Automatic Feature Segmentation in Dental Periapical Radiographs

Evaluation of root canal filling length on periapical radiograph using artificial intelligence

OBJECTIVES

This work proposes a novel method to evaluate root canal filling (RCF) success using artificial intelligence (AI) and image analysis techniques.

METHODS

1121 teeth with root canal treatment in 597 periapical radiographs (PARs) were anonymized and manually labeled. First, RCFs were segmented using 5 different state-of-the-art deep learning models based on convolutional neural networks. Their performances were compared based on the intersection over union (IoU), dice score and accuracy. Additionally, fivefold cross validation was applied for the best-performing model and their outputs were later used for further analysis. Secondly, images were processed via a graphical user interface (GUI) that allows dental clinicians to mark the apex of the tooth, which was used to find the distance between the apex of the tooth and the nearest RCF prediction of the deep learning model towards it. The distance can show whether the RCF is normal, short or long.

RESULTS

Model performances were evaluated by well-known evaluation metrics for segmentation such as IoU, Dice score and accuracy. CNN-based models can achieve an accuracy of 88%, an IoU of 79% and Dice score of 88% in segmenting root canal fillings.

CONCLUSIONS

Our study demonstrates that AI-based solutions present accurate and reliable performance for root canal filling evaluation.

Artificial Intelligence in Dentistry: A Descriptive Review

Artificial intelligence (AI) is an area of computer science that focuses on designing machines or systems that can perform operations that would typically need human intelligence. AI is a rapidly developing technology that has grabbed the interest of researchers from all across the globe in the healthcare industry. Advancements in machine learning and data analysis have revolutionized oral health diagnosis, treatment, and management, making it a transformative force in healthcare, particularly in dentistry. Particularly in dentistry, AI is becoming increasingly prevalent as it contributes to the diagnosis of oro-facial diseases, offers treatment modalities, and manages practice in the dental operatory. All dental disciplines, including oral medicine, operative dentistry, pediatric dentistry, periodontology, orthodontics, oral and maxillofacial surgery, prosthodontics, and forensic odontology, have adopted AI. The majority of AI applications in dentistry are for diagnoses based on radiographic or optical images, while other tasks are less applicable due to constraints such as data availability, uniformity, and computational power. Evidence-based dentistry is considered the gold standard for decision making by dental professionals, while AI machine learning models learn from human expertise. Dentistry AI and technology systems can provide numerous benefits, such as improved diagnosis accuracy and increased administrative task efficiency. Dental practices are already implementing various AI applications, such as imaging and diagnosis, treatment planning, robotics and automation, augmented and virtual reality, data analysis and predictive analytics, and administrative support. The dentistry field has extensively used artificial intelligence to assist less-skilled practitioners in reaching a more precise diagnosis. These AI models effectively recognize and classify patients with various oro-facial problems into different risk categories, both individually and on a group basis. The objective of this descriptive review is to review the most recent developments of AI in the field of dentistry.

Clinical Validation of Deep Learning for Segmentation of Multiple Dental Features in Periapical Radiographs

Periapical radiographs are routinely used in dental practice for diagnosis and treatment planning purposes. However, they often suffer from artifacts, distortions, and superimpositions, which can lead to potential misinterpretations. Thus, an automated detection system is required to overcome these challenges. Artificial intelligence (AI) has been revolutionizing various fields, including medicine and dentistry, by facilitating the development of intelligent systems that can aid in performing complex tasks such as diagnosis and treatment planning. The purpose of the present study was to verify the diagnostic performance of an AI system for the automatic detection of teeth, caries, implants, restorations, and fixed prosthesis on periapical radiographs. A dataset comprising 1000 periapical radiographs collected from 500 adult patients was analyzed by an AI system and compared with annotations provided by two oral and maxillofacial radiologists. A strong correlation (R > 0.5) was observed between AI perception and observers 1 and 2 in carious teeth (0.7-0.73), implants (0.97-0.98), restored teeth (0.85-0.89), teeth with fixed prosthesis (0.92-0.94), and missing teeth (0.82-0.85). The automatic detection by the AI system was comparable to the oral radiologists and may be useful for automatic identification in periapical radiographs.

Automatic feature segmentation in dental panoramic radiographs

OBJECTIVE

The purpose of the present study was to verify the diagnostic performance of an AI system for the automatic detection of teeth, caries, implants, restorations, and fixed prosthesis on panoramic radiography.

METHODS

This is a cross-sectional study. A dataset comprising 1000 panoramic radiographs collected from 500 adult patients was analyzed by an AI system and compared with annotations provided by two oral and maxillofacial radiologists.

RESULTS

A strong correlation (R > 0.5) was observed between AI perception and observers 1 and 2 in carious teeth (0.691-0.878), implants (0.770-0.952), restored teeth (0.773-0.834), teeth with fixed prostheses (0.972-0.980), and missing teeth (0.956-0.988).

DISCUSSION

Panoramic radiographs are commonly used for diagnosis and treatment planning. However, they often suffer from artifacts, distortions, and superimpositions, leading to potential misinterpretations. Thus, an automated detection system is required to tackle these challenges. Artificial intelligence (AI) has revolutionized various fields, including dentistry, by enabling the development of intelligent systems that can assist in complex tasks such as diagnosis and treatment planning.

CONCLUSION

The automatic detection by the AI system was comparable to oral radiologists and may be useful for automatic identifications in panoramic radiographs. These findings signify the potential for AI systems to enhance diagnostic accuracy and efficiency in dental practices, potentially reducing the likelihood of diagnostic errors caused by unexperienced professionals.

Validation of artificial intelligence application for dental caries diagnosis on intraoral bitewing and periapical radiographs

OBJECTIVES

This study aimed to assess the reliability of AI-based system that assists the healthcare processes in the diagnosis of caries on intraoral radiographs.

METHODS

The proximal surfaces of the 323 selected teeth on the intraoral radiographs were evaluated by two independent observers using an AI-based (Diagnocat) system. The presence or absence of carious lesions was recorded during Phase 1. After 4 months, the AI-aided human observers evaluated the same radiographs (Phase 2), and the advanced convolutional neural network (CNN) reassessed the radiographic data (Phase 3). Subsequently, data reflecting human disagreements were excluded (Phase 4). For each phase, the Cohen and Fleiss kappa values, as well as the sensitivity, specificity, positive and negative predictive values, and diagnostic accuracy of Diagnocat, were calculated.

RESULTS

During the four phases, the range of Cohen kappa values between the human observers and Diagnocat were κ=0.66-1, κ=0.58-0.7, and κ=0.49-0.7. The Fleiss kappa values were κ=0.57-0.8. The sensitivity, specificity and diagnostic accuracy values ranged between 0.51-0.76, 0.88-0.97 and 0.76-0.86, respectively.

CONCLUSIONS

The Diagnocat CNN supports the evaluation of intraoral radiographs for caries diagnosis, as determined by consensus between human and AI system observers.

CLINICAL SIGNIFICANCE

Our study may aid in the understanding of deep learning-based systems developed for dental imaging modalities for dentists and contribute to expanding the body of results in the field of AI-supported dental radiology..

Tooth numbering and classification on bitewing radiographs: an artificial intelligence pilot study

OBJECTIVE

The aim of this study is to assess the efficacy of employing a deep learning methodology for the automated identification and enumeration of permanent teeth in bitewing radiographs. The experimental procedures and techniques employed in this study are described in the following section.

STUDY DESIGN

A total of 1248 bitewing radiography images were annotated using the CranioCatch labeling program, developed in Eskişehir, Turkey. The dataset has been partitioned into 3 subsets: training (n = 1000, 80% of the total), validation (n = 124, 10% of the total), and test (n = 124, 10% of the total) sets. The images were subjected to a 3 × 3 clash operation in order to enhance the clarity of the labeled regions.

RESULTS

The F1, sensitivity and precision results of the artificial intelligence model obtained using the Yolov5 architecture in the test dataset were found to be 0.9913, 0.9954, and 0.9873, respectively.

CONCLUSION

The utilization of numerical identification for teeth within deep learning-based artificial intelligence algorithms applied to bitewing radiographs has demonstrated notable efficacy. The utilization of clinical decision support system software, which is augmented by artificial intelligence, has the potential to enhance the efficiency and effectiveness of dental practitioners.

Fully automated deep learning approach to dental development assessment in panoramic radiographs

BACKGROUND

Dental development assessment is an important factor in dental age estimation and dental maturity evaluation. This study aimed to develop and evaluate the performance of an automated dental development staging system based on Demirjian's method using deep learning.

METHODS

The study included 5133 anonymous panoramic radiographs obtained from the Department of Pediatric Dentistry database at Seoul National University Dental Hospital between 2020 and 2021. The proposed methodology involves a three-step procedure for dental staging: detection, segmentation, and classification. The panoramic data were randomly divided into training and validating sets (8:2), and YOLOv5, U-Net, and EfficientNet were trained and employed for each stage. The models' performance, along with the Grad-CAM analysis of EfficientNet, was evaluated.

RESULTS

The mean average precision (mAP) was 0.995 for detection, and the segmentation achieved an accuracy of 0.978. The classification performance showed F1 scores of 69.23, 80.67, 84.97, and 90.81 for the Incisor, Canine, Premolar, and Molar models, respectively. In the Grad-CAM analysis, the classification model focused on the apical portion of the developing tooth, a crucial feature for staging according to Demirjian's method.

CONCLUSIONS

These results indicate that the proposed deep learning approach for automated dental staging can serve as a supportive tool for dentists, facilitating rapid and objective dental age estimation and dental maturity evaluation.

Exploring the Methodological Approaches of Studies on Radiographic Databases Used in Cariology to Feed Artificial Intelligence: A Systematic Review

INTRODUCTION

A growing number of studies on diagnostic imaging show superior efficiency and accuracy of computer-aided diagnostic systems compared to those of certified dentists. This methodological systematic review aimed to evaluate the different methodological approaches used by studies focusing on machine learning and deep learning that have used radiographic databases to classify, detect, and segment dental caries.

METHODS

The protocol was registered in PROSPERO before data collection (CRD42022348097). Literature research was performed in MEDLINE, Embase, IEEE Xplore, and Web of Science until December 2022, without language restrictions. Studies and surveys using a dental radiographic database for the classification, detection, or segmentation of carious lesions were sought. Records deemed eligible were retrieved and further assessed for inclusion by two reviewers who resolved any discrepancies through consensus. A third reviewer was consulted when any disagreements or discrepancies persisted between the two reviewers. After data extraction, the same reviewers assessed the methodological quality using the CLAIM and QUADAS-AI checklists.

RESULTS

After screening 325 articles, 35 studies were eligible and included. The bitewing was the most commonly used radiograph (n = 17) at the time when detection (n = 15) was the most explored computer vision task. The sample sizes used ranged from 95 to 38,437, while the augmented training set ranged from 300 to 315,786. Convolutional neural network was the most commonly used model. The mean completeness of CLAIM items was 49% (SD ± 34%). The applicability of the CLAIM checklist items revealed several weaknesses in the methodology of the selected studies: most of the studies were monocentric, and only 9% of them used an external test set when evaluating the model's performance. The QUADAS-AI tool revealed that only 43% of the studies included in this systematic review were at low risk of bias concerning the standard reference domain.

CONCLUSION

This review demonstrates that the overall scientific quality of studies conducted to feed artificial intelligence algorithms is low. Some improvement in the design and validation of studies can be made with the development of a standardized guideline for the reproducibility and generalizability of results and, thus, their clinical applications.

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.