A two-stage deep learning architecture for radiographic staging of periodontal bone loss

Evaluating Factors Influencing Periodontal Bone Loss Using Cone Beam Computed Tomography: A Retrospective Study

BACKGROUND The assessment of alveolar bone loss and determining patterns of disease progression with respect to different etiologic or contributing factors plays a vital role in the diagnosis of periodontitis, prognosis of the disease, and better treatment planning. This study aimed to determine periodontal bone loss using cone beam computed tomography images obtained from various age groups and evaluate the effects of age, sex, jaw type, tooth type, and tooth surface width on periodontal destruction. MATERIAL AND METHODS In total, 200 cone beam computed tomography images obtained for any indication were randomly selected and analyzed. The distance between the alveolar crest and cemento-enamel junction was measured, and values exceeding 2 mm were considered as bone loss. Furthermore, the buccolingual and mesiodistal widths of all teeth at the cemento-enamel junction were measured to determine tooth surface width. RESULTS Among the patients included in the study, bone loss increased with age. The highest bone loss was observed in the maxillary molars, followed by the mandibular incisors. Although there was no significant difference in mean bone loss values between the jaws, distal surfaces in the maxilla showed greater bone loss than that in the mandible. Furthermore, the relationship between tooth surface width at the cemento-enamel junction and bone loss varied by tooth type. In mandibular incisors and premolars, bone loss increased as the buccolingual and mesiodistal widths decreased. CONCLUSIONS These findings indicate that periodontal bone loss is influenced by age, sex, tooth type, and tooth surface width.

Artificial intelligence models for periodontitis classification: A systematic review

OBJECTIVES

The graded diagnosis of periodontitis has always been a difficulty for dentists. This systematic review aimed to investigate the performance of artificial intelligence (AI) models for periodontitis classification.

DATA

This review includes original studies that explore the application of AI in periodontitis classification systems.

SOURCES

Two reviewers independently conducted a comprehensive search of literature published up to April 2024 in databases including PubMed, Web of Science, MEDLINE, Scopus, and Cochrane Library.

STUDY SELECTION

A total of 28 articles were eventually included in this study, from which 10 mapping parameters were extracted and evaluated separately for each article.

RESULTS

AI's diagnostic capabilities are comparable to those of a general dentist/periodontist, achieving an overall diagnostic accuracy rate of over 70 % for periodontitis classification, with some reaching 80-90 %. Variations in diagnosis accuracy rates were observed across different stages of periodontitis.

CONCLUSIONS

The AI model provides a novel and relatively reliable method for periodontitis classification. However, several key issues remain to be addressed, including access to and quality of data, interpretation of the decision-making process of the model, the ability of the model to generalize, and ethical and privacy considerations.

CLINICAL SIGNIFICANCE

The development of AI models for periodontitis classification is expected to assist dentists in improving diagnostic efficiency and enhancing diagnostic accuracy, and further development is expected to assist telemedicine and home self-testing.

Evaluation of an artificial intelligence-based model in diagnosing periodontal radiographic bone loss

OBJECTIVE

To develop an artificial intelligence model based on convolutional neural network for detecting and measuring periodontal radiographic bone loss (RBL).

MATERIALS AND METHODS

Keypoint annotations were carried out in 595 digital bitewing radiographic images using a Computer Vision Annotation Tool. The dataset was splitted: 416 of these images were trained using the You Only Look Once version 8 architecture with pose estimation (YOLO-v8-pose), 119 images were destined for the validation set, and 60 images were used for the test set, resulting in a model capable of detecting keypoints related to the cementoenamel junction (CEJ) and alveolar bone crest (ABC). In order to evaluate the performance of the obtained model, the following metrics were analyzed: F1-Score, precision, sensitivity and mean average precision (mAP). Then, an algorithm was implemented to measure the RBL by calculating the Euclidean distance between CEJ and ABC.

RESULTS

The model achieved an F1-Score of 66,89%, precision of 61,1%, a sensitivity of 73,9% and an mAP of 73.8%.

CONCLUSIONS

The developed model and its algorithm for identifying and measuring periodontal radiographic bone loss demonstrated promising performance, thereby presenting a potential tool for assisting in periodontal diagnosis. Further studies comparing the developed model with manual measurements performed by specialists are necessary for its validation.

CLINICAL RELEVANCE

Applying artificial intelligence in clinical dental practice can support diagnosis, streamline clinical workflows, and inform treatment planning, representing a significant advancement in dental automation.

Development of a Machine Learning Tool for Home-Based Assessment of Periodontitis

According to an ADA report, approximately 15% of the US population requires dental care annually but does not receive it. Access to dental care, particularly for periodontal examinations, is challenging for many individuals, leading to uncontrolled periodontitis progression and systemic health complications. Periodontitis, an inflammatory gum disease, affects nearly half of American adults over 30. Current diagnostic approaches rely on periodontal exams and radiographs, requiring clinical settings and experienced dental care providers. However, many individuals lack access to dental care, making it difficult to obtain up-to-date clinical probing depth, dental X-rays or CT scans. To address this gap, we developed a machine learning (ML) tool for at-home preliminary periodontitis assessments. This tool would benefit individuals unaware of their undiagnosed periodontal conditions and those with limited access to dental care, empowering them to prioritize dental care and seek timely treatment within their constraints. Our tool leverages the NHANES database to train an ML model on multimodal features relevant to periodontitis that are radiographic-independent. We labeled the individuals with different periodontitis severity based on their periodontal charting records and performed feature engineering on the dataset. We first developed a baseline model and subsequently trained additional classifiers, conducting a comprehensive hyperparameter search that resulted in consistent performance. The best-performing model was evaluated on the test set, achieving an overall precision of 0.80 and AUC of 0.81, demonstrating robust classification performance without overfitting. Feature importance analysis provided guidance for the questionnaire design for the real-world application of this tool. Additionally, our novel approach of analyzing misclassified populations offered insights for data interpretation, supported model improvement, and revealed deeper correlations between periodontitis and its risk factors. Our model exemplifies the capacity to leverage extensive public health databases for periodontitis evaluations. Ultimately, our ML-driven tool aims to overcome existing dental care barriers by providing users with periodontitis predictions and personalized dental care suggestions, all easily accessible from their smartphones or laptops at home.

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.

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.

Hybrid CNN-Transformer Model for Accurate Impacted Tooth Detection in Panoramic Radiographs

Background/Objectives: The integration of digital imaging technologies in dentistry has revolutionized diagnostic and treatment practices, with panoramic radiographs playing a crucial role in detecting impacted teeth. Manual interpretation of these images is time consuming and error prone, highlighting the need for automated, accurate solutions. This study proposes an artificial intelligence (AI)-based model for detecting impacted teeth in panoramic radiographs, aiming to enhance accuracy and reliability. Methods: The proposed model combines YOLO (You Only Look Once) and RT-DETR (Real-Time Detection Transformer) models to leverage their strengths in real-time object detection and learning long-range dependencies, respectively. The integration is further optimized with the Weighted Boxes Fusion (WBF) algorithm, where WBF parameters are tuned using Bayesian optimization. A dataset of 407 labeled panoramic radiographs was used to evaluate the model's performance. Results: The model achieved a mean average precision (mAP) of 98.3% and an F1 score of 96%, significantly outperforming individual models and other combinations. The results were expressed through key performance metrics, such as mAP and F1 scores, which highlight the model's balance between precision and recall. Visual and numerical analyses demonstrated superior performance, with enhanced sensitivity and minimized false positive rates. Conclusions: This study presents a scalable and reliable AI-based solution for detecting impacted teeth in panoramic radiographs, offering substantial improvements in diagnostic accuracy and efficiency. The proposed model has potential for widespread application in clinical dentistry, reducing manual workload and error rates. Future research will focus on expanding the dataset and further refining the model's generalizability.

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.

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.

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.

Automating bone loss measurement on periapical radiographs for predicting the periodontitis stage and grade

Background

The aim of this study was to develop and evaluate an automated approach for segmenting bone loss (BL) on periapical (PA) radiographs and predicting the stage and grade of periodontitis.

Methods

One thousand PA radiographs obtained from 572 patients were utilized for training while a separate set of 1,582 images from 210 patients were used for testing. BL was segmented using a U-Net model, which was trained with augmented datasets to enhance generalizability. Apex detection was performed using YOLO-v9, focusing on identifying apexes of teeth to measure root length. Root length was calculated as the distance between the coordinates of detected apexes and center of cemento-enamel junction (CEJ), which was segmented utilizing a U-Net algorithm. BL percentage (ratio of BL to the root length) was used to predict the stage and grade of periodontitis. Evaluation metrics including accuracy, precision, recall, F1-score, Intersection over Union (IoU), mean absolute error (MAE), intraclass correlation coefficients (ICC), and root mean square error (RMSE) were used to evaluate the models' performance.

Results

The U-Net model achieved high accuracy in segmenting BL with 94.9%, 92.9%, and 95.62% on training, validation, and test datasets, respectively. The YOLO-v9 model exhibited a mean Average Precision (mAP) of 66.7% for apex detection, with a precision of 79.6% and recall of 62.4%. The BL percentage calculated from the segmented images and detected apexes demonstrated excellent agreement with clinical assessments, with ICC exceeding 0.94. Stage and grade prediction for periodontitis showed robust performance specifically for advanced stages (III/IV) and grades (C) with an F1-score of 0.945 and 0.83, respectively.

Conclusion

The integration of U-Net and YOLO-v9 models for BL segmentation and apex detection on PA radiographs proved effective in enhancing the accuracy and reliability of periodontitis diagnosis and grading.

Unsupervised few shot learning architecture for diagnosis of periodontal disease in dental panoramic radiographs

In the domain of medical imaging, the advent of deep learning has marked a significant progression, particularly in the nuanced area of periodontal disease diagnosis. This study specifically targets the prevalent issue of scarce labeled data in medical imaging. We introduce a novel unsupervised few-shot learning algorithm, meticulously crafted for classifying periodontal diseases using a limited collection of dental panoramic radiographs. Our method leverages UNet architecture for generating regions of interest (RoI) from radiographs, which are then processed through a Convolutional Variational Autoencoder (CVAE). This approach is pivotal in extracting critical latent features, subsequently clustered using an advanced algorithm. This clustering is key in our methodology, enabling the assignment of labels to images indicative of periodontal diseases, thus circumventing the challenges posed by limited datasets. Our validation process, involving a comparative analysis with traditional supervised learning and standard autoencoder-based clustering, demonstrates a marked improvement in both diagnostic accuracy and efficiency. For three real-world validation datasets, our UNet-CVAE architecture achieved up to average 14% higher accuracy compared to state-of-the-art supervised models including the vision transformer model when trained with 100 labeled images. This study not only highlights the capability of unsupervised learning in overcoming data limitations but also sets a new benchmark for diagnostic methodologies in medical AI, potentially transforming practices in data-constrained scenarios.

Deep Learning-Based Detection of Impacted Teeth on Panoramic Radiographs

Objective

The aim is to detect impacted teeth in panoramic radiology by refining the pretrained MedSAM model.

Study design

Impacted teeth are dental issues that can cause complications and are diagnosed via radiographs. We modified SAM model for individual tooth segmentation using 1016 X-ray images. The dataset was split into training, validation, and testing sets, with a ratio of 16:3:1. We enhanced the SAM model to automatically detect impacted teeth by focusing on the tooth's center for more accurate results.

Results

With 200 epochs, batch size equals to 1, and a learning rate of 0.001, random images trained the model. Results on the test set showcased performance up to an accuracy of 86.73%, F1-score of 0.5350, and IoU of 0.3652 on SAM-related models.

Conclusion

This study fine-tunes MedSAM for impacted tooth segmentation in X-ray images, aiding dental diagnoses. Further improvements on model accuracy and selection are essential for enhancing dental practitioners' diagnostic capabilities.

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.

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.

Development and characterization of novel magnesium oxide nanoparticle-impregnated chitosan-based guided tissue regeneration membrane - An in vitro study

Introduction

Although a lot of commercially available guided tissue regeneration (GTR) membranes are used, none of them could actually ensure complete bone regeneration so far and they also have certain limitations. This study aims to explore further and develop a membrane that might overcome these limitations and aid in bone regeneration for the treatment of bony defects.

Materials and Methods

Magnesium oxide nanoparticles (MgONPs) were prepared from magnesium nitrate prepared by calcination at different temperatures and dried using filter paper under specific temperature. Later, 0.3 ml of 0.2 M 1% acetic acid was added to water and placed in the stirrer for at least 1 h. Chitosan (CS) (2%) of two different concentrations containing 0.588 g and 0.576 g of CS, respectively, was prepared and added to the previous mixture. To these concentrations, the prepared MgONPs were added and stirred using a magnetic stirrer for 1 h. Later, it was cast in the mold and dried. The prepared membrane was immersed in 1 M sodium hydroxide to neutralize acetic acid. After preparation, they were subjected to scanning electron microscope (SEM) analysis, energy-dispersive X-ray (EDAX), Fourier transform infrared spectroscopy (FTIR), and contact angle test.

Results

In SEM analysis, spherical in size, uniformly dense, and porous agglomeration was noticed. EDAX and FTIR revealed the formation of MgONPs (magnesium oxide) in the membrane. The average contact angles of the CS with MgONPs and control membranes were 85.48° and 80.80°, respectively. Degradation analysis showed that test membrane showed a slower degradation rate than control collagen membrane at the end of the 28th day.

Conclusion

On comparing membranes with pure CS, membranes with nanoparticles, and control collagen membranes, the membrane incorporated with nanoparticles showed more favorable positive outcomes.

Performance of ChatGPT in classifying periodontitis according to the 2018 classification of periodontal diseases

OBJECTIVES

This study assessed the ability of ChatGPT, an artificial intelligence(AI) language model, to determine the stage, grade, and extent of periodontitis based on the 2018 classification.

MATERIALS AND METHODS

This study used baseline digital data of 200 untreated periodontitis patients to compare standardized reference diagnoses (RDs) with ChatGPT findings and determine the best criteria for assessing stage and grade. RDs were provided by four experts who examined each case. Standardized texts containing the relevant information for each situation were constructed to query ChatGPT. RDs were compared to ChatGPT's responses. Variables influencing the responses of ChatGPT were evaluated.

RESULTS

ChatGPT successfully identified the periodontitis stage, grade, and extent in 59.5%, 50.5%, and 84.0% of cases, respectively. Cohen's kappa values for stage, grade and extent were respectively 0.447, 0.284, and 0.652. A multiple correspondence analysis showed high variance between ChatGPT's staging and the variables affecting the stage (64.08%) and low variance between ChatGPT's grading and the variables affecting the grade (42.71%).

CONCLUSIONS

The present performance of ChatGPT in the classification of periodontitis exhibited a reasonable level. However, it is expected that additional improvements would increase its effectiveness and broaden its range of functionalities (NCT05926999).

CLINICAL RELEVANCE

Despite ChatGPT's current limitations in accurately classifying periodontitis, it is important to note that the model has not been specifically trained for this task. However, it is expected that with additional improvements, the effectiveness and capabilities of ChatGPT might be enhanced.

DMAF-Net: deformable multi-scale adaptive fusion network for dental structure detection with panoramic radiographs

OBJECTIVES

Panoramic radiography is one of the most commonly used diagnostic modalities in dentistry. Automatic recognition of panoramic radiography helps dentists in decision support. In order to improve the accuracy of the detection of dental structural problems in panoramic radiographs, we have improved the You Only Look Once (YOLO) network and verified the feasibility of this new method in aiding the detection of dental problems.

METHODS

We propose a Deformable Multi-scale Adaptive Fusion Net (DMAF-Net) to detect 5 types of dental situations (impacted teeth, missing teeth, implants, crown restorations, and root canal-treated teeth) in panoramic radiography by improving the YOLO network. In DMAF-Net, we propose different modules to enhance the feature extraction capability of the network as well as to acquire high-level features at different scales, while using adaptively spatial feature fusion to solve the problem of scale mismatches of different feature layers, which effectively improves the detection performance. In order to evaluate the detection performance of the models, we compare the experimental results of different models in the test set and select the optimal results of the models by calculating the average of different metrics in each category as the evaluation criteria.

RESULTS

About 1474 panoramic radiographs were divided into training, validation, and test sets in the ratio of 7:2:1. In the test set, the average precision and recall of DMAF-Net are 92.7% and 87.6%, respectively; the mean Average Precision (mAP0.5 and mAP[0.5:0.95]) are 91.8% and 63.7%, respectively.

CONCLUSIONS

The proposed DMAF-Net model improves existing deep learning models and achieves automatic detection of tooth structure problems in panoramic radiographs. This new method has great potential for new computer-aided diagnostic, teaching, and clinical applications in the future.

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.

Surveying the landscape of diagnostic imaging in dentistry's future: Four emerging technologies with promise

BACKGROUND

Advances in digital radiography for both intraoral and panoramic imaging and cone-beam computed tomography have led the way to an increase in diagnostic capabilities for the dental care profession. In this article, the authors provide information on 4 emerging technologies with promise.

TYPES OF STUDIES REVIEWED

The authors feature the following: artificial intelligence in the form of deep learning using convolutional neural networks, dental magnetic resonance imaging, stationary intraoral tomosynthesis, and second-generation cone-beam computed tomography sources based on carbon nanotube technology and multispectral imaging. The authors review and summarize articles featuring these technologies.

RESULTS

The history and background of these emerging technologies are previewed along with their development and potential impact on the practice of dental diagnostic imaging. The authors conclude that these emerging technologies have the potential to have a substantial influence on the practice of dentistry as these systems mature. The degree of influence most likely will vary, with artificial intelligence being the most influential of the 4.

CONCLUSIONS AND PRACTICAL IMPLICATIONS

The readers are informed about these emerging technologies and the potential effects on their practice going forward, giving them information on which to base decisions on adopting 1 or more of these technologies. The 4 technologies reviewed in this article have the potential to improve imaging diagnostics in dentistry thereby leading to better patient care and heightened professional satisfaction.

Performance evaluation of three versions of a convolutional neural network for object detection and segmentation using a multiclass and reduced panoramic radiograph dataset

OBJECTIVES

To evaluate the diagnostic performance of three versions of a deep-learning convolutional neural network in terms of object detection and segmentation using a multiclass panoramic radiograph dataset.

METHODS

A total of 600 orthopantomographies were randomly selected for this study and manually annotated by a single operator using an image annotation tool (COCO Annotator v.11.0.1) to establish ground truth. The annotation classes included teeth, maxilla, mandible, inferior alveolar nerve, dento- and implant-supported crowns/pontics, endodontic treatment, resin-based restorations, metallic restorations, and implants. The dataset was then divided into training, validation, and testing subsets, which were used to train versions 5, 7, and 8 of You Only Look Once (YOLO) Neural Network. Results were stored, and a posterior performance analysis was carried out by calculating the precision (P), recall (R), F1 Score, Intersection over Union (IoU), and mean average precision (mAP) at 0.5 and 0.5-0.95 thresholds. The confusion matrix and recall precision graphs were also sketched.

RESULTS

YOLOv5s showed an improvement in object detection results with an average R = 0.634, P = 0.781, mAP0.5 = 0.631, and mAP0.5-0.95 = 0.392. YOLOv7m achieved the best object detection results with average R = 0.793, P = 0.779, mAP0.5 = 0.740, and mAP0.5-0.95 = 0,481. For object segmentation, YOLOv8m obtained the best average results (R = 0.589, P = 0.755, mAP0.5 = 0.591, and mAP0.5-0.95 = 0.272).

CONCLUSIONS

YOLOv7m was better suited for object detection, while YOLOv8m demonstrated superior performance in object segmentation. The most frequent error in object detection was related to background classification. Conversely, in object segmentation, there is a tendency to misclassify True Positives across different dental treatment categories.

CLINICAL SIGNIFICANCE

General diagnostic and treatment decisions based on panoramic radiographs can be enhanced using new artificial intelligence-based tools. Nevertheless, the reliability of these neural networks should be subjected to training and validation to ensure their generalizability.

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.

"Determining the efficacy of a machine learning model for measuring periodontal bone loss"

BACKGROUND

Considering the prevalence of Periodontitis, new tools to help improve its diagnostic workflow could be beneficial. Machine Learning (ML) models have already been used in dentistry to automate radiographic analysis.

AIMS

To determine the efficacy of an ML model for automatically measuring Periodontal Bone Loss (PBL) in panoramic radiographs by comparing it to dentists.

METHODS

A dataset of 2010 images with and without PBL was segmented using Label Studio. The dataset was split into n = 1970 images for building a training dataset and n = 40 images for building a testing dataset. We propose a model composed of three components. Firstly, statistical inference techniques find probability functions that best describe the segmented dataset. Secondly, Convolutional Neural Networks extract visual information from the training dataset. Thirdly, an algorithm calculates PBL as a percentage and classifies it in stages. Afterwards, a standardized test compared the model to two radiologists, two periodontists and one general dentist. The test was built using the testing dataset, 40 questions long, done in controlled conditions, with radiologists considered as ground truth. Presence or absence, percentage, and stage of PBL were asked, and time to answer the test was measured in seconds. Diagnostic indices, performance metrics and performance averages were calculated for each participant.

RESULTS

The model had an acceptable performance for diagnosing light to moderate PBL (weighted sensitivity 0.23, weighted F1-score 0.29) and was able to achieve real-time diagnosis. However, it proved incapable of diagnosing severe PBL (sensitivity, precision, and F1-score = 0).

CONCLUSIONS

We propose a Machine Learning model that automates the diagnosis of Periodontal Bone Loss in panoramic radiographs with acceptable performance.

Comparison of deep learning methods for the radiographic detection of patients with different periodontitis stages

OBJECTIVES

The objective of this study is to assess the accuracy of computer-assisted periodontal classification bone loss staging using deep learning (DL) methods on panoramic radiographs and to compare the performance of various models and layers.

METHODS

Panoramic radiographs were diagnosed and classified into 3 groups, namely "healthy," "Stage1/2," and "Stage3/4," and stored in separate folders. The feature extraction stage involved transferring and retraining the feature extraction layers and weights from 3 models, namely ResNet50, DenseNet121, and InceptionV3, which were proposed for classifying the ImageNet dataset, to 3 DL models designed for classifying periodontal bone loss. The features obtained from global average pooling (GAP), global max pooling (GMP), or flatten layers (FL) of convolutional neural network (CNN) models were used as input to the 8 different machine learning (ML) models. In addition, the features obtained from the GAP, GMP, or FL of the DL models were reduced using the minimum redundancy maximum relevance (mRMR) method and then classified again with 8 ML models.

RESULTS

A total of 2533 panoramic radiographs, including 721 in the healthy group, 842 in the Stage1/2 group, and 970 in the Stage3/4 group, were included in the dataset. The average performance values of DenseNet121 + GAP-based and DenseNet121 + GAP + mRMR-based ML techniques on 10 subdatasets and ML models developed using 2 feature selection techniques outperformed CNN models.

CONCLUSIONS

The new DenseNet121 + GAP + mRMR-based support vector machine model developed in this study achieved higher performance in periodontal bone loss classification compared to other models in the literature by detecting effective features from raw images without the need for manual selection.

A novel collaborative learning model for mixed dentition and fillings segmentation in panoramic radiographs

INTRODUCTION

It is critical for dentists to identify and differentiate primary and permanent teeth, fillings, dental restorations and areas with pathological findings when reviewing dental radiographs to ensure that an accurate diagnosis is made and the optimal treatment can be planned. Unfortunately, dental radiographs are sometimes read incorrectly due to human error or low-quality images. While secondary or group review can help catch errors, many dentists work in practice alone and/or do not have time to review all of their patients' radiographs with another dentist. Artificial intelligence may facilitate the accurate interpretation of radiographs. To help support the review of panoramic radiographs, we developed a novel collaborative learning model that simultaneously identifies and differentiates primary and permanent teeth and detects fillings.

METHODS

We used publicly accessible dental panoramic radiographic images and images obtained from the University of Missouri-Kansas City, School of Dentistry to develop and optimize two high-performance classifiers: (1) a system for tooth segmentation that can differentiate primary and permanent teeth and (2) a system to detect dental fillings.

RESULTS

By utilizing these high-performance classifiers, we created models that can identify primary and permanent teeth (mean average precision [mAP] 95.32 % and performance [F-1] 92.50 %), as well as their associated dental fillings (mAP 91.53 % and F-1 91.00 %). We also designed a novel method for collaborative learning that utilizes these two classifiers to enhance recognition performance (mAP 94.09 % and F-1 93.41 %).

CONCLUSIONS

Our model improves upon the existing machine learning models to simultaneously identify and differentiate primary and permanent teeth, and to identify any associated fillings.

CLINICAL SIGNIFICANCE

Human error can lead to incorrect readings of panoramic radiographs. By developing artificial intelligence and machine learning methods to analyze panoramic radiographs, dentists can use this information to support their radiograph interpretations, help communicate the information to patients, and assist dental students learning to read radiographs.

Deep learning for classifying the stages of periodontitis on dental images: a systematic review and meta-analysis

BACKGROUND

The development of deep learning (DL) algorithms for use in dentistry is an emerging trend. Periodontitis is one of the most prevalent oral diseases, which has a notable impact on the life quality of patients. Therefore, it is crucial to classify periodontitis accurately and efficiently. This systematic review aimed to identify the application of DL for the classification of periodontitis and assess the accuracy of this approach.

METHODS

A literature search up to November 2023 was implemented through EMBASE, PubMed, Web of Science, Scopus, and Google Scholar databases. Inclusion and exclusion criteria were used to screen eligible studies, and the quality of the studies was evaluated by the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology with the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies) tool. Random-effects inverse-variance model was used to perform the meta-analysis of a diagnostic test, with which pooled sensitivity, specificity, positive likelihood ratio (LR), negative LR, and diagnostic odds ratio (DOR) were calculated, and a summary receiver operating characteristic (SROC) plot was constructed.

RESULTS

Thirteen studies were included in the meta-analysis. After excluding an outlier, the pooled sensitivity, specificity, positive LR, negative LR and DOR were 0.88 (95%CI 0.82-0.92), 0.82 (95%CI 0.72-0.89), 4.9 (95%CI 3.2-7.5), 0.15 (95%CI 0.10-0.22) and 33 (95%CI 19-59), respectively. The area under the SROC was 0.92 (95%CI 0.89-0.94).

CONCLUSIONS

The accuracy of DL-based classification of periodontitis is high, and this approach could be employed in the future to reduce the workload of dental professionals and enhance the consistency of classification.

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

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

Automatized Detection of Periodontal Bone Loss on Periapical Radiographs by Vision Transformer Networks

Several artificial intelligence-based models have been presented for the detection of periodontal bone loss (PBL), mostly using convolutional neural networks, which are the state of the art in deep learning. Given the emerging breakthrough of transformer networks in computer vision, we aimed to evaluate various models for automatized PBL detection. An image data set of 21,819 anonymized periapical radiographs from the upper/lower and anterior/posterior regions was assessed by calibrated dentists according to PBL. Five vision transformer networks (ViT-base/ViT-large from Google, BEiT-base/BEiT-large from Microsoft, DeiT-base from Facebook/Meta) were utilized and evaluated. Accuracy (ACC), sensitivity (SE), specificity (SP), positive/negative predictive value (PPV/NPV) and area under the ROC curve (AUC) were statistically determined. The overall diagnostic ACC and AUC values ranged from 83.4 to 85.2% and 0.899 to 0.918 for all evaluated transformer networks, respectively. Differences in diagnostic performance were evident for lower (ACC 94.1-96.7%; AUC 0.944-0.970) and upper anterior (86.7-90.2%; 0.948-0.958) and lower (85.6-87.2%; 0.913-0.937) and upper posterior teeth (78.1-81.0%; 0.851-0.875). In this study, only minor differences among the tested networks were detected for PBL detection. To increase the diagnostic performance and to support the clinical use of such networks, further optimisations with larger and manually annotated image data sets are needed.

Detection of Periodontal Bone Loss on Periapical Radiographs-A Diagnostic Study Using Different Convolutional Neural Networks

Interest in machine learning models and convolutional neural networks (CNNs) for diagnostic purposes is steadily increasing in dentistry. Here, CNNs can potentially help in the classification of periodontal bone loss (PBL). In this study, the diagnostic performance of five CNNs in detecting PBL on periapical radiographs was analyzed. A set of anonymized periapical radiographs (N = 21,819) was evaluated by a group of trained and calibrated dentists and classified into radiographs without PBL or with mild, moderate, or severe PBL. Five CNNs were trained over five epochs. Statistically, diagnostic performance was analyzed using accuracy (ACC), sensitivity (SE), specificity (SP), and area under the receiver operating curve (AUC). Here, overall ACC ranged from 82.0% to 84.8%, SE 88.8-90.7%, SP 66.2-71.2%, and AUC 0.884-0.913, indicating similar diagnostic performance of the five CNNs. Furthermore, performance differences were evident in the individual sextant groups. Here, the highest values were found for the mandibular anterior teeth (ACC 94.9-96.0%) and the lowest values for the maxillary posterior teeth (78.0-80.7%). It can be concluded that automatic assessment of PBL seems to be possible, but that diagnostic accuracy varies depending on the location in the dentition. Future research is needed to improve performance for all tooth groups.

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.

Efficacy of artificial intelligence in the detection of periodontal bone loss and classification of periodontal diseases: A systematic review

BACKGROUND

Artificial intelligence (AI) can aid in the diagnosis and treatment planning of periodontal disease by means of reducing subjectivity. This systematic review aimed to evaluate the efficacy of AI models in detecting radiographic periodontal bone loss (PBL) and accuracy in classifying lesions.

TYPES OF STUDIES REVIEWED

The authors conducted an electronic search of PubMed, Scopus, and Web of Science for articles published through August 2022. Articles evaluating the efficacy of AI in determining PBL were included. The authors assessed the articles using the Quality Assessment for Studies of Diagnostic Accuracy tool. They used the Grading of Recommendations Assessment, Development and Evaluation criteria to evaluate the certainty of evidence.

RESULTS

Of the 13 articles identified through electronic search, 6 studies met the inclusion criteria, using a variety of AI algorithms and different modalities, including panoramic and intraoral radiographs. Sensitivity, specificity, accuracy, and pixel accuracy were the outcomes measured. Although some studies found no substantial difference between AI and dental clinicians' performance, others showed AI's superiority in detecting PBL. Evidence suggests that AI has the potential to aid in the detection of PBL and classification of periodontal diseases. However, further research is needed to standardize AI algorithms and validate their clinical usefulness.

PRACTICAL IMPLICATIONS

Although the use of AI may offer some benefits in the detection and classification of periodontal diseases, the low level of evidence and the inconsistent performance of AI algorithms suggest that caution should be exercised when considering the use of AI models in diagnosing PBL. This review was registered at PROSPERO (CRD42022364600).

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

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

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.

Assessing the Effectiveness of Artificial Intelligence Models for Detecting Alveolar Bone Loss in Periodontal Disease: A Panoramic Radiograph Study

The assessment of alveolar bone loss, a crucial element of the periodontium, plays a vital role in the diagnosis of periodontitis and the prognosis of the disease. In dentistry, artificial intelligence (AI) applications have demonstrated practical and efficient diagnostic capabilities, leveraging machine learning and cognitive problem-solving functions that mimic human abilities. This study aims to evaluate the effectiveness of AI models in identifying alveolar bone loss as present or absent across different regions. To achieve this goal, alveolar bone loss models were generated using the PyTorch-based YOLO-v5 model implemented via CranioCatch software, detecting periodontal bone loss areas and labeling them using the segmentation method on 685 panoramic radiographs. Besides general evaluation, models were grouped according to subregions (incisors, canines, premolars, and molars) to provide a targeted evaluation. Our findings reveal that the lowest sensitivity and F1 score values were associated with total alveolar bone loss, while the highest values were observed in the maxillary incisor region. It shows that artificial intelligence has a high potential in analytical studies evaluating periodontal bone loss situations. Considering the limited amount of data, it is predicted that this success will increase with the provision of machine learning by using a more comprehensive data set in further studies.

Automatic sex estimation using deep convolutional neural network based on orthopantomogram images

Sex estimation is very important in forensic applications as part of individual identification. Morphological sex estimation methods predominantly focus on anatomical measurements. Based on the close relationship between sex chromosome genes and facial characterization, craniofacial hard tissues morphology shows sex dimorphism. In order to establish a more labor-saving, rapid, and accurate reference for sex estimation, the study investigated a deep learning network-based artificial intelligence (AI) model using orthopantomograms (OPG) to estimate sex in northern Chinese subjects. In total, 10703 OPG images were divided into training (80%), validation (10%), and test sets (10%). At the same time, different age thresholds were selected to compare the accuracy differences between adults and minors. The accuracy of sex estimation using CNN (convolutional neural network) model was higher for adults (90.97%) compared with minors (82.64%). This work demonstrated that the proposed model trained with a large dataset could be used in automatic morphological sex-related identification with favorable performance and practical significance in forensic science for adults in northern China, while also providing a reference for minors to some extent.

Comparison of Multi-Label U-Net and Mask R-CNN for panoramic radiograph segmentation to detect periodontitis

Purpose

Periodontitis, the most prevalent chronic inflammatory condition affecting teeth-supporting tissues, is diagnosed and classified through clinical and radiographic examinations. The staging of periodontitis using panoramic radiographs provides information for designing computer-assisted diagnostic systems. Performing image segmentation in periodontitis is required for image processing in diagnostic applications. This study evaluated image segmentation for periodontitis staging based on deep learning approaches.

Materials and Methods

Multi-Label U-Net and Mask R-CNN models were compared for image segmentation to detect periodontitis using 100 digital panoramic radiographs. Normal conditions and 4 stages of periodontitis were annotated on these panoramic radiographs. A total of 1100 original and augmented images were then randomly divided into a training (75%) dataset to produce segmentation models and a testing (25%) dataset to determine the evaluation metrics of the segmentation models.

Results

The performance of the segmentation models against the radiographic diagnosis of periodontitis conducted by a dentist was described by evaluation metrics (i.e., dice coefficient and intersection-over-union [IoU] score). Multi-Label U-Net achieved a dice coefficient of 0.96 and an IoU score of 0.97. Meanwhile, Mask R-CNN attained a dice coefficient of 0.87 and an IoU score of 0.74. U-Net showed the characteristic of semantic segmentation, and Mask R-CNN performed instance segmentation with accuracy, precision, recall, and F1-score values of 95%, 85.6%, 88.2%, and 86.6%, respectively.

Conclusion

Multi-Label U-Net produced superior image segmentation to that of Mask R-CNN. The authors recommend integrating it with other techniques to develop hybrid models for automatic periodontitis detection.

A generative adversarial inpainting network to enhance prediction of periodontal clinical attachment level

OBJECTIVES

Bone level as measured by clinical attachment levels (CAL) are critical findings that determine the diagnosis of periodontal disease. Deep learning algorithms are being used to determine CAL which aid in the diagnosis of periodontal disease. However, the limited field-of-view of bitewing x-rays poses a challenge for convolutional neural networks (CNN) because out-of-view anatomy cannot be directly considered. This study presents an inpainting algorithm using generative adversarial networks (GANs) coupled with partial convolutions to predict out-of-view anatomy to enhance CAL prediction accuracy.

METHODS

Retrospective purposive sampling of cases with healthy periodontium and diseased periodontium with bitewing and periapical radiographs and clinician recorded CAL were utilized. Data utilized was from July 1, 2016 through January 30, 2020. 80,326 images were used for training, 12,901 images were used for validation and 10,687 images were used to compare non-inpainted methods to inpainted methods for CAL predictions. Statistical analyses were mean bias error (MBE), mean absolute error (MAE) and Dunn's pairwise test comparing CAL at p=0.05.

RESULTS

Comparator p-values demonstrated statistically significant improvement in CAL prediction accuracy between corresponding inpainted and non-inpainted methods with MAE of 1.04 mm and 1.50 mm respectively. The Dunn's pairwise test indicated statistically significant improvement in CAL prediction accuracy between inpainted methods compared to their non-inpainted counterparts, with the best performing methods achieving a Dunn's pairwise value of -63.89.

CONCLUSIONS

This study demonstrates the superiority of using a generative adversarial inpainting network with partial convolutions to predict CAL from bitewing and periapical images.

CLINICAL SIGNIFICANCE

Artificial intelligence was developed and utilized to predict clinical attachment level compared to clinical measurements. A generative adversarial inpainting network with partial convolutions was developed, tested and validated to predict clinical attachment level. The inpainting approach was found to be superior to non-inpainted methods and within the 1mm clinician-determined measurement standard.