Dental Images Research Articles

Assessment of Image Quality and Child Acceptance for DSLR, Bridge and Smartphone Cameras Used in Dental Photography (A Comparative Study)

Digital photography is of huge importance during the course of dental treatments. It’s indispensable for recording data, patient motivation, communication with the lab etc. Many photography equipment are available and used in dentistry. Using digital photography is obtaining a footstep in pediatric dentistry. The purpose of this study is to evaluate the quality of dental images made by different digital photography mediums commonly used in dental clinics and measure the acceptance of children towards dental photography. Materials and Methods: in this study images produced by Three DSLR (digital single lens reflex) cameras along their prober lenses, one bridge camera and two smartphones with different sensor sizes were evaluated, based upon the sharpness, quality, color fidelity, and general context of the photographs. The participants were divided into three main groups: under-graduate students; graduated dentists and graduated dentists with photography experience. 20 pediatric patients were imaged using the aforementioned equipment. Results: DSLRs with macro lenses and ring flashes scored the highest means with statistically significant differences (p<0.05). From 20 pediatric patient photographed by each equipment used in this study 18 child accepted smartphone photography Conclusion: Older DSLRS are still usable for dental photography. Kit lenses or extension tubes with camera built in flashes are not appropriate for dental photography. Bridge cameras and smartphones are not adequate for dental photography.

FREQUENCY FOR THE USE OF CBCT ANALYSIS IN DENTISTRY

Cone beam computed tomography (CBCT) is a method that allows a three-dimensional evaluation of hard craniofacial structures with a lower radiation dose than classic CT. This method began to be used more and more frequently in dentistry. Background: The aim of the study was to evaluate the frequency for use of CBCT in dentistry. Methods: The study was addressed to dentists of various specialties from Oltenia region that completed an online questionnaire with 10 questions. Data were collected regarding the physician's specialty, the type of interventions for which the dentist recommends CBCT, how necessary this investigation is considered, and how invasive it is considered to be. The questionnaire was launched online for a period of 4 weeks, and dentists from the Oltenia region were invited to participate. The statistical analysis was carried out with the help of the Google Docs program, the section dedicated to forms, "Google Forms". Results: Young doctors chose this technology more frequently than doctors with more than 5 years of experience. Although implant planning is the primary reason for CBCT imaging in dentistry, CBCT is increasingly being used for other diagnostic purposes, such as periodontics and endodontics. In nearly 20% of cases, the primary diagnosis and/or treatment plan was changed following CBCT evaluation. Conclusions: There is an increasing trend in the use of CBCT in dentistry, especially by young graduates.

Intraoral Ultrasonography for the Exploration of Periodontal Tissues: A Technological Leap for Oral Diagnosis.

Periodontal disease is an infectious syndrome presenting inflammatory aspects. Radiographic evaluation is an essential complement to clinical assessment but has limitations such as the impossibility of assessing tissue inflammation. It seems essential to consider new exploration methods in clinical practice. Ultrasound of periodontal tissues could make it possible to visualize periodontal structures and detect periodontal diseases (periodontal pocket measurement and the presence of intra-tissue inflammation). Clinical Innovation Report: An ultrasound probe has been specially developed to explore periodontal tissues. The objective of this clinical innovation report is to present this device and expose its potential. Various immediate advantages favor using ultrasound: no pain, no bleeding, faster execution time, and an image recording that can be replayed without having to probe the patient again. Ultrasound measurements of pocket depth appear to be as reliable and reproducible as those obtained by manual probing, as do tissue thickness measurements and the detection of intra-tissue inflammation. Ultrasound seems to have a broad spectrum of indications. Given the major advances offered by ultrasound imaging as a complementary aid to diagnosis, additional studies are necessary to validate these elements and clarify the potential field of application of ultrasound imaging in dentistry.

Clinical boundary conditions for propagation-based X-ray phase contrast imaging: from bio-sample models targeting to clinical applications.

Typical propagation-based X-ray phase contrast imaging (PB-PCI) experiments using polyenergetic sources are tested in very ideal conditions: low-energy spectrum (mainly characteristic X-rays), small thickness and homogeneous materials considered weakly absorbing objects, large object-to-detector distance, long exposure times and non-clinical detector. Explore PB-PCI features using boundary conditions imposed by a low power polychromatic X-ray source (X-ray spectrum without characteristic X-rays), thick and heterogenous materials and a small area imaging detector with high low-detection radiation threshold, elements commonly found in a clinical scenario. A PB-PCI setup implemented using a microfocus X-ray source and a dental imaging detector was characterized in terms of different spectra and geometric parameters on the acquired images. Test phantoms containing fibers and homogeneous materials with close attenuation characteristics and animal bone and mixed soft tissues (bio-sample models) were analyzed. Contrast to Noise Ratio (CNR), system spatial resolution and Kerma values were obtained for all images. Phase contrast images showed CNR up to 15% higher than conventional contact images. Moreover, it is better seen when large magnifications (>3) and object-to-detector distances (>13 cm) were used. The influence of the spectrum was not appreciable due to the low efficiency of the detector (thin scintillator screen) at high energies. Despite the clinical boundary condition used in this work, regarding the X-ray spectrum, thick samples, and detection system, it was possible to acquire phase contrast images of biological samples.

A deep blur learning-based motion artifact reduction algorithm for dental cone-beam computed tomography images

We propose a motion artifact correction algorithm (DMBL) for reducing motion artifacts in reconstructed dental cone-beam computed tomography (CBCT) images based on deep blur learning. A blur encoder was used to extract motion-related degradation features to model the degradation process caused by motion, and the obtained motion degradation features were imported in the artifact correction module for artifact removal. The artifact correction module adopts a joint learning framework for image blur removal and image blur simulation for treatment of spatially varying and random motion patterns. Comparative experiments were conducted to verify the effectiveness of the proposed method using both simulated motion data sets and clinical data sets. The experimental results with the simulated dataset showed that compared with the existing methods, the PSNR of the proposed method increased by 2.88%, the SSIM increased by 0.89%, and the RMSE decreased by 10.58%. The results with the clinical dataset showed that the proposed method achieved the highest expert level with a subjective image quality score of 4.417 (in a 5-point scale), significantly higher than those of the comparison methods. The proposed DMBL algorithm with a deep blur joint learning network structure can effectively reduce motion artifacts in dental CBCT images and achieve high-quality image restoration.

Insights into Predicting Tooth Extraction from Panoramic Dental Images: Artificial Intelligence vs. Dentists

ObjectivesTooth extraction is one of the most frequently performed medical procedures. The indication is based on the combination of clinical and radiological examination and individual patient parameters and should be made with great care. However, determining whether a tooth should be extracted is not always a straightforward decision. Moreover, visual and cognitive pitfalls in the analysis of radiographs may lead to incorrect decisions. Artificial intelligence (AI) could be used as a decision support tool to provide a score of tooth extractability.Material and methodsUsing 26,956 single teeth images from 1,184 panoramic radiographs (PANs), we trained a ResNet50 network to classify teeth as either extraction-worthy or preservable. For this purpose, teeth were cropped with different margins from PANs and annotated. The usefulness of the AI-based classification as well that of dentists was evaluated on a test dataset. In addition, the explainability of the best AI model was visualized via a class activation mapping using CAMERAS.ResultsThe ROC-AUC for the best AI model to discriminate teeth worthy of preservation was 0.901 with 2% margin on dental images. In contrast, the average ROC-AUC for dentists was only 0.797. With a 19.1% tooth extractions prevalence, the AI model's PR-AUC was 0.749, while the dentist evaluation only reached 0.589.ConclusionAI models outperform dentists/specialists in predicting tooth extraction based solely on X-ray images, while the AI performance improves with increasing contextual information.Clinical relevanceAI could help monitor at-risk teeth and reduce errors in indications for extractions.

Relationship Between Cone-Beam CT Evaluation and Clinical Evaluation Before and After Orthodontic Treatment and the Rate of Gingival Recession: A Systematic Review.

A systematic review was conducted to investigate the correlation between gingival recession and orthodontic treatment analysis using cone-beam CT (CBCT) dental images to provide an accurate overview of the factors that can influence gingival recession. The literature was searched across several databases (PubMed, Scopus, Web of Science, etc.) for studies using CBCT dental images. Four full-text articles describe how CBCT is used to evaluate gingival recession during orthodontic treatment. The Risk-of-Bias VISualization tool was used to assess the risk of bias. In dentistry, CBCTs are used for various imaging modalities. An accurate assessment of gingival recession was made using CBCT. There were only 35 (22.5%) cases of retraction of the gingival margin after orthodontic treatment. In most studies, various types of malocclusions were treated successfully with fixed orthodontic treatment without gingival recession. Among the studies, 50% had a low risk of bias in all four areas, while one study (25%) had an unclear risk of bias. Only one study (25%) had a high risk of bias. CBCT systems have been extensively studied to show their wide application potential in preventing gingival recession. CBCT systems should be further investigated to address limitations associated with methodology and application. Dental applications of CBCT can be enriched by overcoming these challenges.

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.

Current state and trajectory of artificial intelligence in dentistry: A review

Artificial intelligence to a limited extent is science and engineering engrossed with the study of intelligent behaviour using computers and performing tasks which usually assumed can only be performed by human beings as well as the design of products that display this action. It is making waves in dentistry as exponential amount of digital data is available, note worthy progress in hardware performance, as well as significant advancements in algorithmic and software approaches, the current abilities of this technology is unparalleled. Its applications are diversifying into domains that were exclusive to human specialist and include medical and dental imaging diagnostics, decision making assistance, digital medicine, drug discovery, wearable technology, medical surveillance, robotic and digital assistants. The aim of this review is to define artificial intelligence, its benefits in dental field and its potential risks to community.

Artificial Intelligence and its Awareness and Utilization among Dental Students and Private Dental Practitioners at Alkharj, Saudi Arabia.

Artificial intelligence (AI) is commonly used in the modern day medical system for medical and dental imaging diagnostics, decision support, precision, hospital monitoring, robotic assistants, and so on. All branches of dentistry have a role of AI, like endodontics, cancer diagnosis, and cephalometric analysis. With the advancing technology, dental professionals need to upgrade themselves. To assess awareness and attitude of dental students and dental practitioners in Alkharj toward AI. A total of 100 dental students from a teaching institute and 100 private dental practitioners participated in the study. A closed-ended questionnaire was used containing 14 questions related to awareness and attitude toward AI. Participation was voluntary. 33% of study participants were aware of the working principle of AI; 68% of study subjects are aware of uses of AI in the dental field. 87% thinks AI helps in radiological diagnosis; 56.5% thinks AI helps in cancer detection. Awareness about AI among study participants was less than 50%. The overall attitude of dental professionals was positive.

Advancements in Dental Imaging: A Technological Renaissance in Diagnosis and Treatment Planning

Dental imaging technology has undergone significant advancements in recent years, offering dentists unprecedented tools for accurate diagnosis and treatment planning. This article provides a comprehensive overview of the latest innovations in dental imaging and their clinical applications. Cone Beam Computed Tomography (CBCT) has revolutionized imaging by providing high-resolution 3D images with minimal radiation exposure. Its versatility in applications, from implant planning to TMJ analysis, makes it an indispensable tool in modern dentistry. Studies have shown its effectiveness in detecting root fractures and periapical lesions, enhancing diagnostic precision. Digital Radiography has replaced traditional film-based systems, offering benefits like reduced radiation doses and faster image acquisition. Its superior diagnostic capabilities in detecting caries and periodontal conditions enable early intervention and preventive strategies, improving patient outcomes. Intraoral Scanners have transformed impression-taking, eliminating the need for messy materials and improving patient comfort. Their accuracy in capturing detailed intraoral structures ensures precise fit and aesthetics in crown and bridge restorations, enhancing overall treatment quality. Optical Coherence Tomography (OCT) has gained attention for its non-invasive, high-resolution imaging of dental tissues. Its utility in detecting early caries lesions, evaluating periodontal tissues, and assessing adhesive interfaces in restorations supports conservative treatment approaches and long-term restoration success. Artificial Intelligence (AI) algorithms are being integrated into dental imaging, enhancing diagnostic capabilities and streamlining workflow. AI’s role in image analysis, risk assessment, and treatment planning opens avenues for personalized dentistry and predictive outcomes, ultimately improving patient care. In conclusion, the rapid advancements in dental imaging technologies have transformed dental diagnostics and treatment planning. Integrating these innovations into clinical practice not only improves diagnostic accuracy and treatment outcomes but also enhances patient experience and satisfaction. Continued research and technological advancements promise further enhancements in dental imaging capabilities, paving the way for optimized patient care in the future.

Fine structural human phantom in dentistry and instance tooth segmentation

In this study, we present the development of a fine structural human phantom designed specifically for applications in dentistry. This research focused on assessing the viability of applying medical computer vision techniques to the task of segmenting individual teeth within a phantom. Using a virtual cone-beam computed tomography (CBCT) system, we generated over 170,000 training datasets. These datasets were produced by varying the elemental densities and tooth sizes within the human phantom, as well as varying the X-ray spectrum, noise intensity, and projection cutoff intensity in the virtual CBCT system. The deep-learning (DL) based tooth segmentation model was trained using the generated datasets. The results demonstrate an agreement with manual contouring when applied to clinical CBCT data. Specifically, the Dice similarity coefficient exceeded 0.87, indicating the robust performance of the developed segmentation model even when virtual imaging was used. The present results show the practical utility of virtual imaging techniques in dentistry and highlight the potential of medical computer vision for enhancing precision and efficiency in dental imaging processes.

Advancements in dental imaging: A technological renaissance in diagnosis and treatment planning

Dental imaging has witnessed a significant evolution with advanced technologies, enhancing diagnostic accuracy and treatment planning in dentistry.

Dental Carries Prediction and Prevention

Abstract: A large percentage of people worldwide suffer from dental cavities, which are prevalent disorders. They caused by bacterial activity, can lead to permanent damage to teeth. They pose a risk to tooth health without timely intervention. In order to stop additional harm, early identification and treatment are essential. Traditionally, x-rays or a direct visual inspection are done by dentists to detect cavities. On the other hand, automated systems that employ image processing methods including region recognition, binary image synthesis, and RGB to Gray conversion can help locate cavities in x-ray pictures. Automated tools that utilize ordinary cameras for dental image analysis are also being explored, showing promising results.

Modelling a dense network connectivity for panoptic tooth segmentation using learning approaches

By enabling precise diagnoses and assisting in creating successful treatment regimens, teeth segmentation plays a crucial role in dentistry. The segmentation of teeth has been the primary emphasis of previous approaches, but they frequently ignore the context of the oral tissue as a whole. According to this study, background semantic and foreground instance-based segmentation results are combined in a panoptic-segmentation-based approach. This work provides a tailored architecture for segmenting using a Super Adaptive Dense Convolutional Neural Network (sa−DCNN) with a panoptic quality (PQ) loss function. The PQ loss function speeds up learning and enables the model to predict masks and the classes that go with them instantly. In our suggested design, sa−DCNN enables connectivity between the memory path and the sa−DCNN pixel route. A stacked network block integrating multi-scale properties from several decoding resolutions is also included. The network block's primary duties involve integrating various mechanisms. The output heads analyze the memory path and pixel route data to predict the mask classes used to create the final mask. The proposed model outperforms the most recent panoptic segmentation on the UFBA-UESC Dental Image dataset, notably in performance and resilience. Our study represents a significant advancement in teeth segmentation and advances our knowledge of oral anatomy.

Preoperative assessment of bone density for dental implantation: a comparative study of three different ROI methods

BackgroundDental cone beam computed tomography (CBCT) is commonly used to evaluate cancellous bone density before dental implant surgery. However, to our knowledge, no measurement approach has been standardized yet. This study aimed to evaluate the relationship between three different regions of interest (ROI) methods on cancellous bone density at the dental implant site using dental CBCT images.MethodsPatients’ dental CBCT images (n = 300) obtained before dental implant surgery were processed using Mimics (Materialise, Leuven, Belgium). At the potential implant sites, the rectangle, cylinder, and surrounding cylinder ROI methods were used to measure bone density. Repeated measures one-way analysis of variance was performed to compare the three ROI methods in terms of measurement results. Pearson correlation analysis was performed to identify the likely pair-wise correlations between the three ROI methods.ResultsThe density value obtained using the surrounding cylinder approach (grayscale value [GV],523.56 ± 228.03) was significantly higher than the values obtained using the rectangle (GV, 497.04 ± 236.69) and cylinder (GV,493 ± 231.19) ROI methods in terms of results. Furthermore, significant correlations were noted between the ROI methods (r > 0.965; p < 0.001).ConclusionsThe density measured using the surrounding cylinder method was the highest. The choice of method may not influence the trends of measurement results.Trial registrationThis study was approved by the Institutional Review Board of China Medical University Hospital, No. CMUH111-REC3-205. Informed consent was waived by the Institutional Review Board of China Medical University Hospital, CMUH111-REC3-205, owing to the retrospective nature of the study.

A Deep Learning Approach to Dental Image Analysis and Problem Identification

Panoramic Images are key tool for dentists to spot dental issues early and plan the best course of treatment. In this paper we propose a approach of dental problem detection using images. Model can detect 2 types of issues which are decay and cavity. Model is trained using CNN algorithm. Image processing is perform for detection. After detection of dental problem solution to that problem is also provided such as for decay tooth preventive measures

Symmetry of the external acoustic meatus: A potential alternative reference plane for three-dimensional imaging in dentistry

ObjectiveIn this study, we thoroughly analyzed how balanced the left and right sides of the external acoustic meatus are. Despite previous research focusing on the consistency of various anatomical features and the shape of the external acoustic meatus, which are important for creating guidelines to assess changes in the skull, there hasn't been enough attention given to how symmetrical it is. Our aim was to fill this gap by providing a comprehensive examination of its bilateral symmetry, which is crucial for accurate evaluations in dentistry and medicine. Study designAfter importing 26 cone-beam computed tomography scans of patients into the ITK-SNAP 3D imaging software, a midsagittal plane was set up as the plane of symmetry for each patient. With this plane, we compared the positions of the most superior and inferior left and right points of the external acoustic meatus. We also compared the lengths and depths of the lines connecting the two points. ResultsThere were no statistically significant differences in the position, length, or depth of the external acoustic meatus between the right and left halves of the skull. ConclusionSpecific points on the skull, such as the highest (most superior MSP) and lowest (most inferior MIP) points, demonstrated a high degree of symmetry in the left and right halves. They demonstrated sufficient symmetry to establish a reliable reference plane. Along with the trajectory connecting them, these points can serve as viable alternatives to the Porion for three-dimensional imaging.

Radiological safety perspectives among undergraduate dental students, interns, and dental assistant certificate students at Taibah University Dental College and Hospital, Saudi Arabia

ABSTRACT Background: Dental imaging accounts for &lt;1% of human exposure to ionizing radiation from of all types of medical imaging. Radiation-induced damage to cells has deterministic and stochastic consequences. The international commision for radiological protection recommends that any radiographic examination must be justified. However, studies measuring dental assistant (DA) knowledge and attitude towards radiation protection measures are lacking. Aim: Our study aimed to evaluate the knowledge and attitudes toward radiation protection measures among clinical dental students, interns, and DA certificate students. Settings and Design: The study was a cross-sectional observational analytical online questionnaire-based study. Materials and Methods: The questionnaire distributed over undergraduate students, DA certificate students, and dental interns. Statistical Analysis: Stata/BE 18.0 for Mac was used, Chi-square tests with a P &lt; 0.05 indicating a significant difference in knowledge between groups. Results: A questionnaire was distributed to 203 of the participants. The average overall correct responses 46.14%. However, the DA student average correct answers were 38.71%. Conclusion: Students must improve their knowledge about radiation protective measures through educational sessions or pamphlets, particularly the DA. Radiology lectures should cover new safety precautions such as as low as diagnostically acceptable, shielding, and radiation to pregnant mothers, while incorporating national guidelines and recommendations.

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.