Efficacy of deep convolutional neural network algorithm for the identification and classification of dental implant systems, using panoramic and periapical radiographs: A pilot study

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.

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 for 3D bone segmentation and prediction of missing tooth region for dental implant planning

Recent studies have shown that dental implants have high long-term survival rates, indicating their effectiveness compared to other treatments. However, there is still a concern regarding treatment failure. Deep learning methods, specifically U-Net models, have been effectively applied to analyze medical and dental images. This study aims to utilize U-Net models to segment bone in regions where teeth are missing in cone-beam computerized tomography (CBCT) scans and predict the positions of implants. The proposed models were applied to a CBCT dataset of Taibah University Dental Hospital (TUDH) patients between 2018 and 2023. They were evaluated using different performance metrics and validated by a domain expert. The experimental results demonstrated outstanding performance in terms of dice, precision, and recall for bone segmentation (0.93, 0.94, and 0.93, respectively) with a low volume error (0.01). The proposed models offer promising automated dental implant planning for dental implantologists.

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

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

Identification of dental implant systems from low-quality and distorted dental radiographs using AI trained on a large multi-center dataset

Most artificial intelligence (AI) studies have attempted to identify dental implant systems (DISs) while excluding low-quality and distorted dental radiographs, limiting their actual clinical use. This study aimed to evaluate the effectiveness of an AI model, trained on a large and multi-center dataset, in identifying different types of DIS in low-quality and distorted dental radiographs. Based on the fine-tuned pre-trained ResNet-50 algorithm, 156,965 panoramic and periapical radiological images were used as training and validation datasets, and 530 low-quality and distorted images of four types (including those not perpendicular to the axis of the fixture, radiation overexposure, cut off the apex of the fixture, and containing foreign bodies) were used as test datasets. Moreover, the accuracy performance of low-quality and distorted DIS classification was compared using AI and five periodontists. Based on a test dataset, the performance evaluation of the AI model achieved accuracy, precision, recall, and F1 score metrics of 95.05%, 95.91%, 92.49%, and 94.17%, respectively. However, five periodontists performed the classification of nine types of DISs based on four different types of low-quality and distorted radiographs, achieving a mean overall accuracy of 37.2 ± 29.0%. Within the limitations of this study, AI demonstrated superior accuracy in identifying DIS from low-quality or distorted radiographs, outperforming dental professionals in classification tasks. However, for actual clinical application of AI, extensive standardization research on low-quality and distorted radiographic images is essential.

Artificial Intelligence in Adult and Pediatric Dentistry: A Narrative Review

Artificial intelligence (AI) has been recently introduced into clinical dentistry, and it has assisted professionals in analyzing medical data with unprecedented speed and an accuracy level comparable to humans. With the help of AI, meaningful information can be extracted from dental databases, especially dental radiographs, to devise machine learning (a subset of AI) models. This study focuses on models that can diagnose and assist with clinical conditions such as oral cancers, early childhood caries, deciduous teeth numbering, periodontal bone loss, cysts, peri-implantitis, osteoporosis, locating minor apical foramen, orthodontic landmark identification, temporomandibular joint disorders, and more. The aim of the authors was to outline by means of a review the state-of-the-art applications of AI technologies in several dental subfields and to discuss the efficacy of machine learning algorithms, especially convolutional neural networks (CNNs), among different types of patients, such as pediatric cases, that were neglected by previous reviews. They performed an electronic search in PubMed, Google Scholar, Scopus, and Medline to locate relevant articles. They concluded that even though clinicians encounter challenges in implementing AI technologies, such as data management, limited processing capabilities, and biased outcomes, they have observed positive results, such as decreased diagnosis costs and time, as well as early cancer detection. Thus, further research and development should be considered to address the existing complications.

Accuracy of Artificial Intelligence Models in Dental Implant Fixture Identification and Classification from Radiographs: A Systematic Review

Background and Objectives: The availability of multiple dental implant systems makes it difficult for the treating dentist to identify and classify the implant in case of inaccessibility or loss of previous records. Artificial intelligence (AI) is reported to have a high success rate in medical image classification and is effectively used in this area. Studies have reported improved implant classification and identification accuracy when AI is used with trained dental professionals. This systematic review aims to analyze various studies discussing the accuracy of AI tools in implant identification and classification. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed, and the study was registered with the International Prospective Register of Systematic Reviews (PROSPERO). The focused PICO question for the current study was "What is the accuracy (outcome) of artificial intelligence tools (Intervention) in detecting and/or classifying the type of dental implant (Participant/population) using X-ray images?" Web of Science, Scopus, MEDLINE-PubMed, and Cochrane were searched systematically to collect the relevant published literature. The search strings were based on the formulated PICO question. The article search was conducted in January 2024 using the Boolean operators and truncation. The search was limited to articles published in English in the last 15 years (January 2008 to December 2023). The quality of all the selected articles was critically analyzed using the Quality Assessment and Diagnostic Accuracy Tool (QUADAS-2). Results: Twenty-one articles were selected for qualitative analysis based on predetermined selection criteria. Study characteristics were tabulated in a self-designed table. Out of the 21 studies evaluated, 14 were found to be at risk of bias, with high or unclear risk in one or more domains. The remaining seven studies, however, had a low risk of bias. The overall accuracy of AI models in implant detection and identification ranged from a low of 67% to as high as 98.5%. Most included studies reported mean accuracy levels above 90%. Conclusions: The articles in the present review provide considerable evidence to validate that AI tools have high accuracy in identifying and classifying dental implant systems using 2-dimensional X-ray images. These outcomes are vital for clinical diagnosis and treatment planning by trained dental professionals to enhance patient treatment outcomes.

Advancing maxillofacial prosthodontics by using pre-trained convolutional neural networks: Image-based classification of the maxilla

PURPOSE

The study aimed to compare the performance of four pre-trained convolutional neural networks in recognizing seven distinct prosthodontic scenarios involving the maxilla, as a preliminary step in developing an artificial intelligence (AI)-powered prosthesis design system.

MATERIALS AND METHODS

Seven distinct classes, including cleft palate, dentulous maxillectomy, edentulous maxillectomy, reconstructed maxillectomy, completely dentulous, partially edentulous, and completely edentulous, were considered for recognition. Utilizing transfer learning and fine-tuned hyperparameters, four AI models (VGG16, Inception-ResNet-V2, DenseNet-201, and Xception) were employed. The dataset, consisting of 3541 preprocessed intraoral occlusal images, was divided into training, validation, and test sets. Model performance metrics encompassed accuracy, precision, recall, F1 score, area under the receiver operating characteristic curve (AUC), and confusion matrix.

RESULTS

VGG16, Inception-ResNet-V2, DenseNet-201, and Xception demonstrated comparable performance, with maximum test accuracies of 0.92, 0.90, 0.94, and 0.95, respectively. Xception and DenseNet-201 slightly outperformed the other models, particularly compared with InceptionResNet-V2. Precision, recall, and F1 scores exceeded 90% for most classes in Xception and DenseNet-201 and the average AUC values for all models ranged between 0.98 and 1.00.

CONCLUSIONS

While DenseNet-201 and Xception demonstrated superior performance, all models consistently achieved diagnostic accuracy exceeding 90%, highlighting their potential in dental image analysis. This AI application could help work assignments based on difficulty levels and enable the development of an automated diagnosis system at patient admission. It also facilitates prosthesis designing by integrating necessary prosthesis morphology, oral function, and treatment difficulty. Furthermore, it tackles dataset size challenges in model optimization, providing valuable insights for future research.

A Comparative Analysis of Deep Learning-Based Approaches for Classifying Dental Implants Decision Support System

This study aims to provide an effective solution for the autonomous identification of dental implant brands through a deep learning-based computer diagnostic system. It also seeks to ascertain the system's potential in clinical practices and to offer a strategic framework for improving diagnosis and treatment processes in implantology. This study employed a total of 28 different deep learning models, including 18 convolutional neural network (CNN) models (VGG, ResNet, DenseNet, EfficientNet, RegNet, ConvNeXt) and 10 vision transformer models (Swin and Vision Transformer). The dataset comprises 1258 panoramic radiographs from patients who received implant treatments at Erciyes University Faculty of Dentistry between 2012 and 2023. It is utilized for the training and evaluation process of deep learning models and consists of prototypes from six different implant systems provided by six manufacturers. The deep learning-based dental implant system provided high classification accuracy for different dental implant brands using deep learning models. Furthermore, among all the architectures evaluated, the small model of the ConvNeXt architecture achieved an impressive accuracy rate of 94.2%, demonstrating a high level of classification success.This study emphasizes the effectiveness of deep learning-based systems in achieving high classification accuracy in dental implant types. These findings pave the way for integrating advanced deep learning tools into clinical practice, promising significant improvements in patient care and treatment outcomes.

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.

Deep-learning performance in identifying and classifying dental implant systems from dental imaging: a systematic review and meta-analysis

Deep learning (DL) offers promising performance in computer vision tasks and is highly suitable for dental image recognition and analysis. We evaluated the accuracy of DL algorithms in identifying and classifying dental implant systems (DISs) using dental imaging. In this systematic review and meta-analysis, we explored the MEDLINE/PubMed, Scopus, Embase, and Google Scholar databases and identified studies published between January 2011 and March 2022. Studies conducted on DL approaches for DIS identification or classification were included, and the accuracy of the DL models was evaluated using panoramic and periapical radiographic images. The quality of the selected studies was assessed using QUADAS-2. This review was registered with PROSPERO (CRDCRD42022309624). From 1,293 identified records, 9 studies were included in this systematic review and meta-analysis. The DL-based implant classification accuracy was no less than 70.75% (95% confidence interval [CI], 65.6%-75.9%) and no higher than 98.19 (95% CI, 97.8%-98.5%). The weighted accuracy was calculated, and the pooled sample size was 46,645, with an overall accuracy of 92.16% (95% CI, 90.8%-93.5%). The risk of bias and applicability concerns were judged as high for most studies, mainly regarding data selection and reference standards. DL models showed high accuracy in identifying and classifying DISs using panoramic and periapical radiographic images. Therefore, DL models are promising prospects for use as decision aids and decision-making tools; however, there are limitations with respect to their application in actual clinical practice.

Evaluation of accuracy of deep learning and conventional neural network algorithms in detection of dental implant type using intraoral radiographic images: A systematic review and meta-analysis

STATEMENT OF PROBLEM

With the growing importance of implant brand detection in clinical practice, the accuracy of machine learning algorithms in implant brand detection has become a subject of research interest. Recent studies have shown promising results for the use of machine learning in implant brand detection. However, despite these promising findings, a comprehensive evaluation of the accuracy of machine learning in implant brand detection is needed.

PURPOSE

The purpose of this systematic review and meta-analysis was to assess the accuracy, sensitivity, and specificity of deep learning algorithms in implant brand detection using 2-dimensional images such as from periapical or panoramic radiographs.

MATERIAL AND METHODS

Electronic searches were conducted in PubMed, Embase, Scopus, Scopus Secondary, and Web of Science databases. Studies that met the inclusion criteria were assessed for quality using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Meta-analyses were performed using the random-effects model to estimate the pooled performance measures and the 95% confidence intervals (CIs) using STATA v.17.

RESULTS

Thirteen studies were selected for the systematic review, and 3 were used in the meta-analysis. The meta-analysis of the studies found that the overall accuracy of CNN algorithms in detecting dental implants in radiographic images was 95.63%, with a sensitivity of 94.55% and a specificity of 97.91%. The highest reported accuracy was 99.08% for CNN Multitask ResNet152 algorithm, and sensitivity and specificity were 100.00% and 98.70% respectively for the deep CNN (Neuro-T version 2.0.1) algorithm with the Straumann SLActive BLT implant brand. All studies had a low risk of bias.

CONCLUSIONS

The highest accuracy and sensitivity were reported in studies using CNN Multitask ResNet152 and deep CNN (Neuro-T version 2.0.1) algorithms.

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.

Classification of dental implant systems using cloud-based deep learning algorithm: an experimental study

BACKGROUND

This study aimed to evaluate the accuracy and clinical usability of implant system classification using automated machine learning on a Google Cloud platform.

METHODS

Four dental implant systems were selected: Osstem TSIII, Osstem USII, Biomet 3i Os-seotite External, and Dentsply Sirona Xive. A total of 4,800 periapical radiographs (1,200 for each implant system) were collected and labeled based on electronic medical records. Regions of interest were manually cropped to 400×800 pixels, and all images were uploaded to Google Cloud storage. Approximately 80% of the images were used for training, 10% for validation, and 10% for testing. Google automated machine learning (AutoML) Vision automatically executed a neural architecture search technology to apply an appropriate algorithm to the uploaded data. A single-label image classification model was trained using AutoML. The performance of the mod-el was evaluated in terms of accuracy, precision, recall, specificity, and F1 score.

RESULTS

The accuracy, precision, recall, specificity, and F1 score of the AutoML Vision model were 0.981, 0.963, 0.961, 0.985, and 0.962, respectively. Osstem TSIII had an accuracy of 100%. Osstem USII and 3i Osseotite External were most often confused in the confusion matrix.

CONCLUSION

Deep learning-based AutoML on a cloud platform showed high accuracy in the classification of dental implant systems as a fine-tuned convolutional neural network. Higher-quality images from various implant systems will be required to improve the performance and clinical usability of the model.

The preliminary in vitro study and application of deep learning algorithm in cone beam computed tomography image implant recognition

To properly repair and maintain implants, which are bone tissue implants that replace natural tooth roots, it is crucial to accurately identify their brand and specification. Deep learning has demonstrated outstanding capabilities in analysis, such as image identification and classification, by learning the inherent rules and degrees of representation of data models. The purpose of this study is to evaluate deep learning algorithms and their supporting application software for their ability to recognize and categorize three dimensional (3D) Cone Beam Computed Tomography (CBCT) images of dental implants. By using CBCT technology, the 3D imaging data of 27 implants of various sizes and brands were obtained. Following manual processing, the data were transformed into a data set that had 13,500 two-dimensional data. Nine deep learning algorithms including GoogleNet, InceptionResNetV2, InceptionV3, ResNet50, ResNet50V2, ResNet101, ResNet101V2, ResNet152 and ResNet152V2 were used to perform the data. Accuracy rates, confusion matrix, ROC curve, AUC, number of model parameters and training times were used to assess the efficacy of these algorithms. These 9 deep learning algorithms achieved training accuracy rates of 100%, 99.3%, 89.3%, 99.2%, 99.1%, 99.5%, 99.4%, 99.5%, 98.9%, test accuracy rates of 98.3%, 97.5%, 94.8%, 85.4%, 92.5%, 80.7%, 93.6%, 93.2%, 99.3%, area under the curve (AUC) values of 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00. When used to identify implants, all nine algorithms perform satisfactorily, with ResNet152V2 achieving the highest test accuracy, classification accuracy, confusion matrix area under the curve, and receiver operating characteristic curve area under the curve area. The results showed that the ResNet152V2 has the best classification effect on identifying implants. The artificial intelligence identification system and application software based on this algorithm can efficiently and accurately identify the brands and specifications of 27 classified implants through processed 3D CBCT images in vitro, with high stability and low recognition cost.

Deep learning and clustering approaches for dental implant size classification based on periapical radiographs

This study investigated two artificial intelligence (AI) methods for automatically classifying dental implant diameter and length based on periapical radiographs. The first method, deep learning (DL), involved utilizing the pre-trained VGG16 model and adjusting the fine-tuning degree to analyze image data obtained from periapical radiographs. The second method, clustering analysis, was accomplished by analyzing the implant-specific feature vector derived from three key points coordinates of the dental implant using the k-means++ algorithm and adjusting the weight of the feature vector. DL and clustering model classified dental implant size into nine groups. The performance metrics of AI models were accuracy, sensitivity, specificity, F1-score, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC-ROC). The final DL model yielded performances above 0.994, 0.950, 0.994, 0.974, 0.952, 0.994, and 0.975, respectively, and the final clustering model yielded performances above 0.983, 0.900, 0.988, 0.923, 0.909, 0.988, and 0.947, respectively. When comparing the AI model before tuning and the final AI model, statistically significant performance improvements were observed in six out of nine groups for DL models and four out of nine groups for clustering models based on AUC-ROC. Two AI models showed reliable classification performances. For clinical applications, AI models require validation on various multicenter data.

The CNN model aided the study of the clinical value hidden in the implant images

PURPOSE

This article aims to construct a new method to evaluate radiographic image identification results based on artificial intelligence, which can complement the limited vision of researchers when studying the effect of various factors on clinical implantation outcomes.

METHODS

We constructed a convolutional neural network (CNN) model using the clinical implant radiographic images. Moreover, we used gradient-weighted class activation mapping (Grad-CAM) to obtain thermal maps to present identification differences before performing statistical analyses. Subsequently, to verify whether these differences presented by the Grad-CAM algorithm would be of value to clinical practices, we measured the bone thickness around the identified sites. Finally, we analyzed the influence of the implant type on the implantation according to the measurement results.

RESULTS

The thermal maps showed that the sites with significant differences between Straumann BL and Bicon implants as identified by the CNN model were mainly the thread and neck area. (2) The heights of the mesial, distal, buccal, and lingual bone of the Bicon implant post-op were greater than those of Straumann BL (P < 0.05). (3) Between the first and second stages of surgery, the amount of bone thickness variation at the buccal and lingual sides of the Bicon implant platform was greater than that of the Straumann BL implant (P < 0.05).

CONCLUSION

According to the results of this study, we found that the identified-neck-area of the Bicon implant was placed deeper than the Straumann BL implant, and there was more bone resorption on the buccal and lingual sides at the Bicon implant platform between the first and second stages of surgery. In summary, this study proves that using the CNN classification model can identify differences that complement our limited vision.

Artificial Intelligence Techniques for Automatic Detection of Peri-implant Marginal Bone Remodeling in Intraoral Radiographs

Peri-implantitis can cause marginal bone remodeling around implants. The aim is to develop an automatic image processing approach based on two artificial intelligence (AI) techniques in intraoral (periapical and bitewing) radiographs to assist dentists in determining bone loss. The first is a deep learning (DL) object-detector (YOLOv3) to roughly identify (no exact localization is required) two objects: prosthesis (crown) and implant (screw). The second is an image understanding-based (IU) process to fine-tune lines on screw edges and to identify significant points (intensity bone changes, intersections between screw and crown). Distances between these points are used to compute bone loss. A total of 2920 radiographs were used for training (50%) and testing (50%) the DL process. The mAP@0.5 metric is used for performance evaluation of DL considering periapical/bitewing and screws/crowns in upper and lower jaws, with scores ranging from 0.537 to 0.898 (sufficient because DL only needs an approximation). The IU performance is assessed with 50% of the testing radiographs through the t test statistical method, obtaining p values of 0.0106 (line fitting) and 0.0213 (significant point detection). The IU performance is satisfactory, as these values are in accordance with the statistical average/standard deviation in pixels for line fitting (2.75/1.01) and for significant point detection (2.63/1.28) according to the expert criteria of dentists, who establish the ground-truth lines and significant points. In conclusion, AI methods have good prospects for automatic bone loss detection in intraoral radiographs to assist dental specialists in diagnosing peri-implantitis.

Contemporary Role and Applications of Artificial Intelligence in Dentistry

Artificial Intelligence (AI) technologies play a significant role and significantly impact various sectors, including healthcare, engineering, sciences, and smart cities. AI has the potential to improve the quality of patient care and treatment outcomes while minimizing the risk of human error. Artificial Intelligence (AI) is transforming the dental industry, just like it is revolutionizing other sectors. It is used in dentistry to diagnose dental diseases and provide treatment recommendations. Dental professionals are increasingly relying on AI technology to assist in diagnosis, clinical decision-making, treatment planning, and prognosis prediction across ten dental specialties. One of the most significant advantages of AI in dentistry is its ability to analyze vast amounts of data quickly and accurately, providing dental professionals with valuable insights to enhance their decision-making processes. The purpose of this paper is to identify the advancement of artificial intelligence algorithms that have been frequently used in dentistry and assess how well they perform in terms of diagnosis, clinical decision-making, treatment, and prognosis prediction in ten dental specialties; dental public health, endodontics, oral and maxillofacial surgery, oral medicine and pathology, oral & maxillofacial radiology, orthodontics and dentofacial orthopedics, pediatric dentistry, periodontics, prosthodontics, and digital dentistry in general. We will also show the pros and cons of using AI in all dental specialties in different ways. Finally, we will present the limitations of using AI in dentistry, which made it incapable of replacing dental personnel, and dentists, who should consider AI a complimentary benefit and not a threat.

Applications, functions, and accuracy of artificial intelligence in restorative dentistry: A literature review

OBJECTIVE

The applications of artificial intelligence (AI) are increasing in restorative dentistry; however, the AI performance is unclear for dental professionals. The purpose of this narrative review was to evaluate the applications, functions, and accuracy of AI in diverse aspects of restorative dentistry including caries detection, tooth preparation margin detection, tooth restoration design, metal structure casting, dental restoration/implant detection, removable partial denture design, and tooth shade determination.

OVERVIEW

An electronic search was performed on Medline/PubMed, Embase, Web of Science, Cochrane, Scopus, and Google Scholar databases. English-language articles, published from January 1, 2000, to March 1, 2022, relevant to the aforementioned aspects were selected using the key terms of artificial intelligence, machine learning, deep learning, artificial neural networks, convolutional neural networks, clustering, soft computing, automated planning, computational learning, computer vision, and automated reasoning as inclusion criteria. A manual search was also performed. Therefore, 157 articles were included, reviewed, and discussed.

CONCLUSIONS

Based on the current literature, the AI models have shown promising performance in the mentioned aspects when being compared with traditional approaches in terms of accuracy; however, as these models are still in development, more studies are required to validate their accuracy and apply them to routine clinical practice.

CLINICAL SIGNIFICANCE

AI with its specific functions has shown successful applications with acceptable accuracy in diverse aspects of restorative dentistry. The understanding of these functions may lead to novel applications with optimal accuracy for AI in restorative dentistry.

Artificial intelligence in forensic medicine and forensic dentistry

This review article aims to highlight the current possibilities for applying Artificial Intelligence in modern forensic medicine and forensic dentistry and present the advantages and disadvantages of its use. For this purpose, the relevant academic literature was searched using PubMed, Web of Science and Scopus. The application of Artificial Intelligence in forensic medicine and forensic dentistry is still in its early stages. However, the possibilities are great, and the future will show what is applicable in daily practice. Artificial Intelligence will improve the accuracy and efficiency of work in forensic medicine and forensic dentistry; it can automate some tasks; and enhance the quality of evidence. Disadvantages of the application of Artificial Intelligence may be related to discrimination, transparency, accountability, privacy, security, ethics and others. Artificial Intelligence systems should be used as a support tool, not as a replacement for forensic experts.

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.

Deep learning for osteoarthritis classification in temporomandibular joint

OBJECTIVES

This study aimed to develop a diagnostic support tool using pretrained models for classifying panoramic images of the temporomandibular joint (TMJ) into normal and osteoarthritis (OA) cases.

SUBJECTS AND METHODS

A total of 858 panoramic images of the TMJ (395 normal and 463 TMJ-OA) were obtained from 518 individuals from January 2015 to December 2018. The data were randomly divided into training, validation, and testing sets (6:2:2). We used pretrained Resnet152 and EfficientNet-B7 as transfer learning models. The accuracy, specificity, sensitivity, area under the curve, and gradient-weighted class activation mapping (grad-CAM) of both trained models were evaluated. The performances of the trained models were compared to that of dentists (both TMD specialists and general dentists).

RESULTS

The classification accuracies of ResNet-152 and EfficientNet-B7 were 0.87 and 0.88, respectively. The trained models exhibited the highest accuracy in OA classification. In the grad-CAM analysis, the trained models focused on specific areas in osteoarthritis images where erosion or osteophyte were observed.

CONCLUSIONS

The artificial intelligence model improved the diagnostic power of TMJ-OA when trained with two-dimensional panoramic condyle images and can be effectively applied by dentists as a screening diagnostic tool for TMJ-OA.

Automated deep learning for classification of dental implant radiographs using a large multi-center dataset

This study aimed to evaluate the accuracy of automated deep learning (DL) algorithm for identifying and classifying various types of dental implant systems (DIS) using a large-scale multicenter dataset. Dental implant radiographs of pos-implant surgery were collected from five college dental hospitals and 10 private dental clinics, and validated by the National Information Society Agency and the Korean Academy of Oral and Maxillofacial Implantology. The dataset contained a total of 156,965 panoramic and periapical radiographic images and comprised 10 manufacturers and 27 different types of DIS. The accuracy, precision, recall, F1 score, and confusion matrix were calculated to evaluate the classification performance of the automated DL algorithm. The performance metrics of the automated DL based on accuracy, precision, recall, and F1 score for 116,756 panoramic and 40,209 periapical radiographic images were 88.53%, 85.70%, 82.30%, and 84.00%, respectively. Using only panoramic images, the DL algorithm achieved 87.89% accuracy, 85.20% precision, 81.10% recall, and 83.10% F1 score, whereas the corresponding values using only periapical images achieved 86.87% accuracy, 84.40% precision, 81.70% recall, and 83.00% F1 score, respectively. Within the study limitations, automated DL shows a reliable classification accuracy based on large-scale and comprehensive datasets. Moreover, we observed no statistically significant difference in accuracy performance between the panoramic and periapical images. The clinical feasibility of the automated DL algorithm requires further confirmation using additional clinical datasets.

Artificial Intelligence in Periodontology: A Scoping Review

Artificial intelligence (AI) is the development of computer systems whereby machines can mimic human actions. This is increasingly used as an assistive tool to help clinicians diagnose and treat diseases. Periodontitis is one of the most common diseases worldwide, causing the destruction and loss of the supporting tissues of the teeth. This study aims to assess current literature describing the effect AI has on the diagnosis and epidemiology of this disease. Extensive searches were performed in April 2022, including studies where AI was employed as the independent variable in the assessment, diagnosis, or treatment of patients with periodontitis. A total of 401 articles were identified for abstract screening after duplicates were removed. In total, 293 texts were excluded, leaving 108 for full-text assessment with 50 included for final synthesis. A broad selection of articles was included, with the majority using visual imaging as the input data field, where the mean number of utilised images was 1666 (median 499). There has been a marked increase in the number of studies published in this field over the last decade. However, reporting outcomes remains heterogeneous because of the variety of statistical tests available for analysis. Efforts should be made to standardise methodologies and reporting in order to ensure that meaningful comparisons can be drawn.

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

STATEMENT OF PROBLEM

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

PURPOSE

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

MATERIAL AND METHODS

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

RESULTS

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

CONCLUSIONS

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

Artificial intelligence applications in implant dentistry: A systematic review

STATEMENT OF PROBLEM

Artificial intelligence (AI) applications are growing in dental implant procedures. The current expansion and performance of AI models in implant dentistry applications have not yet been systematically documented and analyzed.

PURPOSE

The purpose of this systematic review was to assess the performance of AI models in implant dentistry for implant type recognition, implant success prediction by using patient risk factors and ontology criteria, and implant design optimization combining finite element analysis (FEA) calculations and AI models.

MATERIAL AND METHODS

An electronic systematic review was completed in 5 databases: MEDLINE/PubMed, EMBASE, World of Science, Cochrane, and Scopus. A manual search was also conducted. Peer-reviewed studies that developed AI models for implant type recognition, implant success prediction, and implant design optimization were included. The search strategy included articles published until February 21, 2021. Two investigators independently evaluated the quality of the studies by applying the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Quasi-Experimental Studies (nonrandomized experimental studies). A third investigator was consulted to resolve lack of consensus.

RESULTS

Seventeen articles were included: 7 investigations analyzed AI models for implant type recognition, 7 studies included AI prediction models for implant success forecast, and 3 studies evaluated AI models for optimization of implant designs. The AI models developed to recognize implant type by using periapical and panoramic images obtained an overall accuracy outcome ranging from 93.8% to 98%. The models to predict osteointegration success or implant success by using different input data varied among the studies, ranging from 62.4% to 80.5%. Finally, the studies that developed AI models to optimize implant designs seem to agree on the applicability of AI models to improve the design of dental implants. This improvement includes minimizing the stress at the implant-bone interface by 36.6% compared with the finite element model; optimizing the implant design porosity, length, and diameter to improve the finite element calculations; or accurately determining the elastic modulus of the implant-bone interface.

CONCLUSIONS

AI models for implant type recognition, implant success prediction, and implant design optimization have demonstrated great potential but are still in development. Additional studies are indispensable to the further development and assessment of the clinical performance of AI models for those implant dentistry applications reviewed.

Machine Learning in Dentistry: A Scoping Review

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

Accurate detection for dental implant and peri-implant tissue by transfer learning of faster R-CNN: a diagnostic accuracy study

BACKGROUND

The diagnosis of dental implants and the periapical tissues using periapical radiographs is crucial. Recently, artificial intelligence has shown a rapid advancement in the field of radiographic imaging.

PURPOSE

This study attempted to detect dental implants and peri-implant tissues by using a deep learning method known as object detection on the implant image of periapical radiographs.

METHODS

After implant treatment, the periapical images were collected and data were processed by labeling the dental implant and peri-implant tissue together in the images. Next, 300 images of the periapical radiographs were split into 80:20 ratio (i.e. 80% of the data were used for training the model while 20% were used for testing the model). These were evaluated using an object detection model known as Faster R-CNN, which simultaneously performs classification and localization. This model was evaluated on the classification performance using metrics, including precision, recall, and F1 score. Additionally, in order to assess the localization performance, an evaluation through intersection over union (IoU) was utilized, and, Average Precision (AP) was used to assess both the classification and localization performance.

RESULTS

Considering the classification performance, precision = 0.977, recall = 0.992, and F1 score = 0.984 were derived. The indicator of localization was derived as mean IoU = 0.907. On the other hand, considering the indicators of both classification and localization performance, AP showed an object detection level of AP@0.5 = 0.996 and AP@0.75 = 0.967.

CONCLUSION

Thus, the implementation of Faster R-CNN model for object detection on 300 periapical radiographic images including dental implants, resulted in high-quality object detection for dental implants and peri-implant tissues.

Advancements in Dentistry with Artificial Intelligence: Current Clinical Applications and Future Perspectives

Artificial intelligence has been widely used in the field of dentistry in recent years. The present study highlights current advances and limitations in integrating artificial intelligence, machine learning, and deep learning in subfields of dentistry including periodontology, endodontics, orthodontics, restorative dentistry, and oral pathology. This article aims to provide a systematic review of current clinical applications of artificial intelligence within different fields of dentistry. The preferred reporting items for systematic reviews (PRISMA) statement was used as a formal guideline for data collection. Data was obtained from research studies for 2009-2022. The analysis included a total of 55 papers from Google Scholar, IEEE, PubMed, and Scopus databases. Results show that artificial intelligence has the potential to improve dental care, disease diagnosis and prognosis, treatment planning, and risk assessment. Finally, this study highlights the limitations of the analyzed studies and provides future directions to improve dental care.

Deep-learning approach for caries detection and segmentation on dental bitewing radiographs

OBJECTIVES

The aim of this study is to recommend an automatic caries detection and segmentation model based on the Convolutional Neural Network (CNN) algorithms in dental bitewing radiographs using VGG-16 and U-Net architecture and evaluate the clinical performance of the model comparing to human observer.

METHODS

A total of 621 anonymized bitewing radiographs were used to progress the Artificial Intelligence (AI) system (CranioCatch, Eskisehir, Turkey) for the detection and segmentation of caries lesions. The radiographs were obtained from the Radiology Archive of the Department of Oral and Maxillofacial Radiology of the Faculty of Dentistry of Ordu University. VGG-16 and U-Net implemented with PyTorch models were used for the detection and segmentation of caries lesions, respectively.

RESULTS

The sensitivity, precision, and F-measure rates for caries detection and caries segmentation were 0.84, 0.81; 0.84, 0.86; and 0.84, 0.84, respectively. Comparing to 5 different experienced observers and AI models on external radiographic dataset, AI models showed superiority to assistant specialists.

CONCLUSION

CNN-based AI algorithms can have the potential to detect and segmentation of dental caries accurately and effectively in bitewing radiographs. AI algorithms based on the deep-learning method have the potential to assist clinicians in routine clinical practice for quickly and reliably detecting the tooth caries. The use of these algorithms in clinical practice can provide to important benefit to physicians as a clinical decision support system in dentistry.

Randomized clinical trial to evaluate the efficacy and safety of two types of sandblasted with large-grit and acid-etched surface implants with different surface roughness

Objectives

This study aims to evaluate the efficacy and safety of two types of sandblasted with large-grit and acid-etched (SLA) surface implants with different surface roughness.

Patients and Methods This study was conducted based on a clinical record review of 55 patients (mean age, 53.00 years). A total of 80 SLA surface implants was placed. Among the 80 implants, 38 implants placed in 29 subjects had surface roughness (Ra) of 3.09 µm (test group, TG), while the other 42 implants placed in 31 subjects had a surface roughness (Ra) of 2.50 µm (control group, CG). A comparison was made of implant primary/secondary stability; success and survival rates; marginal bone loss; and soft tissue assessment including probing pocket depth (PPD), plaque index (PI), gingival index (GI), and bleeding on probing (BOP) between the groups at 1 year after implant placement.

Results

Among the implants that were initially registered, 1 from the TG and 4 from the CG dropped out, leaving 37 implants in the TG and 38 implants in the CG to be traced and analyzed. Although 1 TG case showed unstable primary stability, all cases showed stable secondary stability. Success and survival rates at 1 year after implant placement were 100% in both groups. Marginal bone loss was 0.07 mm and 0.00 mm for the TG and CG, respectively, but the difference was not significant. Among the several parameters for evaluation of soft tissue, the TG showed lower PI at 1 year after implant placement (TG=0.00, CG=0.29; P=0.0004), while the remaining categories showed no significant difference between the groups.

Conclusion

This study shows that the two types of SLA implants with different surface roughness have no difference in efficacy or safety. Therefore, both of the implants can be used safely and with promising outcomes.

Automatic Classification System for Periapical Lesions in Cone-Beam Computed Tomography

Imaging examinations are of remarkable importance for diagnostic support in Dentistry. Imaging techniques allow analysis of dental and maxillofacial tissues (e.g., bone, dentine, and enamel) that are inaccessible through clinical examination, which aids in the diagnosis of diseases as well as treatment planning. The analysis of imaging exams is not trivial; so, it is usually performed by oral and maxillofacial radiologists. The increasing demand for imaging examinations motivates the development of an automatic classification system for diagnostic support, as proposed in this paper, in which we aim to classify teeth as healthy or with endodontic lesion. The classification system was developed based on a Siamese Network combined with the use of convolutional neural networks with transfer learning for VGG-16 and DenseNet-121 networks. For this purpose, a database with 1000 sagittal and coronal sections of cone-beam CT scans was used. The results in terms of accuracy, recall, precision, specificity, and F1-score show that the proposed system has a satisfactory classification performance. The innovative automatic classification system led to an accuracy of about 70%. The work is pioneer since, to the authors knowledge, no other previous work has used a Siamese Network for the purpose of classifying teeth as healthy or with endodontic lesion, based on cone-beam computed tomography images.

Is attention branch network effective in classifying dental implants from panoramic radiograph images by deep learning?

Attention mechanism, which is a means of determining which part of the forced data is emphasized, has attracted attention in various fields of deep learning in recent years. The purpose of this study was to evaluate the performance of the attention branch network (ABN) for implant classification using convolutional neural networks (CNNs). The data consisted of 10191 dental implant images from 13 implant brands that cropped the site, including dental implants as pretreatment, from digital panoramic radiographs of patients who underwent surgery at Kagawa Prefectural Central Hospital between 2005 and 2021. ResNet 18, 50, and 152 were evaluated as CNN models that were compared with and without the ABN. We used accuracy, precision, recall, specificity, F1 score, and area under the receiver operating characteristics curve as performance metrics. We also performed statistical and effect size evaluations of the 30-time performance metrics of the simple CNNs and the ABN model. ResNet18 with ABN significantly improved the dental implant classification performance for all the performance metrics. Effect sizes were equivalent to “Huge” for all performance metrics. In contrast, the classification performance of ResNet50 and 152 deteriorated by adding the attention mechanism. ResNet18 showed considerably high compatibility with the ABN model in dental implant classification (AUC = 0.9993) despite the small number of parameters. The limitation of this study is that only ResNet was verified as a CNN; further studies are required for other CNN models.

Deep learning in periodontology and oral implantology: A scoping review

Deep learning (DL) has been employed for a wide range of tasks in dentistry. We aimed to systematically review studies employing DL for periodontal and implantological purposes. A systematic electronic search was conducted on four databases (Medline via PubMed, Google Scholar, Scopus, and Embase) and a repository (ArXiv) for publications after 2010, without any limitation on language. In the present review, we included studies that reported deep learning models' performance on periodontal or oral implantological tasks. Given the heterogeneities in the included studies, no meta-analysis was performed. The risk of bias was assessed using the QUADAS-2 tool. We included 47 studies: focusing on imaging data (n = 20) and non-imaging data in periodontology (n = 12), or dental implantology (n = 15). The detection of periodontitis and gingivitis or periodontal bone loss, the classification of dental implant systems, or the prediction of treatment outcomes in periodontology and implantology were major use cases. The performance of the models was generally high. However, it varied given the employed methods (which includes various types of convolutional neural networks (CNN) and multi-layered perceptron (MLP)), the variety in specific modeling tasks, as well as the chosen and reported outcomes, outcome measures and outcome level. Only a few studies (n = 7) showed a low risk of bias across all assessed domains. A growing number of studies evaluated DL for periodontal or implantological objectives. Heterogeneity in study design, poor reporting and a high risk of bias severely limit the comparability of studies and the robustness of the overall evidence.

Diagnosis of Tooth Prognosis Using Artificial Intelligence

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

Multispectral Image under Tissue Classification Algorithm in Screening of Cervical Cancer

The objectives of this study were to improve the efficiency and accuracy of early clinical diagnosis of cervical cancer and to explore the application of tissue classification algorithm combined with multispectral imaging in screening of cervical cancer. 50 patients with suspected cervical cancer were selected. Firstly, the multispectral imaging technology was used to collect the multispectral images of the cervical tissues of 50 patients under the conventional white light waveband, the narrowband green light waveband, and the narrowband blue light waveband. Secondly, the collected multispectral images were fused, and then the tissue classification algorithm was used to segment the diseased area according to the difference between the cervical tissues without lesions and the cervical tissues with lesions. The difference in the contrast and other characteristics of the multiband spectrum fusion image would segment the diseased area, which was compared with the results of the disease examination. The average gradient, standard deviation (SD), and image entropy were adopted to evaluate the image quality, and the sensitivity and specificity were selected to evaluate the clinical application value of discussed method. The fused spectral image was compared with the image without lesions, it was found that there was a clear difference, and the fused multispectral image showed a contrast of 0.7549, which was also higher than that before fusion (0.4716), showing statistical difference (P < 0.05). The average gradient, SD, and image entropy of the multispectral image assisted by the tissue classification algorithm were 2.0765, 65.2579, and 4.974, respectively, showing statistical difference (P < 0.05). Compared with the three reported indicators, the values of the algorithm in this study were higher. The sensitivity and specificity of the multispectral image with the tissue classification algorithm were 85.3% and 70.8%, respectively, which were both greater than those of the image without the algorithm. It showed that the multispectral image assisted by tissue classification algorithm can effectively screen the cervical cancer and can quickly, efficiently, and safely segment the cervical tissue from the lesion area and the nonlesion area. The segmentation result was the same as that of the doctor's disease examination, indicating that it showed high clinical application value. This provided an effective reference for the clinical application of multispectral imaging technology assisted by tissue classification algorithm in the early screening and diagnosis of cervical cancer.

Transfer learning for medical image classification: a literature review

BACKGROUND

Transfer learning (TL) with convolutional neural networks aims to improve performances on a new task by leveraging the knowledge of similar tasks learned in advance. It has made a major contribution to medical image analysis as it overcomes the data scarcity problem as well as it saves time and hardware resources. However, transfer learning has been arbitrarily configured in the majority of studies. This review paper attempts to provide guidance for selecting a model and TL approaches for the medical image classification task.

METHODS

425 peer-reviewed articles were retrieved from two databases, PubMed and Web of Science, published in English, up until December 31, 2020. Articles were assessed by two independent reviewers, with the aid of a third reviewer in the case of discrepancies. We followed the PRISMA guidelines for the paper selection and 121 studies were regarded as eligible for the scope of this review. We investigated articles focused on selecting backbone models and TL approaches including feature extractor, feature extractor hybrid, fine-tuning and fine-tuning from scratch.

RESULTS

The majority of studies (n = 57) empirically evaluated multiple models followed by deep models (n = 33) and shallow (n = 24) models. Inception, one of the deep models, was the most employed in literature (n = 26). With respect to the TL, the majority of studies (n = 46) empirically benchmarked multiple approaches to identify the optimal configuration. The rest of the studies applied only a single approach for which feature extractor (n = 38) and fine-tuning from scratch (n = 27) were the two most favored approaches. Only a few studies applied feature extractor hybrid (n = 7) and fine-tuning (n = 3) with pretrained models.

CONCLUSION

The investigated studies demonstrated the efficacy of transfer learning despite the data scarcity. We encourage data scientists and practitioners to use deep models (e.g. ResNet or Inception) as feature extractors, which can save computational costs and time without degrading the predictive power.

Artificial Intelligence-Based Solution in Personalized Computer-Aided Arthroscopy of Shoulder Prostheses

Background: Early recognition of prostheses before reoperation can reduce perioperative morbidity and mortality. Because of the intricacy of the shoulder biomechanics, accurate classification of implant models before surgery is fundamental for planning the correct medical procedure and setting apparatus for personalized medicine. Expert surgeons usually use X-ray images of prostheses to set the patient-specific apparatus. However, this subjective method is time-consuming and prone to errors. Method: As an alternative, artificial intelligence has played a vital role in orthopedic surgery and clinical decision-making for accurate prosthesis placement. In this study, three different deep learning-based frameworks are proposed to identify different types of shoulder implants in X-ray scans. We mainly propose an efficient ensemble network called the Inception Mobile Fully-Connected Convolutional Network (IMFC-Net), which is comprised of our two designed convolutional neural networks and a classifier. To evaluate the performance of the IMFC-Net and state-of-the-art models, experiments were performed with a public data set of 597 de-identified patients (597 shoulder implants). Moreover, to demonstrate the generalizability of IMFC-Net, experiments were performed with two augmentation techniques and without augmentation, in which our model ranked first, with a considerable difference from the comparison models. A gradient-weighted class activation map technique was also used to find distinct implant characteristics needed for IMFC-Net classification decisions. Results: The results confirmed that the proposed IMFC-Net model yielded an average accuracy of 89.09%, a precision rate of 89.54%, a recall rate of 86.57%, and an F1.score of 87.94%, which were higher than those of the comparison models. Conclusion: The proposed model is efficient and can minimize the revision complexities of implants.

Transfer learning in a deep convolutional neural network for implant fixture classification: A pilot study

Purpose

This study aimed to evaluate the performance of transfer learning in a deep convolutional neural network for classifying implant fixtures.

Materials and Methods

Periapical radiographs of implant fixtures obtained using the Superline (Dentium Co. Ltd., Seoul, Korea), TS III (Osstem Implant Co. Ltd., Seoul, Korea), and Bone Level Implant (Institut Straumann AG, Basel, Switzerland) systems were selected from patients who underwent dental implant treatment. All 355 implant fixtures comprised the total dataset and were annotated with the name of the system. The total dataset was split into a training dataset and a test dataset at a ratio of 8 to 2, respectively. YOLOv3 (You Only Look Once version 3, available at https://pjreddie.com/darknet/yolo/), a deep convolutional neural network that has been pretrained with a large image dataset of objects, was used to train the model to classify fixtures in periapical images, in a process called transfer learning. This network was trained with the training dataset for 100, 200, and 300 epochs. Using the test dataset, the performance of the network was evaluated in terms of sensitivity, specificity, and accuracy.

Results

When YOLOv3 was trained for 200 epochs, the sensitivity, specificity, accuracy, and confidence score were the highest for all systems, with overall results of 94.4%, 97.9%, 96.7%, and 0.75, respectively. The network showed the best performance in classifying Bone Level Implant fixtures, with 100.0% sensitivity, specificity, and accuracy.

Conclusion

Through transfer learning, high performance could be achieved with YOLOv3, even using a small amount of data.

Current applications and development of artificial intelligence for digital dental radiography

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

Machine learning for identification of dental implant systems based on shape - A descriptive study

Aim:

To evaluate the efficacy of machine learning in identification of dental implant systems from panoramic radiographs based on the shape.

Settings and Design:

In vitro–Descriptive study

Materials and Methods:

A Dataset of digital panoramic radiographs of three dental implant systems were obtained. The images were divided into two datasets: one for training and another for testing of the machine learning models. Machine learning algorithms namely, support vector machine, logistic regression, K Nearest neighbor and X boost classifiers were trained to classify implant systems from radiographs, based on the shape using Hu and Eigen values. Performance of algorithms was evaluated by its classification accuracy using the test dataset.

Statistical Analysis Used:

Accuracy and recover operating characteristic (ROC) curve were calculated to analyze the performance of the model.

Results:

The classifiers tested in the study were able to identify the implant systems with an average accuracy of 0.67. Of the classifiers trained, logistic regression showed best overall performance followed by SVM, KNN and X boost classifiers.

Conclusions:

Machine learning models tested in the study are proficient enough to identify dental implant systems; hence we are proposing machine learning as a method for implant identification and can be generalized with a larger dataset and more cross sectional studies.

The Use and Performance of Artificial Intelligence in Prosthodontics: A Systematic Review

(1) Background: The rapid pace of digital development in everyday life is also reflected in dentistry, including the emergence of the first systems based on artificial intelligence (AI). This systematic review focused on the recent scientific literature and provides an overview of the application of AI in the dental discipline of prosthodontics. (2) Method: According to a modified PICO-strategy, an electronic (MEDLINE, EMBASE, CENTRAL) and manual search up to 30 June 2021 was carried out for the literature published in the last five years reporting the use of AI in the field of prosthodontics. (3) Results: 560 titles were screened, of which 30 abstracts and 16 full texts were selected for further review. Seven studies met the inclusion criteria and were analyzed. Most of the identified studies reported the training and application of an AI system (n = 6) or explored the function of an intrinsic AI system in a CAD software (n = 1). (4) Conclusions: While the number of included studies reporting the use of AI was relatively low, the summary of the obtained findings by the included studies represents the latest AI developments in prosthodontics demonstrating its application for automated diagnostics, as a predictive measure, and as a classification or identification tool. In the future, AI technologies will likely be used for collecting, processing, and organizing patient-related datasets to provide patient-centered, individualized dental treatment.

Scope and challenges of machine learning-based diagnosis and prognosis in clinical dentistry: A literature review

BACKGROUND

Machine learning (ML) has emerged as a branch of artificial intelligence dealing with the analysis of large amounts of data. The applications of ML algorithms have also expanded to health care, including dentistry. Recent advances in this field point to future improvements in diagnostic techniques and the prognosis of various diseases of the teeth and other maxillofacial structures.

AIM

The aim of this literature review is to describe the basis for ML being applied to different dental sub-fields in recent years, to identify typical algorithms used in the studies, and to summarize the scope and challenges of using these techniques in dental clinical practice.

RELEVANCE FOR PATIENTS

The proficiency of emerging technologies that have begun to show encouraging results in the diagnosis and prognosis of oral diseases can improve the precision in the selection of treatment for patients. It is necessary to understand the challenges associated with using these tools to effectively use them in dental services and ensure a higher quality of care for patients.

Multi-Task Deep Learning Model for Classification of Dental Implant Brand and Treatment Stage Using Dental Panoramic Radiograph Images

It is necessary to accurately identify dental implant brands and the stage of treatment to ensure efficient care. Thus, the purpose of this study was to use multi-task deep learning to investigate a classifier that categorizes implant brands and treatment stages from dental panoramic radiographic images. For objective labeling, 9767 dental implant images of 12 implant brands and treatment stages were obtained from the digital panoramic radiographs of patients who underwent procedures at Kagawa Prefectural Central Hospital, Japan, between 2005 and 2020. Five deep convolutional neural network (CNN) models (ResNet18, 34, 50, 101 and 152) were evaluated. The accuracy, precision, recall, specificity, F1 score, and area under the curve score were calculated for each CNN. We also compared the multi-task and single-task accuracies of brand classification and implant treatment stage classification. Our analysis revealed that the larger the number of parameters and the deeper the network, the better the performance for both classifications. Multi-tasking significantly improved brand classification on all performance indicators, except recall, and significantly improved all metrics in treatment phase classification. Using CNNs conferred high validity in the classification of dental implant brands and treatment stages. Furthermore, multi-task learning facilitated analysis accuracy.

Artificial Intelligence in Fractured Dental Implant Detection and Classification: Evaluation Using Dataset from Two Dental Hospitals

Fracture of a dental implant (DI) is a rare mechanical complication that is a critical cause of DI failure and explantation. The purpose of this study was to evaluate the reliability and validity of a three different deep convolutional neural network (DCNN) architectures (VGGNet-19, GoogLeNet Inception-v3, and automated DCNN) for the detection and classification of fractured DI using panoramic and periapical radiographic images. A total of 21,398 DIs were reviewed at two dental hospitals, and 251 intact and 194 fractured DI radiographic images were identified and included as the dataset in this study. All three DCNN architectures achieved a fractured DI detection and classification accuracy of over 0.80 AUC. In particular, automated DCNN architecture using periapical images showed the highest and most reliable detection (AUC = 0.984, 95% CI = 0.900–1.000) and classification (AUC = 0.869, 95% CI = 0.778–0.929) accuracy performance compared to fine-tuned and pre-trained VGGNet-19 and GoogLeNet Inception-v3 architectures. The three DCNN architectures showed acceptable accuracy in the detection and classification of fractured DIs, with the best accuracy performance achieved by the automated DCNN architecture using only periapical images.