INTRODUCTION

Radiographic diagnostic competences are a primary focus of dental education. This study assessed two feedback methods to enhance learning outcomes and explored the feasibility of artificial intelligence (AI) to support education.

MATERIALS AND METHODS

Fourth-year dental students had access to 16 virtual radiological example cases for 8 weeks. They were randomly assigned to either elaborated feedback (eF) or knowledge of results feedback (KOR) based on expert consensus. Students´ diagnostic competences were tested on bitewing/periapical radiographs for detection of caries, apical periodontitis, accuracy for all radiological findings and image quality. We additionally assessed the accuracy of an AI system (dentalXrai Pro 3.0), where applicable. Data were analysed descriptively and using ROC analysis (accuracy, sensitivity, specificity, AUC). Groups were compared with Welch's t-test.

RESULTS

Among 55 students, the eF group by large performed significantly better than the KOR group in detecting enamel caries (accuracy 0.840 ± 0.041, p = .196; sensitivity 0.638 ± 0.204, p = .037; specificity 0.859 ± 0.050, p = .410; ROC AUC 0.748 ± 0.094, p = .020), apical periodontitis (accuracy 0.813 ± 0.095, p = .011; sensitivity 0.476 ± 0.230, p = .003; specificity 0.914 ± 0.108, p = .292; ROC AUC 0.695 ± 0.123, p = .001) and in assessing the image quality of periapical images (p = .031). No significant differences were observed for the other outcomes. The AI showed almost perfect diagnostic performance (enamel caries: accuracy 0.964, sensitivity 0.857, specificity 0.074; dentin caries: accuracy 0.988, sensitivity 0.941, specificity 1.0; overall: accuracy 0.976, sensitivity 0.958, specificity 0.983).

CONCLUSION

Elaborated feedback can improve student's radiographic diagnostic competences, particularly in detecting enamel caries and apical periodontitis. Using an AI may constitute an alternative to expert labelling of radiographs.

Segmentation of periapical lesions with automatic deep learning on panoramic radiographs: an artificial intelligence study

Periapical periodontitis may manifest as a radiographic lesion radiographically. Periapical lesions are amongst the most common dental pathologies that present as periapical radiolucencies on panoramic radiographs. The objective of this research is to assess the diagnostic accuracy of an artificial intelligence (AI) model based on U²-Net architecture in the detection of periapical lesions on dental panoramic radiographs and to determine whether they can be useful in aiding clinicians with diagnosis of periapical lesions and improving their clinical workflow. 400 panoramic radiographs that included at least one periapical radiolucency were selected retrospectively. 780 periapical radiolucencies in these anonymized radiographs were manually labeled by two independent examiners. These radiographs were later used to train the AI model based on U²-Net architecture trained using a deep supervision algorithm. An AI model based on the U²-Net architecture was implemented. The model achieved a dice score of 0.8 on the validation set and precision, recall, and F1-score of 0.82, 0.77, and 0.8 respectively on the test set. This study has shown that an AI model based on U²-Net architecture can accurately diagnose periapical lesions on panoramic radiographs. The research provides evidence that AI-based models have promising applications as adjunct tools for dentists in diagnosing periapical radiolucencies and procedure planning. Further studies with larger data sets would be required to improve the diagnostic accuracy of AI-based detection models.

Exploring the Applications of Artificial Intelligence in Dental Image Detection: A Systematic Review

BACKGROUND

Dental care has been transformed by neural networks, introducing advanced methods for improving patient outcomes. By leveraging technological innovation, dental informatics aims to enhance treatment and diagnostic processes. Early diagnosis of dental problems is crucial, as it can substantially reduce dental disease incidence by ensuring timely and appropriate treatment. The use of artificial intelligence (AI) within dental informatics is a pivotal tool that has applications across all dental specialties. This systematic literature review aims to comprehensively summarize existing research on AI implementation in dentistry. It explores various techniques used for detecting oral features such as teeth, fillings, caries, prostheses, crowns, implants, and endodontic treatments. AI plays a vital role in the diagnosis of dental diseases by enabling precise and quick identification of issues that may be difficult to detect through traditional methods. Its ability to analyze large volumes of data enhances diagnostic accuracy and efficiency, leading to better patient outcomes.

METHODS

An extensive search was conducted across a number of databases, including Science Direct, PubMed (MEDLINE), arXiv.org, MDPI, Nature, Web of Science, Google Scholar, Scopus, and Wiley Online Library.

RESULTS

The studies included in this review employed a wide range of neural networks, showcasing their versatility in detecting the dental categories mentioned above. Additionally, the use of diverse datasets underscores the adaptability of these AI models to different clinical scenarios. This study highlights the compatibility, robustness, and heterogeneity among the reviewed studies. This indicates that AI technologies can be effectively integrated into current dental practices. The review also discusses potential challenges and future directions for AI in dentistry. It emphasizes the need for further research to optimize these technologies for broader clinical applications.

CONCLUSIONS

By providing a detailed overview of AI's role in dentistry, this review aims to inform practitioners and researchers about the current capabilities and future potential of AI-driven dental care, ultimately contributing to improved patient outcomes and more efficient dental practices.

Tooth numbering with polygonal segmentation on periapical radiographs: an artificial intelligence study

OBJECTIVES

Accurately identification and tooth numbering on radiographs is essential for any clinicians. The aim of the present study was to validate the hypothesis that Yolov5, a type of artificial intelligence model, can be trained to detect and number teeth in periapical radiographs.

MATERIALS AND METHODS

Six thousand four hundred forty six anonymized periapical radiographs without motion-related artifacts were randomly selected from the database. All periapical radiographs in which all boundaries of any tooth could be distinguished were included in the study. The radiographic images used were randomly divided into three groups: 80% training, 10% validation, and 10% testing. The confusion matrix was used to examine model success.

RESULTS

During the test phase, 2578 labelings were performed on 644 periapical radiographs. The number of true positive was 2434 (94.4%), false positive was 115 (4.4%), and false negative was 29 (1.2%). The recall, precision, and F1 scores were 0.9882, 0.9548, and 0.9712, respectively. Moreover, the model yielded an area under curve (AUC) of 0.603 on the receiver operating characteristic curve (ROC).

CONCLUSIONS

This study showed us that YOLOv5 is nearly perfect for numbering teeth on periapical radiography. Although high success rates were achieved as a result of the study, it should not be forgotten that artificial intelligence currently only can be guides dentists for accurate and rapid diagnosis.

CLINICAL RELEVANCE

It is thought that dentists can accelerate the radiographic examination time and inexperienced dentists can reduce the error rate by using YOLOv5. Additionally, YOLOv5 can also be used in the education of dentistry students.

Automating Dental Condition Detection on Panoramic Radiographs: Challenges, Pitfalls, and Opportunities

Background/Objectives: The integration of AI into dentistry holds promise for improving diagnostic workflows, particularly in the detection of dental pathologies and pre-radiotherapy screening for head and neck cancer patients. This study aimed to develop and validate an AI model for detecting various dental conditions, with a focus on identifying teeth at risk prior to radiotherapy. Methods: A YOLOv8 model was trained on a dataset of 1628 annotated panoramic radiographs and externally validated on 180 radiographs from multiple centers. The model was designed to detect a variety of dental conditions, including periapical lesions, impacted teeth, root fragments, prosthetic restorations, and orthodontic devices. Results: The model showed strong performance in detecting implants, endodontic treatments, and surgical devices, with precision and recall values exceeding 0.8 for several conditions. However, performance declined during external validation, highlighting the need for improvements in generalizability. Conclusions: YOLOv8 demonstrated robust detection capabilities for several dental conditions, especially in training data. However, further refinement is needed to enhance generalizability in external datasets and improve performance for conditions like periapical lesions and bone loss.

Comparing the accuracy of two machine learning models in detection and classification of periapical lesions using periapical radiographs

BACKGROUND

Previous deep learning-based studies were mainly conducted on detecting periapical lesions; limited information in classification, such as the periapical index (PAI) scoring system, is available. The study aimed to apply two deep learning models, Faster R-CNN and YOLOv4, in detecting and classifying periapical lesions using the PAI score from periapical radiographs (PR) in three different regions of the dental arch: anterior teeth, premolars, and molars.

METHODS

Out of 2658 PR selected for the study, 2122 PR were used for training, 268 PR were used for validation and 268 PR were used for testing. The diagnosis made by experienced dentists was used as the reference diagnosis.

RESULTS

The Faster R-CNN and YOLOv4 models obtained great sensitivity, specificity, accuracy, and precision for detecting periapical lesions. No clear difference in the performance of both models among these three regions was found. The true prediction of Faster R-CNN was 89%, 83.01% and 91.84% for PAI 3, PAI 4 and PAI 5 lesions, respectively. The corresponding values of YOLOv4 were 68.06%, 50.94%, and 65.31%.

CONCLUSIONS

Our study demonstrated the potential of YOLOv4 and Faster R-CNN models for detecting and classifying periapical lesions based on the PAI scoring system using periapical radiographs.

Toward Digital Periodontal Health: Recent Advances and Future Perspectives

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

Evaluation of tooth development stages with deep learning-based artificial intelligence algorithm

BACKGROUND

This study aims to evaluate the performance of a deep learning system for the evaluation of tooth development stages on images obtained from panoramic radiographs from child patients.

METHODS

The study collected a total of 1500 images obtained from panoramic radiographs from child patients between the ages of 5 and 14 years. YOLOv5, a convolutional neural network (CNN)-based object detection model, was used to automatically detect the calcification states of teeth. Images obtained from panoramic radiographs from child patients were trained and tested in the YOLOv5 algorithm. True-positive (TP), false-positive (FP), and false-negative (FN) ratios were calculated. A confusion matrix was used to evaluate the performance of the model.

RESULTS

Among the 146 test group images with 1022 labels, there were 828 TPs, 308 FPs, and 1 FN. The sensitivity, precision, and F1-score values of the detection model of the tooth stage development model were 0.99, 0.72, and 0.84, respectively.

CONCLUSIONS

In conclusion, utilizing a deep learning-based approach for the detection of dental development on pediatric panoramic radiographs may facilitate a precise evaluation of the chronological correlation between tooth development stages and age. This can help clinicians make treatment decisions and aid dentists in finding more accurate treatment options.

Conquering class imbalances in deep learning-based segmentation of dental radiographs with different loss functions

OBJECTIVE

The imbalanced nature of real-world datasets is an ongoing challenge in the field of machine and deep learning. In medicine and in dentistry, most data samples represent patients not affected by pathologies, and on imagery, pathologic image areas are often smaller than healthy ones. Selecting suitable loss functions during deep learning is essential and may help to overcome the resulting imbalance. We assessed six different loss functions for one exemplary task, tooth structure segmentation on bitewing radiographs, for their performance.

METHODS

Six different loss functions (Focal Loss, Dice Loss, Tversky Loss and hybrid losses of Cross-Entropy and Dice Loss, Focal and Dice Loss, Focal and Generalized Dice Loss) were compared on a tooth structure segmentation task of 1,625 bitewing radiographs. Training was performed using three different model architectures (U-Net, Linknet, DeepLavbV3+) over a 5-fold cross-validation. Tooth structures consisted of the classes (occurrence in% of samples/captures areas measured on pixel level) enamel (100 %/25 %), dentin (100 %/50 %), root canal (100 %/10 %), filling (81 %/8 %) and crown (28 %/5 %).

RESULTS

Hybrid loss functions significantly outperformed standalone ones and provided robust results over the different architectures for the classes enamel, dentin, root canal and filling. Specifically, the Dice Focal loss reached high performance to conquer both image level and pixel level class imbalance, respectively.

CLINICAL SIGNIFICANCE

In dental use cases it is often important to predict minority classes such as pathologies accurately. Using specific loss function may be an effective strategy to overcome data imbalance when training deep learning models.

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

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

Deep learning based approach: automated gingival inflammation grading model using gingival removal strategy

Gingival inflammation grade serves as a well-established index in periodontitis. The aim of this study was to develop a deep learning network utilizing a novel feature extraction method for the automatic assessment of gingival inflammation. T-distributed Stochastic Neighbor Embedding (t-SNE) was utilized for dimensionality reduction. A convolutional neural network (CNN) model based on DenseNet was developed for the identification and evaluation of gingival inflammation. To enhance the performance of the deep learning (DL) model, a novel teeth removal algorithm was implemented. Additionally, a Grad-CAM +  + encoder was applied to generate heatmaps for computer visual attention analysis. The mean Intersection over Union (MIoU) for the identification of gingivitis was 0.727 ± 0.117. The accuracy rates for the five inflammatory degrees were 77.09%, 77.25%, 74.38%, 73.68% and 79.22%. The Area Under the Receiver Operating Characteristic (AUROC) values were 0.83, 0.80, 0.81, 0.81 and 0.84, respectively. The attention ratio towards gingival tissue increased from 37.73% to 62.20%, and within 8 mm of the gingival margin, it rose from 21.11% to 38.23%. On the gingiva, the overall attention ratio increased from 51.82% to 78.21%. The proposed DL model with novel feature extraction method provides high accuracy and sensitivity for identifying and grading gingival inflammation.

AI-Driven localization of all impacted teeth and prediction of winter angulation for third molars on panoramic radiographs: Clinical user interface design

PURPOSE

Impacted teeth are abnormal tooth disorders under the gums or jawbone that cannot take their normal position even though it is time to erupt. This study aims to detect all impacted teeth and to classify impacted third molars according to the Winter method with an artificial intelligence model on panoramic radiographs.

METHODS

In this study, 1197 panoramic radiographs from the dentistry faculty database were collected for all impacted teeth, and 1000 panoramic radiographs were collected for Winter classification. Some pre-processing methods were performed and the images were doubled with data augmentation. Both datasets were randomly divided into 80% training, 10% validation, and 10% testing. After transfer learning and fine-tuning processes, the two datasets were trained with the YOLOv8 deep learning algorithm, a high-performance artificial intelligence model, and the detection of impacted teeth was carried out. The results were evaluated with precision, recall, mAP, and F1-score performance metrics. A graphical user interface was designed for clinical use with the artificial intelligence weights obtained as a result of the training.

RESULTS

For the detection of impacted third molar teeth according to Winter classification, the average precision, average recall, and average F1 score were obtained to be 0.972, 0.967, and 0.969, respectively. For the detection of all impacted teeth, the average precision, average recall, and average F1 score were obtained as 0.991, 0.995, and 0.993, respectively.

CONCLUSION

According to the results, the artificial intelligence-based YOLOv8 deep learning model successfully detected all impacted teeth and the impacted third molar teeth according to the Winter classification system.

Artificial intelligence in dentistry: A bibliometric analysis from 2000 to 2023

Background/purpose

Artificial intelligence (AI) is reshaping clinical practice in dentistry. This study aims to provide a comprehensive overview of global trends and research hotspots on the application of AI to dentistry.

Materials and methods

Studies on AI in dentistry published between 2000 and 2023 were retrieved from the Web of Science Core Collection. Bibliometric parameters were extracted and bibliometric analysis was conducted using VOSviewer, Pajek, and CiteSpace software.

Results

A total of 651 publications were identified, 88.7 % of which were published after 2019. Publications originating from the United States and China accounted for 34.5 % of the total. The Charité Medical University of Berlin was the institution with the highest number of publications, and Schwendicke and Krois were the most active authors in the field. The Journal of Dentistry had the highest citation count. The focus of AI in dentistry primarily centered on the analysis of imaging data and the dental diseases most frequently associated with AI were periodontitis, bone fractures, and dental caries. The dental AI applications most frequently discussed since 2019 included neural networks, medical devices, clinical decision support systems, head and neck cancer, support vector machine, geometric deep learning, and precision medicine.

Conclusion

Research on AI in dentistry is experiencing explosive growth. The prevailing research emphasis and anticipated future development involve the establishment of medical devices and clinical decision support systems based on innovative AI algorithms to advance precision dentistry. This study provides dentists with valuable insights into this field.

Accuracy of machine learning in the diagnosis of odontogenic cysts and tumors: a systematic review and meta-analysis

BACKGROUND

The recent impact of artificial intelligence in diagnostic services has been enormous. Machine learning tools offer an innovative alternative to diagnose cysts and tumors radiographically that pose certain challenges due to the near similar presentation, anatomical variations, and superimposition. It is crucial that the performance of these models is evaluated for their clinical applicability in diagnosing cysts and tumors.

METHODS

A comprehensive literature search was carried out on eminent databases for published studies between January 2015 and December 2022. Studies utilizing machine learning models in the diagnosis of odontogenic cysts or tumors using Orthopantomograms (OPG) or Cone Beam Computed Tomographic images (CBCT) were included. QUADAS-2 tool was used for the assessment of the risk of bias and applicability concerns. Meta-analysis was performed for studies reporting sufficient performance metrics, separately for OPG and CBCT.

RESULTS

16 studies were included for qualitative synthesis including a total of 10,872 odontogenic cysts and tumors. The sensitivity and specificity of machine learning in diagnosing cysts and tumors through OPG were 0.83 (95% CI 0.81-0.85) and 0.82 (95% CI 0.81-0.83) respectively. Studies utilizing CBCT noted a sensitivity of 0.88 (95% CI 0.87-0.88) and specificity of 0.88 (95% CI 0.87-0.89). Highest classification accuracy was 100%, noted for Support Vector Machine classifier.

CONCLUSION

The results from the present review favoured machine learning models to be used as a clinical adjunct in the radiographic diagnosis of odontogenic cysts and tumors, provided they undergo robust training with a huge dataset. However, the arduous process, investment, and certain ethical concerns associated with the total dependence on technology must be taken into account. Standardized reporting of outcomes for diagnostic studies utilizing machine learning methods is recommended to ensure homogeneity in assessment criteria, facilitate comparison between different studies, and promote transparency in research findings.

Artificial Intelligence in Periodontics: A Comprehensive Review

Periodontal diseases are prevalent worldwide and pose a significant public health burden. With the advent of artificial intelligence (AI), there has been growing interest in leveraging AI technologies to improve diagnosis, treatment planning, and management of periodontal conditions. This review aims to provide a comprehensive overview of the applications of AI in periodontics, including its potential benefits, challenges, and future directions. Fifteen relevant studies were analyzed to explore the role of AI in periodontal disease detection, risk assessment, treatment planning, and patient management. The findings highlight the promising role of AI in enhancing the accuracy, efficiency, and personalized care delivery in periodontics.

Evaluation of deep learning for detecting intraosseous jaw lesions in cone beam computed tomography volumes

OBJECTIVE

The study aim was to develop and assess the performance of a deep learning (DL) algorithm in the detection of radiolucent intraosseous jaw lesions in cone beam computed tomography (CBCT) volumes.

STUDY DESIGN

A total of 290 CBCT volumes from more than 12 different scanners were acquired. Fields of view ranged from 6 × 6 × 6 cm to 18 × 18 × 16 cm. CBCT volumes contained either zero or at least one biopsy-confirmed intraosseous lesion. 80 volumes with no intraosseous lesions were included as controls and were not annotated. 210 volumes with intraosseous lesions were manually annotated using ITK-Snap 3.8.0. 150 volumes (10 control, 140 positive) were presented to the DL software for training. Validation was performed using 60 volumes (30 control, 30 positive). Testing was performed using the remaining 80 volumes (40 control, 40 positive).

RESULTS

The DL algorithm obtained an adjusted sensitivity by case, specificity by case, positive predictive value by case, and negative predictive value by case of 0.975, 0.825, 0.848, and 0.971, respectively.

CONCLUSIONS

A DL algorithm showed moderate success at lesion detection in their correct locations, as well as recognition of lesion shape and extent. This study demonstrated the potential of DL methods for intraosseous lesion detection in CBCT volumes.

Automatic detection and classification of nasopalatine duct cyst and periapical cyst on panoramic radiographs using deep convolutional neural networks

OBJECTIVE

The aim of this study was to evaluate a deep convolutional neural network (DCNN) method for the detection and classification of nasopalatine duct cysts (NPDC) and periapical cysts (PAC) on panoramic radiographs.

STUDY DESIGN

A total of 1,209 panoramic radiographs with 606 NPDC and 603 PAC were labeled with a bounding box and divided into training, validation, and test sets with an 8:1:1 ratio. The networks used were EfficientDet-D3, Faster R-CNN, YOLO v5, RetinaNet, and SSD. Mean average precision (mAP) was used to assess performance. Sixty images with no lesion in the anterior maxilla were added to the previous test set and were tested on 2 dentists with no training in radiology (GP) and on EfficientDet-D3. The performances were comparatively examined.

RESULTS

The mAP for each DCNN was EfficientDet-D3 93.8%, Faster R-CNN 90.8%, YOLO v5 89.5%, RetinaNet 79.4%, and SSD 60.9%. The classification performance of EfficientDet-D3 was higher than that of the GPs' with accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 94.4%, 94.4%, 97.2%, 94.6%, and 97.2%, respectively.

CONCLUSIONS

The proposed method achieved high performance for the detection and classification of NPDC and PAC compared with the GPs and presented promising prospects for clinical application.

Oral Health Status and Treatment Needs Based on Artificial Intelligence (AI) Dental Panoramic Radiograph (DPR) Analysis: A Cross-Sectional Study

Background: The application of artificial intelligence (AI) is gaining popularity in modern dentistry. AI has been successfully used to interpret dental panoramic radiographs (DPRs) and quickly screen large groups of patients. This cross-sectional study aimed to perform a population-based assessment of the oral health status and treatment needs of the residents of Kielce, Poland, and the surrounding area based on DPR analysis performed by a high-accuracy AI algorithm trained with over 250,000 radiographs. Methods: This study included adults who had a panoramic radiograph performed, regardless of indications. The following diagnoses were used for analysis: (1) dental caries, (2) missing tooth, (3) dental filling, (4) root canal filling, (5) endodontic lesion, (6) implant, (7) implant abutment crown, (8) pontic crown, (9) dental abutment crown, and (10) sound tooth. The study sample included 980 subjects. Results: The patients had an average of 15 sound teeth, with the domination of the lower dental arch over the upper one. The most commonly identified pathology was dental caries, which affected 99% of participants. A total of 67% of patients underwent root canal treatment. Every fifth endodontically treated tooth presented a periapical lesion. Of study group members, 82% lost at least one tooth. Pontics were identified more often (9%) than implants (2%) in replacing missing teeth. Conclusions: DPR assessment by AI has proven to be an efficient method for population analysis. Despite recent improvements in the oral health status of Polish residents, its level is still unsatisfactory and suggests the need to improve oral health. However, due to some limitations of this study, the results should be interpreted with caution.

Radiographic diagnosis of periodontal diseases - Current evidence versus innovations

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

Artificial intelligence and personalized diagnostics in periodontology: A narrative review

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

AI model to detect contact relationship between maxillary sinus and posterior teeth

Objectives

To establish a novel deep learning networks (MSF-MPTnet) based on panoramic radiographs (PRs) for automatic assessment of relationship between maxillary sinus floor (MSF) and maxillary posterior teeth (MPT), and to compare accuracy of MSF-MPTnet, dentists and radiologists identifying contact relationship.

Study design

A total of 1035 PRs and 1035 Cone-beam computed tomographys （CBCT）images were collected from January 2018 to April 2022. The relationships were classified into class I and II by CBCT. Class I represents non-contact group, and class II represents contact group. 350 PRs were randomly selected as test dataset and accuracy of MSF-MPTnet, dentists, and radiologists was compared.

Results

The intraclass correlation coefficient of dentists was 0.460-0.690 and it was 0.453-0.664 for radiologists. Sensitivity and accuracy of MSF-MPTnet were 0.682-0.852and 0.890-0.951, indicating that the output performance of MSF-MPTnet was reliable. Accuracy of maxillary premolars and molars were 79.7%-90.3 %, 76.2%-89.2 % and 72.9%-88.3 % in MSF-MPTnet model, dentists and radiologists. Accuracy of class I relationship in the MSF-MPTnet model (67.7%-94.6 %) was higher than that of dentists (56.5%-84.6 %) in maxillary first premolars and right second premolar, and accuracy of class I relationship in the MSF-MPTnet model is also higher than radiologists (40.0%-78.1 %) in all teeth positions (p < 0.05).

Conclusions

MSF-MPTnet model could increase detecting accuracy of the relationship between MSF and MPT, minimize pseudo contact relationship and reduce frequency of CBCT use.

Periodontal grading-estimation of responsiveness to therapy and progression of disease

OBJECTIVE

To investigate the capability of periodontal grading to estimate the progression of periodontal disease and the responsiveness to therapy.

MATERIALS AND METHODS

Eighty-four patients who underwent non-surgical therapy (NST) were included. Direct and indirect evidence of progression were determined according to the current classification. Responsiveness to therapy was examined using mean pocket probing depths reduction (PPDRed), reduction of bleeding on probing (BOPRed), and the rate of pocket closure (%PC) after six months.

RESULTS

Statistical analysis revealed no agreement between direct and indirect evidence in grading periodontitis (κ = 0.070). The actual rate of progression as determined by longitudinal data was underestimated in 13% (n = 11), overestimated in 51% (n = 43) and correctly estimated in 30% (n = 36) by indirect evidence. No significant differences in responsiveness to therapy were observed in patients graded according to direct evidence. Using indirect evidence, patients assigned grade C showed more PPDRed but less BOPRed and lower %PC compared to grade B.

CONCLUSION

The present data indicate that indirect evidence may lead to inaccuracies compared to direct evidence regarding the estimation of periodontal progression. However, indirect evidence seems to be more suitable in the estimation of responsiveness to therapy than direct evidence, helping to identify cases that are more likely to require additional therapies such as re-instrumentation or periodontal surgery.

CLINICAL RELEVANCE

Regarding the estimation of disease progression and responsiveness to periodontal therapy, accuracy and reliability of both direct and indirect evidence are limited when grading periodontitis.

Classification of Periapical and Bitewing Radiographs as Periodontally Healthy or Diseased by Deep Learning Algorithms

Objectives The aim of this artificial intelligence (AI) study was to develop a deep learning algorithm capable of automatically classifying periapical and bitewing radiography images as either periodontally healthy or unhealthy and to assess the algorithm's diagnostic success. Materials and methods The sample of the study consisted of 1120 periapical radiographs (560 periodontally healthy, 560 periodontally unhealthy) and 1498 bitewing radiographs (749 periodontally healthy, 749 periodontally ill). From the main datasets of both radiography types, three sub-datasets were randomly created: a training set (80%), a validation set (10%), and a test set (10%). Using these sub-datasets, a deep learning algorithm was developed with the YOLOv8-cls model (Ultralytics, Los Angeles, California, United States) and trained over 300 epochs. The success of the developed algorithm was evaluated using the confusion matrix method. Results The AI algorithm achieved classification accuracies of 75% or higher for both radiograph types. For bitewing radiographs, the sensitivity, specificity, precision, accuracy, and F1 score values were 0.8243, 0.7162, 0.7439, 0.7703, and 0.7821, respectively. For periapical radiographs, the sensitivity, specificity, precision, accuracy, and F1 score were 0.7500, 0.7500, 0.7500, 0.7500, and 0.7500, respectively. Conclusion The AI models developed in this study demonstrated considerable success in classifying periodontal disease. Future applications may involve employing AI algorithms for assessing periodontal status across various types of radiography images and for automated disease detection.

Perceptiveness and Attitude on the use of Artificial Intelligence (AI) in Dentistry among Dentists and Non-Dentists - A Regional Survey

Artificial intelligence (AI) is an emerging tool in modern medicine and the digital world. AI can help dentists diagnose oral diseases, design treatment plans, monitor patient progress and automate administrative tasks. The aim of this study is to evaluate the perception and attitude on use of artificial intelligence in dentistry for diagnosis and treatment planning among dentists and non-dentists' population of south Tamil Nadu region in India.

Materials and Methods

A cross sectional online survey conducted using 20 close ended questionnaire google forms which were circulated among the dentists and non -dentists population of south Tamil Nadu region in India. The data collected from 264 participants (dentists -158, non-dentists -106) within a limited time frame were subjected to descriptive statistical analysis.

Results

70.9% of dentists are aware of artificial intelligence in dentistry. 40.5% participants were not aware of AI in caries detection but aware of its use in interpretation of radiographs (43.9%) and in planning of orthognathic surgery (42.4%) which are statistically significant P < 0.05.44.7% support clinical experience of a human doctor better than AI diagnosis. Dentists of 54.4% agree to support AI use in dentistry.

Conclusion

The study concluded AI use in dentistry knowledge is more with dentists and perception of AI in dentistry is optimistic among dentists than non -dentists, majority of participants support AI in dentistry as an adjunct tool to diagnosis and treatment planning.

Establishing a novel deep learning model for detecting peri-implantiti

BACKGROUND/PURPOSE

The diagnosis of peri-implantitis using periapical radiographs is crucial. Recently, artificial intelligence may apply in radiographic image analysis effectively. The aim of this study was to differentiate the degree of marginal bone loss of an implant, and also to classify the severity of peri-implantitis using a deep learning model.

MATERIALS AND METHODS

A dataset of 800 periapical radiographic images were divided into training (n = 600), validation (n = 100), and test (n = 100) datasets with implants used for deep learning. An object detection algorithm (YOLOv7) was used to identify peri-implantitis. The classification performance of this model was evaluated using metrics, including the specificity, precision, recall, and F1 score.

RESULTS

Considering the classification performance, the specificity was 100%, precision was 100%, recall was 94.44%, and F1 score was 97.10%.

CONCLUSION

Results of this study suggested that implants can be identified from periapical radiographic images using deep learning-based object detection. This identification system could help dentists and patients suffering from implant problems. However, more images of other implant systems are needed to increase the learning performance to apply this system in clinical practice.

Dental radiographic changes in individuals with COVID-19: a controlled retrospective study

OBJECTIVE

The aim of this study is to compare the pre-COVID-19 and post-COVID-19 dental radiological findings of individuals with positive rRT-PCR test results and with healthy controls using the apical periodontitis grade scale (APGS), radiographic-based periodontal bone loss (R-PBL), and radiographic DMFT indices, and to investigate the relatively long-term dental effects of COVID-19.

METHODS

This study included people who had two panoramic radiographs taken between 2018 and 2022. There are 52 patients with positive rRT-PCR tests in the study group. The control group included 50 individuals. Study and control groups were compared using the apical periodontitis grade scale (APGS), radiographic-based periodontal bone loss (R-PBL), and radiographic DMFT indices.

RESULTS

Although results showed a significant difference in percentage R-PBL value and R-PBL types in the study group, there was no significant difference in percentage R-PBL value and R-PBL types in the control group. Also, both groups showed a significant difference in the DMFT index.

CONCLUSIONS

According to the results of this study, it can be said that COVID-19 increases the incidence of periodontitis, and it can be interpreted that the pandemic may adversely affect the general oral health of all people.

Panoramic imaging errors in machine learning model development: a systematic review

OBJECTIVES

To investigate the management of imaging errors from panoramic radiography (PAN) datasets used in the development of machine learning (ML) models.

METHODS

This systematic literature followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and used three databases. Keywords were selected from relevant literature.

ELIGIBILITY CRITERIA

PAN studies that used ML models and mentioned image quality concerns.

RESULTS

Out of 400 articles, 41 papers satisfied the inclusion criteria. All the studies used ML models, with 35 papers using deep learning (DL) models. PAN quality assessment was approached in 3 ways: acknowledgement and acceptance of imaging errors in the ML model, removal of low-quality radiographs from the dataset before building the model, and application of image enhancement methods prior to model development. The criteria for determining PAN image quality varied widely across studies and were prone to bias.

CONCLUSIONS

This study revealed significant inconsistencies in the management of PAN imaging errors in ML research. However, most studies agree that such errors are detrimental when building ML models. More research is needed to understand the impact of low-quality inputs on model performance. Prospective studies may streamline image quality assessment by leveraging DL models, which excel at pattern recognition tasks.

Comparison between observer-based and AI-based reading of CBCT datasets: An interrater-reliability study

Objective

To assess the performance of human observers and convolutional neural networks (CNNs) in detecting periodontal lesions in cone beam computed tomography (CBCT), a total of 38 datasets were examined. Three human readers and a CNN-based solution were employed to evaluate the presence of periodontal pathologies in these datasets.

Materials and Methods

Datasets were acquired with a Veraview X800 L P (JMorita Mfg. Corp., Kyoto, Japan). Three general dentists, previously calibrated by a general principal investigator, read the datasets in 3D MPR mode using Horos(LGPL license at Horosproject.org and sponsored by Nimble Co LLC d/b/a Purview in Annapolis, MD, USA) as a DICOM reader. All pathological changes including vertical bone loss, furcation involvement, and periradicular osteolysis were detected. Furthermore, the same datasets were analyzed automatically by Diagnocat (Diagnocat LLC, Prague, Czech Republic), a deep CNN. Finally, the performance of the dentists and the CNN were compared and evaluated.

Results

The CNN's performance was significantly lower compared to the human readers in the search for different types of lesions. The human observers achieved good to very good interobserver agreement, except for the evaluation of the vertical lesions, which resulted in a moderate agreement.

Conclusion

The CNN used in this study was found to be ineffective in identifying periodontal lesions and was not adequately trained to offer significant assistance in the automated evaluation of periodontal lesions in CBCT datasets.

Detection of periodontal bone loss patterns and furcation defects from panoramic radiographs using deep learning algorithm: a retrospective study

BACKGROUND

This retrospective study aimed to develop a deep learning algorithm for the interpretation of panoramic radiographs and to examine the performance of this algorithm in the detection of periodontal bone losses and bone loss patterns.

METHODS

A total of 1121 panoramic radiographs were used in this study. Bone losses in the maxilla and mandibula (total alveolar bone loss) (n = 2251), interdental bone losses (n = 25303), and furcation defects (n = 2815) were labeled using the segmentation method. In addition, interdental bone losses were divided into horizontal (n = 21839) and vertical (n = 3464) bone losses according to the defect patterns. A Convolutional Neural Network (CNN)-based artificial intelligence (AI) system was developed using U-Net architecture. The performance of the deep learning algorithm was statistically evaluated by the confusion matrix and ROC curve analysis.

RESULTS

The system showed the highest diagnostic performance in the detection of total alveolar bone losses (AUC = 0.951) and the lowest in the detection of vertical bone losses (AUC = 0.733). The sensitivity, precision, F1 score, accuracy, and AUC values were found as 1, 0.995, 0.997, 0.994, 0.951 for total alveolar bone loss; found as 0.947, 0.939, 0.943, 0.892, 0.910 for horizontal bone losses; found as 0.558, 0.846, 0.673, 0.506, 0.733 for vertical bone losses and found as 0.892, 0.933, 0.912, 0.837, 0.868 for furcation defects (respectively).

CONCLUSIONS

AI systems offer promising results in determining periodontal bone loss patterns and furcation defects from dental radiographs. This suggests that CNN algorithms can also be used to provide more detailed information such as automatic determination of periodontal disease severity and treatment planning in various dental radiographs.