Dental Diagnosis Research Articles

Safety net patients' satisfaction with oral health services by provider type and intent to return for more care.

This study examined patients' satisfaction with services provided by different oral health providers, their intent to return for additional care, and associations with patients' demographics and service characteristics. Descriptive analyses and multivariable ordinal logistic regressions were conducted using survey data from 898 patients who received care at Apple Tree Dental (ATD) in Minnesota during 2021. The questionnaire included 12 statements on patient satisfaction with the clinician's ability to explain the dental diagnosis and treatment options, to be considerate of the patient's needs and dental anxiety, and to provide technically competent services. In general, patients reported high satisfaction with dental care and a strong intent to return to ATD for future services. No significant differences in patient satisfaction were observed by provider type. Patients' intent to return was higher among non-White respondents (OR = 1.76; 95% CI = 1.06-2.92) and patients who were more satisfied with their providers' technical competence/treatment (OR = 1.47; 95% CI = 1.37-1.57). The association between intent to return and patient satisfaction with providers' information/communication was stronger for patients treated by dental hygienists. The association between intent to return and patient satisfaction with providers' technical competence/treatment was also stronger for patients who were more satisfied with providers' information/communication and understanding/acceptance, and for those treated by their desired or usual provider. The study underscores the benefits of introducing dental therapists to the oral healthcare team, showing that this can be achieved without sacrificing either the quality of patient care or patient satisfaction.

Awareness and perceptions of artificial intelligence in dentistry: A cross-sectional survey among Indian dental professionals

Introduction Artificial intelligence (AI) is inevitably going to impact healthcare including dentistry and will become an essential tool in medical diagnosis and decision-making. Dental professionals must be familiar with growing trends in dentistry such as AI and its future scope. Despite the positive developments in AI research, there are divergent perspectives on its benefits and risks among stakeholders. We intended to understand the knowledge, awareness, and perceptions of dental professionals towards AI and its applications in dentistry. Methods and Material A semi-structured, 25-item Google form questionnaire consisting of closed and open-ended questions was made and the link to answer the survey was circulated among postgraduate students, dental academicians, and practitioners across India in an online mode, and the responses were collected and analyzed. Results 83.3% of participants were aware of AI and its applications. Most of the participants understood the attributes, advantages, and disadvantages of AI. Interestingly 72% of participants agreed that they have witnessed AI being used in clinical practice and 92.7% agreed to use AI for diagnosis. 65.3% expressed concern over unemployment due to AI and 85% agreed that AI has ethical issues. Over 85% of participants agreed AI should be a part of the postgraduate dental curriculum. Conclusions We found that dental professionals are updated with AI technology and showed a willingness to adopt AI into dental practice. The participants lacked a deeper understanding of AI and concerned about the potential risk of unemployment resulting from AI and trusting AI alone in dental diagnosis. Keywords: Artificial intelligence, Cross-sectional survey, Dentist, Knowledge, Perceptions.

Potential Benefits of Photon-Counting CT in Dental Imaging: A Narrative Review.

Background/Objectives: Advancements in oral imaging technology are continually shaping the landscape of dental diagnosis and treatment planning. Among these, photon-counting computed tomography (PCCT), introduced in 2021, has emerged as a promising, high-quality oral technology. Dental imaging typically requires a resolution beyond the standard CT systems achievable with the specialized cone-beam CT. PCCT can offer up to 100 µm resolution, improve soft-tissue contrast, and provide faster scanning times, which are crucial for detailed dental diagnosis and treatment planning. Using semiconductor detectors, PCCT produces sharper images and can potentially reduce the number of scans required, thereby decreasing patient radiation exposure. This review aimed to explore the potential benefits of PCCT in dental imaging. Methods: This review analyzed the literature on PCCT in dental imaging from January 2010 to February 2024, sourced from PubMed, Scopus, and Web of Science databases, focusing on high-resolution, patient safety, and diagnostic efficiency in dental structure assessment. We included English-language articles, case studies, letters, observational studies, and randomized controlled trials while excluding duplicates and studies unrelated to PCCT's application in dental imaging. Results: Studies have highlighted the superiority of PCCT in reducing artifacts, which are often problematic, compared to conventional CBCT and traditional CT scans, due to metallic dental implants, particularly when used with virtual monoenergetic imaging and iterative metal artifact reduction, thereby improving implant imaging. This review acknowledges limitations, such as the potential for overlooking other advanced imaging technologies, a narrow study timeframe, the lack of real-world clinical application data in this field, and costs. Conclusions: PCCT represents a promising advancement in dental imaging, offering high-resolution visuals, enhanced contrast, and rapid scanning with reduced radiation exposure.

The Association of Dental Caries or Dental Implant Placement Within One Year of Primary Total Hip Arthroplasty on Medical and Implant Complications

BackgroundTotal hip arthroplasty (THA) adverse events among patients who have dental diagnoses remain unclear. We sought to determine if dental caries or dental implant placements increased (1) 90-day medical complications, (2) 90-day readmissions, and (3) 2-year implant-related complications, including periprosthetic joint infections (PJIs) after THA. MethodsA nationwide database for primary THAs from 2010 to 2021 was queried to compare 3 patient cohorts. Patients who had a history of dental implants or caries within 12 months prior to THA (n = 1,179) or 12 months after THA (n = 1,218) were case-matched to patients who did not have dental history (n = 6,090) by age and comorbidities. Outcomes included 90-day complications, 90-day readmissions, and 2-year implant-related complications. Logistic regression models computed the odds ratios (ORs) of complications and readmissions. P values less than .006 were significant. ResultsPatients who had dental caries or implant placement 12 months before or after THA experienced 1.6-fold greater odds of 90-day medical complications compared to case-matched patients. Readmissions within 90 days increased for patients who had a dental history before (11.7% versus 8.3%; OR: 1.49, P < .0001) and after (14.2% versus 8.3%; OR: 1.84, P < .0001) THA compared to case-matched patients. A dental caries diagnosis or dental implant placement within 12 months following THA increased 2-year implant complications compared to case-matched patients (15.2% versus 9.3%; OR: 1.69, P < .0001), including PJIs (5.3% versus 2.8%; OR: 1.98, P < .0001), dislocations (4.4% versus 2.7%; OR: 1.63, P = .002), and THA revisions (3.9% versus 2.5%; OR: 1.61, P = .005). ConclusionsDental diagnoses within 12 months of THA are associated with increased medical complications. Dental diagnoses occurring 12 months after THA are associated with greater implant-related complications, including PJIs. Delaying invasive dental procedures for 12 months after THA may be advisable.

Study of artificial intelligence and its awareness in dental mainly aesthetic dentistry and prosthodontics

: Each day there is a new research. Similarly in dental profession with recent advances, artificial intelligence is a new trend. It's for better accurate superior health care. This makes dental diagnosis treatment plan better and successful. : This study is to assess the awareness and knowledge of artificial intelligence in dental profession. : this cross sectional study was done by questionnaire in 100 dental professionals. This was to know their knowledge and awareness in artificial intelligence. : very few dental professionals know of artificially intelligence. Those who know actually know v little. : Attempt should be made to know more about artificial intelligence. Its new application and more uses of artificial intelligence can be done in dental in future.

A Survey on Dentes Condition Detection System Using Machine Learning

For dental health to be maintained and consequences to be avoided, early diagnosis of diseases connected to dentes is essential. Missed treatment possibilities arise from dentists' frequent inability to visually identify these problems. In response, a real-time X-ray analysis model has been created to quickly and precisely identify dental issues. This invention improves patient care by accelerating treatment planning and reducing the possibility of misdiagnosis. The concept saves time and dollars by streamlining diagnostics through the use of cutting-edge imaging technology. It increases the efficiency of dental care by doing away with time-consuming manual examinations. This model allows for early intervention in diseases connected to dentes, which represents a significant development in dental diagnosis.

Unveiling the Potential of VELScope in Potentially Malignant Oral Lesion Early Detection for Future Dental Diagnosis Technology

Introduction: Oral lesions range from innocuous to possibly dangerous, including oral cancer, which is the world's 16th most common malignancy. Early detection of oral cancer can enhance a patient's life expectancy by a decade, emphasizing the need of diagnostic instruments like VELScope. VELScope is a portable device that employs blue light to examine oral tissue, exposing natural fluorescence and emphasizing aberrant regions that would otherwise be invisible without the use of specialized equipment. Objectives: This review looks at the possibility of the VELScope for identifying oral lesions, since it has the potential to be a future dental diagnostic tool. Method: We began with 88 findings and reduced them down to 33 research regarding VELScope technology published in the previous five years. Following screening, we chose ten articles using the same criteria for all research. Results: VELScope uses fluorescence to identify both obvious and concealed oral lesions, which is especially useful when collagen loss occurs in normal tissues. While using autofluorescence requires knowledge, it assists in analyzing margins in probable pre- malignant oral conditions. VELScope, despite its limitations, has promise for early cancer diagnosis as technology develops. Conclusion: When utilized for early diagnosis, VELScope helps to reduce oral cancer mortality, reaching an 83% 5-year survival rate. While it has limits, continuous technical improvements have the potential to improve its accuracy, making it a vital tool for accurate early diagnosis and lowering oral cancer mortality.

Deep learning methods for fully automated dental age estimation on orthopantomograms.

This study aimed to use all permanent teeth as the target and establish an automated dental age estimation method across all developmental stages of permanent teeth, accomplishing all the essential steps of tooth determination, tooth development staging, and dental age assessment. A three-step framework for automatically estimating dental age was developed for children aged 3 to 15. First, a YOLOv3 network was employed to complete the tasks of tooth localization and numbering on a digital orthopantomogram. Second, a novel network named SOS-Net was established for accurate tooth development staging based on a modified Demirjian method. Finally, the dental age assessment procedure was carried out through a single-group meta-analysis utilizing the statistical data derived from our reference dataset. The performance tests showed that the one-stage YOLOv3 detection network attained an overall mean average precision 50 of 97.50 for tooth determination. The proposed SOS-Net method achieved an average tooth development staging accuracy of 82.97% for a full dentition. The dental age assessment validation test yielded an MAE of 0.72years with a full dentition (excluding the third molars) as its input. The proposed automated framework enhances the dental age estimation process in a fast and standard manner, enabling the reference of any accessible population. The tooth development staging network can facilitate the precise identification of permanent teeth with abnormal growth, improving the effectiveness and comprehensiveness of dental diagnoses using pediatric orthopantomograms.

STSN-Net: Simultaneous Tooth Segmentation and Numbering Method in Crowded Environments with Deep Learning.

Accurate tooth segmentation and numbering are the cornerstones of efficient automatic dental diagnosis and treatment. In this paper, a multitask learning architecture has been proposed for accurate tooth segmentation and numbering in panoramic X-ray images. A graph convolution network was applied for the automatic annotation of the target region, a modified convolutional neural network-based detection subnetwork (DSN) was used for tooth recognition and boundary regression, and an effective region segmentation subnetwork (RSSN) was used for region segmentation. The features extracted using RSSN and DSN were fused to optimize the quality of boundary regression, which provided impressive results for multiple evaluation metrics. Specifically, the proposed framework achieved a top F1 score of 0.9849, a top Dice metric score of 0.9629, and an mAP (IOU = 0.5) score of 0.9810. This framework holds great promise for enhancing the clinical efficiency of dentists in tooth segmentation and numbering tasks.

Self-equilibrium segmentation of near-infrared images of dental microcracks

The issue of missing teeth caused by dental microcracks has become a central topic in dental clinical medicine. Near-infrared technology combined with advanced image segmentation algorithms has become one of the leading technical routes for dental clinical medicine diagnosis. In this study, the NIR image segmentation method of dental microcracks was studied in depth to further utilize the advantages of near-infrared imaging technology in the detection technology of dental microcracks. The main contributions and research contents of this paper include (1) we designed a near-infrared imaging system for dental imaging, which provides solid data support for the study of advanced segmentation algorithms; (2) we designed a self-equilibrium segmentation algorithm for occult fracture of teeth based on U-Net++. The core module of this algorithm includes a multi-resolution residual encoder and a self-equilibrium composite loss function. The multi-scale residual encoder significantly improves the feature extraction capability of U-Net++. The self-equilibrium combined loss function ensures the consistency of the model’s attention to positive and negative samples, which enhances the robustness of U-Net++. (3) We have demonstrated through extensive experiments that our algorithm performs better than other dental occultation segmentation methods. This study is valuable for improving the diagnostic accuracy of dental occult fractures and promptly detecting the potential danger of the dental.

Dental Caries diagnosis from bitewing images using convolutional neural networks

BackgroundDental caries, also known as tooth decay, is a widespread and long-standing condition that affects people of all ages. This ailment is caused by bacteria that attach themselves to teeth and break down sugars, creating acid that gradually wears away at the tooth structure. Tooth discoloration, pain, and sensitivity to hot or cold foods and drinks are common symptoms of tooth decay. Although this condition is prevalent among all age groups, it is especially prevalent in children with baby teeth. Early diagnosis of dental caries is critical to preventing further decay and avoiding costly tooth repairs. Currently, dentists employ a time-consuming and repetitive process of manually marking tooth lesions after conducting radiographic exams. However, with the rapid development of artificial intelligence in medical imaging research, there is a chance to improve the accuracy and efficiency of dental diagnosis.MethodsThis study introduces a data-driven model for accurately diagnosing dental decay through the use of Bitewing radiology images using convolutional neural networks. The dataset utilized in this research includes 713 patient images obtained from the Samin Maxillofacial Radiology Center located in Tehran, Iran. The images were captured between June 2020 and January 2022 and underwent processing via four distinct Convolutional Neural Networks. The images were resized to 100 × 100 and then divided into two groups: 70% (4219) for training and 30% (1813) for testing. The four networks employed in this study were AlexNet, ResNet50, VGG16, and VGG19.ResultsAmong different well-known CNN architectures compared in this study, the VGG19 model was found to be the most accurate, with a 93.93% accuracy.ConclusionThis promising result indicates the potential for developing an automatic AI-based dental caries diagnostic model from Bitewing images. It has the potential to serve patients or dentists as a mobile app or cloud-based diagnosis service (clinical decision support system).

Classification of the Relative Position between the Third Molar and the Inferior Alveolar Nerve Using a Convolutional Neural Network Based on Transfer Learning

In recent years, there has been a significant increase in collaboration between medical imaging and artificial intelligence technology. The use of automated techniques for detecting medical symptoms has become increasingly prevalent. However, there has been a lack of research on the relationship between impacted teeth and the inferior alveolar nerve (IAN) in DPR images. The severe compression of teeth against the IAN may necessitate the requirement for nerve canal treatment. To reduce the occurrence of such events, this study aims to develop an auxiliary detection system capable of precisely locating the relative positions of the IAN and impacted teeth through object detection and image enhancement. This system is designed to shorten the duration of examinations for dentists while concurrently mitigating the chances of diagnostic errors. The innovations in this research are as follows: (1) using YOLO_v4 to identify impacted teeth and the IAN in DPR images achieves an accuracy of 88%. However, the developed algorithm in this study achieves an accuracy of 93%. (2) Image enhancement is utilized in this study to expand the dataset, with an accuracy of up to 2~3% enhancement in detecting diseases. (3) The segmentation technique proposed in this study surpasses previous methods by achieving 6% higher accuracy in dental diagnosis.

Rule-Based Text Classification of Dental Diagnosis.

Unstructured medical records boast an abundance of information that could greatly facilitate medical decision-making and improve patient care. With the development of Natural Language Processing (NLP) methodology, the free-text medical data starts to attract more and more research attention. Most existing studies try to leverage the power of such unstructured data using Machine Learning algorithms, which would usually require a relatively large training set, and high computational capacity. However, when faced with a smaller-scale project, opting for an alternative approach may be more effective and practical. This project proposes an efficient and light-weight rule-based approach to categorize dental diagnosis data. It not only fills the void of dental records in the medical free-text processing area, but also demonstrates that with expertly designed research structure and proper implementation, simple method could achieve our study goal very competently.

Dental student application of artificial intelligence technology in detecting proximal caries lesions.

This study aimed to investigate the caries diagnosis performances of dental students after training with an artificial intelligence (AI) application utilizing deep learning techniques, a type of artificial neural network. A total of 1200 bitewing radiographs were obtained from the institution's database and two specialist dentists labeled the caries lesions in the images. Randomly selected 1000 images were used for training purposes and the remaining 200 radiographs were used to evaluate the caries diagnostic performance of the AI. Then, a convolutional neural network, a deep learning algorithm commonly employed to analyze visual imagery problems, called "You Only Look Once," was modified and trained to detect enamel and dentin caries lesions in the radiographs. Forty dental students were selected voluntarily and randomly divided into two groups. The pre-test results of dental caries diagnosis performances of both groups were recorded. After 1 week, group 2 students were trained using an AI application. Then, the post-test results of both groups were recorded. The labeling duration of the students was also measured and analyzed. When both groups' pre-test and post-test results were evaluated, a statistically significant improvement was found for all parameters examined except precision score (p<0.05). However, the trained group's accuracy, sensitivity, specificity, and F1 scores were significantly higher than the non-trained group in terms of post-test scores (p<0.05). In group 2 (trained group), the post-test labeling time was considerably increased (p<0.05). The students trained by AI showed promising results in detecting caries lesions. The use of AI can also contribute to the clinical education of dental students.

Proposal of dental demineralization diagnosis with OCT echo based on multiscale entropy analysis.

Optical coherence tomography (OCT) has been widely used for the diagnosis of dental demineralization. Most methods rely on extracting optical features from OCT echoes for evaluation or diagnosis. However, due to the diversity of biological samples and the complexity of tissues, the separability and robustness of extracted optical features are inadequate, resulting in a low diagnostic efficiency. Given the widespread utilization of entropy analysis in examining signals from biological tissues, we introduce a dental demineralization diagnosis method using OCT echoes, employing multiscale entropy analysis. Three multiscale entropy analysis methods were used to extract features from the OCT one-dimensional echo signal of normal and demineralized teeth, and a probabilistic neural network (PNN) was used for dental demineralization diagnosis. By comparing diagnostic efficiency, diagnostic speed, and parameter optimization dependency, the multiscale dispersion entropy-PNN (MDE-PNN) method was found to have comprehensive advantages in dental demineralization diagnosis with a diagnostic efficiency of 0.9397. Compared with optical feature-based dental demineralization diagnosis methods, the entropy features-based analysis had better feature separability and higher diagnostic efficiency, and showed its potential in dental demineralization diagnosis with OCT.

Development of handheld optical coherence tomography based on commercial intra‐oral scanner shape for extended clinical utility in dentistry

AbstractThe main objective of this study is to develop a handheld dental optical coherence tomography (OCT) system capable of imaging the target area of the teeth in the oral cavity and demonstrate the applicability of the developed system to in vivo dental disease diagnosis based on a user‐friendly scanner form. The design of the developed system is based on mimicking the shape of a commonly used commercial intra‐oral scanner (IOS) so that the system users do not feel awkward when using it. A microelectromechanical system (MEMS) scanning unit was used to optimize the overall size of the system, and the optical axis was designed in a Z‐shape to maximize the beam size reflected by the MEMS mirror. A pre‐study of in vivo oral scanning was performed to demonstrate the enhanced clinical feasibility of the developed system. Furthermore, the main experiment classified teeth with worn cervical regions into two groups according to the cervical abrasion shape. The developed dental OCT system produced a three‐dimensional image of the scanning area and quantitatively analyzed the margin gap through an A‐scan profile based on cross‐sectional images. This study confirms the effectiveness of the developed handheld dental OCT system in actual dental clinical practice, and we expect our system to be utilized for various dental diseases, not only for diagnosing a cervical abrasions.

Influence of the digital file format on radiographic diagnostic in dentistry: a scoping review.

Given today's higher demand for online transmission of radiographic images, clinicians and regulatory agencies should be given the evidence they need to guide them in choosing the best image file format to be adopted. To this end, the present scoping review aims to explore, map, and evaluate the literature, with the object of reporting the influence of image file formats on dental diagnostic tasks by assessing intraoral radiographic images. This scoping review complies with PRISMA-ScR. It was customized to assess the risk of bias of the included studies, and was registered on the Open Science Framework platform. The data extraction protocol was developed based on the PCC acronym. An electronic search was conducted in six databases (Pubmed, Web of Science, Scopus, Embase, Lilacs, Cochrane) in December 2023. Original articles were screened, having observational, diagnostic accuracy, and consisting of in vivo or ex vivo laboratory studies investigating the influence of file formats on different diagnostic tasks in dentistry. Eighteen studies, published between the years 1996 and 2022, were included. The following data were extracted from the selected articles: article title, authors' citation, publication date, country, diagnostic task, image file formats tested, compression level, and main conclusion. The most widely investigated diagnostic task was caries lesions (n = 10), led by root resorptions (n = 3), root fractures (n = 2), periapical lesions (n = 2), and periodontal disease (n = 1). The most commonly used radiographic techniques were periapical (n = 12) and bitewing (n = 6). The most frequently investigated image file formats were JPEG (all studies) and TIFF (n = 10 studies). BMP, PNG, and JPEG2000 were also included in 7, 3 and 3 studies, respectively. No studies included the DICOM file format. In regard to the subjective assessment of the several dental diagnostic tasks, the studies mostly showed that the influence of the file formats was not significant (n = 10/55.5%). As for the quality assessment of the included papers, more than 70% of the studies featured a low risk of bias. Current evidence on image file formats and dental radiographic diagnosis is reliable. Any image file format can be used without impairing diagnostic accuracy.

Deep Learning-Based Detection of Impacted Teeth on Panoramic Radiographs.

The aim is to detect impacted teeth in panoramic radiology by refining the pretrained MedSAM model. Impacted teeth are dental issues that can cause complications and are diagnosed via radiographs. We modified SAM model for individual tooth segmentation using 1016 X-ray images. The dataset was split into training, validation, and testing sets, with a ratio of 16:3:1. We enhanced the SAM model to automatically detect impacted teeth by focusing on the tooth's center for more accurate results. With 200 epochs, batch size equals to 1, and a learning rate of 0.001, random images trained the model. Results on the test set showcased performance up to an accuracy of 86.73%, F1-score of 0.5350, and IoU of 0.3652 on SAM-related models. This study fine-tunes MedSAM for impacted tooth segmentation in X-ray images, aiding dental diagnoses. Further improvements on model accuracy and selection are essential for enhancing dental practitioners' diagnostic capabilities.

Effect of auto-adaptive metal artifact reduction (aMAR) program in cone-beam computed tomography on assessing pre-implant bone levels.

This research aimed to introduce an auto-adaptive metal artifact reduction (aMAR) algorithm in cone-beam computed tomography (CBCT) to assess the levels of the pre-implant alveolar crest. Dental implants as a treatment modality for edentulous patients consist of a titanium alloy, which creates a metal artifact, resulting in a dark dental structure in the CBCT scans. Metallic artifacts are limiting factors for the precise detection in CBCT images. These are related to the dark areas around materials and metallic structures (e.g., restorations, implants, and endodontic instruments). To overcome this problem, the metal artifact reduction (MAR) program has been recommended as a post-procedure stage for CBCT image reconstruction. Recent developments offer CBCT scanners with an aMAR option with a greater dynamic range to help overcome the challenges of peri-implant bone evaluation to reach accurate dental diagnoses.

Spatial Prior-Guided Bi-Directional Cross-Attention Transformers for Tooth Instance Segmentation.

Tooth instance segmentation of dental panoramic X-ray images represents a task of significant clinical importance. Teeth demonstrate symmetry within the upper and lower jawbones and are arranged in a specific order. However, previous studies frequently overlook this crucial spatial prior information, resulting in misidentifications of tooth categories for adjacent or similarly shaped teeth. In this paper, we propose SPGTNet, a spatial prior-guided transformer method, designed to both the extracted tooth positional features from CNNs and the long-range contextual information from vision transformers for dental panoramic X-ray image segmentation. Initially, a center-based spatial prior perception module is employed to identify each tooth's centroid, thereby enhancing the spatial prior information for the CNN sequence features. Subsequently, a bi-directional cross-attention module is designed to facilitate the interaction between the spatial prior information of the CNN sequence features and the long-distance contextual features of the vision transformer sequence features. Finally, an instance identification head is employed to derive the tooth segmentation results. Extensive experiments on three public benchmark datasets have demonstrated the effectiveness and superiority of our proposed method in comparison with other state-of-the-art approaches. The proposed method demonstrates the capability to accurately identify and analyze tooth structures, thereby providing crucial information for dental diagnosis, treatment planning, and research.