Dental Diagnosis Research Articles

Pregled nedavnih dostignuća u dečjoj stomatologiji

Introduction: As a result of developments in technology, materials, and illness knowledge, paediatric dentistry has experienced major changes. When dealing with children, comfort, speed, safety, and conservative therapies are paramount. Of course, this also applies to adults, but getting youngsters started with dental appointments and making the experience stress-free for both patients and parents is extremely vital. The introduction and use of new technology caused a paradigm change in the practice of dentistry. This has been marked by the development of new and superior restorative materials that allow us to remove less tooth structure. We have now achieved the therapeutic goals discovered in giving more minimally invasive dentistry. Early dental diagnosis has become increasingly important in recent years. Early detection of dental caries has grown in relevance in recent years as conservative dentistry has remained at the forefront. The dentist should emphasize early identification of dental caries and the least invasive treatment alternatives, especially in paediatric patients. Traditional caries detection methods frequently miss early enamel lesions that have not developed into cavitation. As a result, several innovative strategies for detecting cavities early have been developed. Aim: The purpose of the current literature is to provide broad information regarding new restorative materials and caries detection technologies and summarize recent advances in paediatric dentistry. Conclusion: This overview highlighted a huge variety of current equipment that is either now used or can be utilized in dental diagnostics. Some of these devices are rather pricey and take up a lot of room. Technology evolves so quickly that it is impossible to stay up. As a result, possibly the best answer for each pedodontist or pedodontics clinic is to select the equipment with the greatest cost benefit.

A progressive framework for tooth and substructure segmentation from cone-beam CT images

Background:Accurate segmentation of individual tooth and their substructures including enamel, pulp, and dentin from cone-beam computed tomography (CBCT) images is essential for dental diagnosis and treatment planning in digital dentistry. Existing methods for tooth segmentation based on CBCT images have achieved substantial progress; however, techniques for further segmentation into substructures are yet to be developed. Purpose:We aim to propose a novel three-stage progressive deep-learning-based framework for automatically segmenting 3D tooth from CBCT images, focusing on finer substructures, i.e., enamel, pulp, and dentin. Methods:In this paper, we first detect each tooth using its centroid by a clustering scheme, which efficiently determines each tooth detection by applying learned displacement vectors from the foreground tooth region. Next, guided by the detected centroid, each tooth proposal, combined with the corresponding tooth map, is processed through our tooth segmentation network. We also present an attention-based hybrid feature fusion mechanism, which provides intricate details of the tooth boundary while maintaining the global tooth shape, thereby enhancing the segmentation process. Additionally, we utilize the skeleton of the tooth as a guide for subsequent substructure segmentation. Results:Our algorithm is extensively evaluated on a collected dataset of 314 patients, and the extensive comparison and ablation studies demonstrate superior segmentation results of our approach. Conclusions:Our proposed method can automatically segment tooth and finer substructures from CBCT images, underlining its potential applicability for clinical diagnosis and surgical treatment.

Dental bitewing radiographs segmentation using deep learning-based convolutional neural network algorithms.

Dental radiographs, particularly bitewing radiographs, are widely used in dental diagnosis and treatment Dentalimage segmentation is difficult for various reasons, such as intricate structures, low contrast, noise, roughness, andunclear borders, resulting in poor image quality. Recent developments in deep learning models have improvedperformance in analyzing dental images. In this research, our primary objective is to determine the most effectivesegmentation technique for bitewing radiographs based on different metrics: accuracy, training time, and thenumber of training parameters as a reflection of architectural cost. In this research, we employ several deep learning models, namely Resnet-18, Resnet-50, Xception, InceptionResnet v2, and Mobilenetv2, to segment bitewing radiographs. The process begins by importing the radiographsinto MATLAB®(MathWorks Inc), where the images are first improved, then segmented using the graph cut methodbased on regions to produce a binary mask that distinguishes the background from the original X-ray. The deep learning models were trained on 298 and 99 radiograph training and validation sets and were evaluatedusing 99 images from the testing set. We also compare the segmentation model using several criteria, includingaccuracy, speed, and size, to determine which network is superior. Furthermore, we compare our findings with priorresearch to provide a comprehensive understanding of the advancements made in dental image segmentation. Theaccurate segmentation achieved was 93.67% and 94.42% by the Resnet-18 and Resnet-50 models, respectively. This research advances dental image analysis and facilitates more accurate diagnoses and treatment planning bydetermining the best segmentation technique. The outcomes of this study can guide researchers and practitionersin selecting appropriate segmentation methods for practical dental image analysis.

Studying the spatial flow characteristics of droplets induced by ultrasonic tooth-cleaning machines

The use of an ultrasonic scaler in the dental diagnosis and treatment process can produce a large number of droplets due to splashing, which can be transformed into droplet nuclei in the form of aerosols suspended in the air of the clinic, and droplets and aerosols may carry pathogenic microorganisms that pose a great threat to the health of clinical staff and patients. This paper takes a dental clinic as the research object and adopts experimental and numerical simulation methods to study the splashing droplet flow during ultrasonic dental cleaning surgery. It explored the similarities and differences in the spatial flow characteristics of droplets caused by different operation modes in the ultrasonic dental cleaning process. The results showed that the large droplets (particle size >100 μm) caused by the ultrasonic dental cleaning machine could be spread to the dangerous range of the patient's surroundings at 2.5 m. The droplets could be spattered to the patient's oral cavity at 1.5 m above the room, and the room was contaminated with a space of 17.5 m3. The droplets were concentrated, and the smaller droplets particles were more concentrated in the 0.75 m of the patient's oral cavity. The concentration of the small droplet particles (particle size 1–100 μm) of the splash height can be up to the clinic's ceiling in the air for a long time, and the contamination range can be expanded to the entire clinic. This study will provide strong guidance for developing microbial adsorption and elimination equipment for dental clinics and formulating new prevention and control opinions for dental clinics.

ИНФЕКЦИОННЫЙ ЭНДОКАРДИТ В ПРАКТИКЕ ВРАЧА-СТОМАТОЛОГА: КЛИНИЧЕСКИЙ СЛУЧАЙ

Subject. The subject of this case report is infective endocarditis.
 Objective. The objective is to describe a clinical case under outpatient-polyclinic conditions at the dentist’s (periodontist’s) office.
 Methodology. Infective endocarditis is one of the most severe coronary vascular diseases associated with high mortality affected by the area and characteristics of infection, the phase of development, the type of a causative agent, the time to start antibacterial therapy, coexisting diseases, the extent of sequelae, surgical treatment and other circumstances. Following the appropriate treatment, more than 70% patients with infective endocarditis achieve recovery. 
 Conclusions. This case report presents clinical manifestations in the oral cavity of a patient with infective endocarditis. The patient did not exhibit specific symptoms of infective endocarditis serving as the basis for the preliminary dental diagnosis and sending the patient to a cardiologist, rheumatologist for examination and treatment. It should be noted that the patient needs the complex treatment of periodontitis, oral cavity sanation, the case follow-up, supportive periodontal therapy and professional oral hygiene on a quarterly basis. These all can significantly improve the patient’s quality of life and longevity.

2267. Evaluation of Antibiotic Prescribing Trends from a Dental Clinic After Implementation of a Dental Antimicrobial Stewardship Initiative

Abstract Background Inappropriate antibiotic prescribing by dentists is as high as 80% and in the U.S., dentists rank third for writing the most antibiotic prescriptions. At NewYork-Presbyterian Brooklyn Methodist (NYP-BMH), the Antimicrobial Stewardship Program (ASP) and Division of Dental Medicine collaborated to create a clinical practice guideline regarding antibiotic use in dentistry that was implemented in May 2021 and is reinforced with annual education to dental prescribers. The purpose of this study is to compare outpatient antibiotic prescribing trends pre- and post-guideline implementation. Methods Patients presenting to NYP-BMH dental clinic for an emergent indication and prescribed an antibiotic from January-April of 2019-2021 (pre) and January-April of 2022 (post) were included. The primary outcome was appropriateness of antibiotic therapy. Secondary outcomes included a comparison of antibiotic selection, dosing and duration of therapy. A subgroup analysis of patients with reported penicillin allergies was conducted to compare alternative agents prescribed. Results A total of 119 patients in the pre and 100 patients in the post-guideline period were included. The primary outcome of appropriate antibiotic prescriptions significantly improved after guideline implementation (pre 45% vs post 64%, p< 0.01). The majority of antibiotic indications were treatment-related and the most common dental diagnosis was caries. The most common antibiotic prescribed in the study was amoxicillin. Compared to pre-guideline, there was significant decreased utilization of penicillin VK (pre 3.4% vs post 0%, p=0.05) and amoxicillin-clavulanate post-guideline (pre 16% vs post 8%, p=0.02). A wide variety of dosing regimens were utilized with more guideline-concordant regimens utilized post-guideline (pre 59% vs post 72%, p=0.04). On average, shorter durations of therapy were prescribed post-guideline (pre 6.8 days vs post 5.7 days, p< 0.02). Penicillin-allergic patients were prescribed less clindamycin after guideline implementation. Conclusion ASP initiatives and collaboration with outpatient dental clinics can improve appropriate antibiotic prescribing. As ASPs look to expand into ambulatory care settings, dental clinics should be prioritized. Disclosures All Authors: No reported disclosures

A New Era of Dental Care: Harnessing Artificial Intelligence for Better Diagnosis and Treatment.

The integration of artificial intelligence (AI) into dental care holds the promise of revolutionizing the field by enhancing the accuracy of dental diagnosis and treatment. This paper explores the impact of AI in dental care, with a focus on its applications in diagnosis, treatment planning, and patient engagement. AI-driven dental imaging and radiography, computer-aided detection and diagnosis of dental conditions, and early disease detection and prevention are discussed in detail. Moreover, the paper delves into how AI assists in personalized treatment planning and provides predictive analytics for dental care. Ethical and privacy considerations, including data security, fairness, and regulatory aspects, are addressed, highlighting the need for a responsible and transparent approach to AI implementation. Finally, the paper underscores the potential for a collaborative partnership between AI and dental professionals to offer the best possible care to patients, making dental care more efficient, patient-centric, and effective. The advent of AI in dentistry presents a remarkable opportunity to improve oral health outcomes, benefiting both patients and the healthcare community.

DeAPIR: Efficient and Enhanced Dental Arch-Guided Panoramic Image Reconstruction from CBCT Data

Dental CBCT and panoramic imaging play a pivotal role in dental diagnosis and treatment planning, alongside the indispensable use of computed tomography (CT) and X-ray imaging in dentistry, particularly for surgical planning. Given the widespread adoption of dental cone beam CT in clinics today, we explore a novel approach in this study—utilizing CT’s three-dimensional (3D) data to reconstruct a two-dimensional (2D) panoramic dental image. This method eliminates the requirement for an extra panoramic scan. In this work, we propose a novel framework to generate an enhanced and extended 2D panoramic view by using the dental arch extracted from 3D CBCT. Our method involves segmenting the patient’s dental arch from their 3D CBCT image by identifying horizontal slices with above-average intensity, followed by morphological operations, including dilation, Gaussian filtering, and skeletonization, to delineate the dental arch line. Additionally, we extend the dental arch beyond the wisdom teeth using quadratic curve fitting. Finally, we employ Maximum Intensity Projection on rotated cubic segments aligned with the dental arch curve to produce captivating panoramic images. The panoramic view produced using our proposed method, when compared to the results obtained from BlueSky and OpenInventor, exhibited superior enhancements and greater accuracy in panoramic visualization.

A Comparison between Saudi Dental Students' Knowledge of CBCT and Intraoral Radiographs: A Cross-Sectional Study

Intraoral radiography is integral to dental diagnosis while cone beam computed tomography (CBCT) provides enhanced 3D visualization. We aimed to assess and compare knowledge regarding CBCT and intraoral radiography among 386 Saudi dental undergraduates via a cross-sectional questionnaire. Nearly all participants successfully identified bitewing (99%), periapical (97%) and CBCT (98%) images, indicating strong familiarity. However, only 6% had clinically utilized CBCT versus 80% using intraoral radiography. Just 14% received CBCT training compared to 65% trained in intraoral radiography. Most appropriately recognized children as the most radiation-sensitive group (47%) and recommended limited CBCT (62%) and intraoral (37%) radiographs monthly per ALARA principles. Root canal treatment (44%) and implant planning (29%) were the most common CBCT referral indications. Key barriers to routine CBCT implementation included high costs (67%), difficulty (16%), and insufficient training (14%). Advantages perceived were reduced radiation (17%), rapid imaging (38%), and lack of film processing (24%). Results highlight excellent conceptual understanding but limited practical experience and training in CBCT compared to intraoral radiography among Saudi dental students. Targeted education programs integrating hands-on CBCT exposure with training in radiation safety and appropriate use are warranted to support optimal future clinical adoption.

Oral panorama reconstruction method based on pre-segmentation and Bezier function

For patients with partial jaw defects, cysts and dental implants, doctors need to take panoramic X-ray films or manually draw dental arch lines to generate Panorama images in order to observe their complete dentition information during oral diagnosis. In order to solve the problems of additional burden for patients to take panoramic X-ray films and time-consuming issue for doctors to manually segment dental arch lines, this paper proposes an automatic panorama reconstruction method based on cone beam computerized tomography (CBCT). The V-network (VNet) is used to pre-segment the teeth and the background to generate the corresponding binary image, and then the Bezier curve is used to define the best dental arch curve to generate the oral panorama. In addition, this research also addressed the issues of mistakenly recognizing the teeth and jaws as dental arches, incomplete coverage of the dental arch area by the generated dental arch lines, and low robustness, providing intelligent methods for dental diagnosis and improve the work efficiency of doctors.

Creating patients: how technology and measurement approaches are misused in diagnosis and convert healthy individuals into TMD patients

The advances made in recent years regarding technological approaches to medical and dental diagnosis are impressive. However, while those tools, procedures, and instruments may produce an improved clinical diagnosis or discover a new disorder, they also can be misused and misinterpreted in various ways. In the field of temporomandibular disorders (TMDs), the very nature of those conditions is similar to common orthopedic problems elsewhere in the body. Yet, beyond imaging of the affected areas, there have been few important new technological approaches to augment the traditional history and examination for a sufficient diagnosis of such problems. The traditional approach is exemplified by the Diagnostic Criteria for Temporomandibular Disorders, which has high inter-examiner reliability and diagnostic validity; translations into over 20 languages allow for widespread use. In contrast and unfortunately, the TMD field is replete with a variety of so-called diagnostic instruments and procedures, which have not been tested for diagnostic validity; these instruments and procedures, through misuse, are capable of complicating a true diagnosis of patients who present with symptoms, while also creating new patients by finding so-called abnormalities in healthy subjects. This paper discusses those technological approaches and their misuse with respect to TMD diagnosis from a critical viewpoint, and the authors argue that there are significant risks for patients if their uncritical implementation becomes accepted and widespread. Therefore, dentists are encouraged to reject the proposed application of such technological approaches to diagnosis of the stomatognathic system.

The Use of Extra-oral Bitewing Radiographs for Treatment Planning in Paediatric Patients

The extra-oral bitewing is becoming increasingly popular among clinicians to help overcome some of the challenges posed when taking intra-oral radiographs for paediatric patients. When combined with clinical examination, this radiographic view provides important diagnostic information in the management of caries in the posterior dentition. In this article, the use of extra-oral bitewings will be explored. The introduction of a decision tree provides the dental practitioner working in either a primary or secondary care setting, a pathway to assist with deciding the optimal radiographic prescription. CPD/Clinical Relevance: Extra-oral bitewings can be a useful radiographic tool for dental caries diagnosis in the mixed dentition.

New bag of features using reinforcement Aquila optimization and weighted Bayesian Gaussian mixture modelling for dental images

AbstractDentistry diseases are worldwide concern with the fact that 5% of medical budget is spent on it. Dental diagnosis requires frequent visits to clinic and multiple personal check‐ups by an expert. This delays the process of diagnosis as well as introduces the danger of oral infection spread. Automation is boon to speed up the process of diagnosis, reduce expert's involvement and help in handling volume of patients. In situations like pandemic as seen in this decade, computer based automatic systems in health have proven their importance and necessity. For dental diagnosis, images are useful tool for better anatomical views and accurate decision of treatment. However, manual dental analysis increases load on dentists for initial check‐up that can be easily performed with automated systems or self‐kits. Oral cavity is a dental illness, if not identified and treated on time may lead to other serious ailments. This work presents a framework that performs tooth image classification for cavity and non‐cavity with new bag of features (NBoF) method. NBoF method is an attempt to improve the performance of bag of features (BoF) using the proposed reinforcement Aquila optimization (RAO) and weighted Bayesian Gaussian mixture modelling (WBGMM). An analysis of the performance of the NBoF using the proposed RAO and WBGMM is conducted using standard metrics. The comparative study of results proves that the proposed NBoF method outperforms the existing state‐of‐the‐art algorithms.

Assessment of Prescription Patterns and Appropriateness of Antibiotics for Prophylaxis in Dental Procedures: A Retrospective Study

There is a lack of data on dentists’ prescribing behaviors when it comes to the correct use of antibiotics in dental treatments. Antibiotic prescriptions made by dentists were analyzed from a diagnostic perspective in a countrywide study. This study retrospectively examined national health data from January 2018 through August 2019, collected through the Prescription Information System. The analysis only included prescriptions for a single illness and at least one systemic antibiotic. Antibiotic prescribing practices and the role of dental specialization and diagnosis were studied. A total of 9,214,956 prescriptions out of 9,293,410 matched the criteria for inclusion. An average course of antibiotic treatment consists of 1.01 pills. Antibiotics were prescribed most frequently for dental caries (16.2% of cases), dental examinations (20.7% of cases), and periapical abscess without sinuses (28.1% of cases). Antibiotics were prescribed for 96.6% of patients for illogical or confusing reasons, whereas just 3.4% were given based on a single, unambiguous diagnosis, such as cellulitis or a mouth abscess. The most commonly prescribed treatment for any medical issue was amoxicillin combined with an enzyme inhibitor (58.6%). Compared to unidentified dental practitioners (58.2%; p = 0.0001), dental specialists in Groups A and B significantly overprescribed amoxicillin plus an enzyme inhibitor (67.0 and 67.8%, respectively; p = 0.0001) (data not shown). The results of the study suggest that dentists routinely and arbitrarily prescribe antibiotics with questionable rationale in the contexts studied. These results emphasize the need for dentists to begin prescribing antibiotics more deliberately and evidence-based. Educational programs, awareness campaigns, and antibiotic stewardship programmes are just some of the interventions that may be implemented to improve prescribing practices and ensure the correct use of antibiotics in dental operations. The results of this countrywide study highlight problems with dentists’ procedures for prescribing dental antibiotics. The paper states that dentists should base their prescription practices on sound reasoning and empirical data. Dentists can help with global efforts to lower antibiotic resistance and improve patient care if they prescribe them in a more reasonable and responsible manner.

Deep Learning-Based Recognition of Periodontitis and Dental Caries in Dental X-ray Images.

Dental X-ray images are important and useful for dentists to diagnose dental diseases. Utilizing deep learning in dental X-ray images can help dentists quickly and accurately identify common dental diseases such as periodontitis and dental caries. This paper applies image processing and deep learning technologies to dental X-ray images to propose a simultaneous recognition method for periodontitis and dental caries. The single-tooth X-ray image is detected by the YOLOv7 object detection technique and cropped from the periapical X-ray image. Then, it is processed through contrast-limited adaptive histogram equalization to enhance the local contrast, and bilateral filtering to eliminate noise while preserving the edge. The deep learning architecture for classification comprises a pre-trained EfficientNet-B0 and fully connected layers that output two labels by the sigmoid activation function for the classification task. The average precision of tooth detection using YOLOv7 is 97.1%. For the recognition of periodontitis, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve is 98.67%, and the AUC of the precision-recall (PR) curve is 98.38%. For the recognition of dental caries, the AUC of the ROC curve is 98.31%, and the AUC of the PR curve is 97.55%. Different from the conventional deep learning-based methods for a single disease such as periodontitis or dental caries, the proposed approach can provide the recognition of both periodontitis and dental caries simultaneously. This recognition method presents good performance in the identification of periodontitis and dental caries, thus facilitating dental diagnosis.

Barriers and Enablers to Implementing Teledentistry From the Perspective of Dental Health Care Professionals: Protocol for a Systematic Quantitative, Qualitative, and Mixed Studies Review.

There is growing literature on the potential of digital technologies for improving access to, ensuring continuity and quality of health care, and to strengthen health systems. Some studies have reported the cost-effectiveness of teledentistry, its reliability for remote dental screening, diagnosis, consultation, and treatment planning. Nonetheless, current evidence suggests that teledentistry implementation faces many challenges and is not yet adopted by dental health care providers (DHCPs). Developing strategies to improve teledentistry adoption requires an understanding of the factors that promote or hinder its successful implementation. This systematic review aims to identify and synthetize barriers and enablers to implementing teledentistry as perceived by DHCPs in their clinical practices, using the Theoretical Domains Framework (TDF) and the Capacity, Opportunity, and Motivation Behavior (COM-B) model. This protocol follows the PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Protocols) checklist. Literature will be searched in the following databases: PubMed, Cochrane Library, Web of Science, CINAHL, Embase, and PsycINFO. We will perform additional searches on Google, Google Scholar, and ProQuest Dissertations & Theses Global, screen the references of the included studies to capture additional relevant studies, and contact the authors of studies if we need more details. We will consider studies using qualitative, quantitative, and mixed methods. There will be no restrictions on the publication date and dental setting. We will include studies published in French, English, and Portuguese. Two independent reviewers will select the study, extract data, and assess methodological quality using the Mixed Methods Appraisal Tool's checklist. Data analysis will include a descriptive and a thematic content analysis. We will synthetize and categorize the barriers and enablers using the TDF and COM-B model and present a narrative synthesis of our results using tables, figures, and quotes. By March 2023, the literature search has retrieved 7355 publications. We will identify the range of barriers and enablers to implementing teledentistry through DHCPs' perspectives. Considering the critical need for theory-based implementation interventions to improve the use of evidence-informed practices, we will synthesize the factors influencing the adoption of teledentistry based on the TDF domains and the 3 essential conditions predicting behavior change in accordance with the COM-B model. As needed, we will include additional determinants if not included in the TDF. We will conduct some subgroups analyses if studies are sufficient. We expect to complete the review by July 2024. This review will provide some insights on the determinants of teledentistry implementation as perceived by DHCPs in dental settings. These findings will cater to patients, families, DHCPs, researchers, academic and professional decision-makers, and policy makers. The results of the systematic review could be used to develop theory-led interventions in improving teledentistry implementation. PROSPERO CRD42021293376; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=293376. PRR1-10.2196/44218.

Impact of Selective median filter on dental caries classification system using deep learning models

<p>Accurate classification of dental caries is crucial for effective oral healthcare. Filters help to increase exposure of the picture taken for the investigation without degrading image quality. Selective median filter is the chosen preprocessing technique that helps to reduce the noise present in the captured image. Dental caries classification system is a model used to detect the presence of cavity in the given input image. Dental caries classification system is evolved with the use of conventional techniques to artificial neural network. Deep learning models are the artificial neural network models that can able to learn the features from the raw images available in the dataset. If this raw image has noise, then it severely affects the accuracy of the deep learning models. In this paper, impact of the preprocessing technique on the classification accuracy is analyzed. Initially, raw images are taken for training on deep learning models without applying any preprocessing technique. This study investigates the impact of Selective median filtering on a dental caries classification system using deep learning models. The motivation behind this research is to enhance the accuracy and reliability of dental caries diagnosis by reducing noise, removing artifacts, and preserving important details in dental radiographs. Experimental results demonstrate that the implementation of Selective median filtering significantly improves the performance of the deep learning model. The hybrid neural network (HNN) classifier achieves an accuracy of 96.15% with Selective median filtering, outperforming the accuracy of 85.07% without preprocessing. The study highlights the theoretical contribution of Selective median filtering in enhancing dental caries classification systems and emphasizes the practical implications for dental clinics, offering improved diagnostic capabilities and better patient outcomes.</p>

Transforming Dental Caries Diagnosis Through Artificial Intelligence-Based Techniques.

Diagnosing dental caries plays a pivotal role in preventing and treating tooth decay. However, traditional methods of diagnosing caries often fall short in accuracy and efficiency. Despite the endorsement of radiography as a diagnostic tool, the identification of dental caries through radiographic images can be influenced by individual interpretation. Incorporating artificial intelligence (AI) into diagnosing dental caries holds significant promise, potentially enhancing the precision and efficiency of diagnoses. This review introduces the fundamental concepts of AI, including machine learning and deep learning algorithms, and emphasizes their relevance and potential contributions to the diagnosis of dental caries. It further explains the process of gathering and pre-processing radiography data for AI examination. Additionally, AI techniques for dental caries diagnosis are explored, focusing on image processing, analysis, and classification models for predicting caries risk and severity. Deep learning applications in dental caries diagnosis using convolutional neural networks are presented. Furthermore, the integration of AI systems into dental practice is discussed, including the challenges and considerations for implementation as well as ethical and legal aspects. The breadth of AI technologies and their prospective utility in clinical scenarios for diagnosing dental caries from dental radiographs is presented. This review outlines the advancements of AI and its potential in revolutionizing dental caries diagnosis, encouraging further research and development in this rapidly evolving field.

Deep Learning for Dental Diagnosis: A Novel Approach to Furcation Involvement Detection on Periapical Radiographs.

Furcation defects pose a significant challenge in the diagnosis and treatment planning of periodontal diseases. The accurate detection of furcation involvements (FI) on periapical radiographs (PAs) is crucial for the success of periodontal therapy. This research proposes a deep learning-based approach to furcation defect detection using convolutional neural networks (CNN) with an accuracy rate of 95%. This research has undergone a rigorous review by the Institutional Review Board (IRB) and has received accreditation under number 202002030B0C505. A dataset of 300 periapical radiographs of teeth with and without FI were collected and preprocessed to enhance the quality of the images. The efficient and innovative image masking technique used in this research better enhances the contrast between FI symptoms and other areas. Moreover, this technology highlights the region of interest (ROI) for the subsequent CNN models training with a combination of transfer learning and fine-tuning techniques. The proposed segmentation algorithm demonstrates exceptional performance with an overall accuracy up to 94.97%, surpassing other conventional methods. Moreover, in comparison with existing CNN technology for identifying dental problems, this research proposes an improved adaptive threshold preprocessing technique that produces clearer distinctions between teeth and interdental molars. The proposed model achieves impressive results in detecting FI with identification rates ranging from 92.96% to a remarkable 94.97%. These findings suggest that our deep learning approach holds significant potential for improving the accuracy and efficiency of dental diagnosis. Such AI-assisted dental diagnosis has the potential to improve periodontal diagnosis, treatment planning, and patient outcomes. This research demonstrates the feasibility and effectiveness of using deep learning algorithms for furcation defect detection on periapical radiographs and highlights the potential for AI-assisted dental diagnosis. With the improvement of dental abnormality detection, earlier intervention could be enabled and could ultimately lead to improved patient outcomes.

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.