Dental Radiographs Research Articles

A Study on Dental Radiographic Interpretation Skills and Knowledge among Undergraduate Students

Abstract: Introduction: Dental radiographs play a pivotal role in oral health diagnostics and treatment planning. Competency in interpreting these images is vital for dental undergraduate students to provide effective patient care. The aim of this study is to evaluate the existing levels of perceived confidence and actual skills in radiographic interpretation among dental undergraduate students, with the ultimate goal of identifying areas for improvement in dental education. Methods: A cross-sectional study was conducted at a private dental college in Chennai, involving 101 dental undergraduate students. A structured questionnaire comprising 20 questions, divided into confidence and knowledge assessment sections, was administered to the participants. The data collected were analysed using statistical software. Results: The majority of participants were female (71.3%) and in their early twenties (mean age 22.5 years). While 37.6% felt moderately comfortable identifying anatomical structures on radiographs, 45.5% expressed moderate confidence in diagnosing dental pathologies.

Correlation Between Radiographer’s Level Of Knowledge About COVID-19 With Its Preventive Behavior In Dental Radiography Examination, 2022

Backgroundnd: Health Care Associated Infections (HAIs) including COVID-19 are an ongoing challenge to the safety of patients and health workers as well as the quality of health services that require attention and ongoing efforts to prevent the risk of transmission. Radiographers who perform dental radiographic examinations are at high risk of spreading COVID-19 because of the examination requires close contact with patients and the risk of spreading droplets or aerosols. This research was conducted with the aim of studying and analyzing the relationship between the radiographer’s level of knowledge about COVID-19 and the preventive behavior of its transmission in dental radiographic examinations. Methods: The research was conducted by collecting data using an online questionnaire via Google Forms that distributed to 99 radiographers in East Java. Data analysis was carried out using Spearman’s Rank Correlation to find out the correlation between radiographers’ level of knowledge about COVID-19 and preventive behavior towards its transmission in dental radiographic examinations. Results: Radiographers’ level of knowledge about COVID-19 showed 54% good, 42% moderate, and 4% less. Radiographers’ preventive behavior towards the transmission of COVID-19 in dental radiology installations showed 67% good, 30% moderate, and 3% less. Spearman’s Rank Correlation showed weak correlation between the level of knowledge and preventive behavior with the value of sig. (2-tailed) was 0,032 and correlation coefficient was 0,215(+). Conclusions: The level of knowledge of radiographers in East Java about COVID-19 has significant correlation with the preventive behavior of its transmission in dental radiographic examinations with a very weak strength and unidirectional correlation.

Influence of Pixel Size Difference on Imaging Plate Computed Radiography on the Quality of Digital Images in Intraoral Dental Radiography Examination

This research aims to determine the assessment of digital image quality from 18 x 24 cm Imaging Plates of the ST (Standard) and HR (High-Resolution) types by engineering the dimensions on intraoral dental radiographic examinations so that we can determine the differences in image quality for each type of imaging. Plate Computed Radiography. The research method took the form of an experiment using the subjective quality assessment method of digital images in intraoral dental radiographic examinations using the object of patients who carried out the examination and brought a referral letter for 60 patients. Analyze image quality by measuring MSE (Mean et al.), PSNR (Peak Signal to Noise) and PIQE (Perception-based Image Quality Evaluator) score. The results of the research show that the analysis of differences in IP FCR ST and HR types with the Mann-Whitney Test shows the significance values of all digital images (< 0.05), namely: MSE (0.041), PSNR (0.041), PIQE score (0.012), meaning that there is a difference in digital image quality between IP FCR ST and HR, while with subjective analysis the respondents gave IP FCR HR results of 57.14% and IP FCR ST 42.86%. The difference in the results of the two types shows that IP FCR HR can be used as the main alternative in selecting IP FCR in intraoral dental radiographic examinations).

Awareness of Biological Hazards and Radiation Protection Techniques of Dental Imaging Among Dental students in South Chennai-A Questionnaire study

Abstract: Aim and objectives: To assess the attitude and awareness about Biological Hazards of dental X-rays and appropriate radiographic protection techniques of dental imaging among dental students in South Chennai. Materials and methods: The study participants comprised of 100 undergraduate dental students (pre-clinical & clinical) in South Chennai, whose curriculum includes X-ray physics. The information was collected via an online 15 structured multiple choices questionnaires. Statistical analysis was performed using Pearson correlation co-efficient test to know the validity of questionnaire and any P≤0.05 was considered statistically significant. Results: Of all the 100 undergraduate dental students enrolled in this study, 80% of dental students were aware of harmful effects of dental X-rays. While majority of dental students (25%) were unaware about which speed film used to reduce radiation exposure. It was found less percentage of dental students (47%) were aware of the contraindication of radiography during first trimester of pregnancy. However, majority of dental students (68%) were aware of position-distance rule and remaining were unaware about this rule. Conclusion: This study highlights significant awareness among dental students in South Chennai regarding biological hazards and radiation protection techniques in dental imaging. The majority demonstrated a commendable understanding of crucial aspects, such as the harmful effects of dental x-rays, radiation protection recommendations, and the composition of the intensifying screen. However, variations in knowledge were observed, particularly concerning radiation dosage and contraindications during pregnancy. This underscores the importance of continuous education and training in radiation safety within the dental community.

Detection of Cavities from Dental Panoramic X-ray Images Using Nested U-Net Models

Dental caries is one of the most prevalent and chronic diseases worldwide. Dental X-ray radiography is considered a standard tool and a valuable resource for radiologists to identify dental diseases and problems that are hard to recognize by visual inspection alone. However, the available dental panoramic image datasets are extremely limited and only include a small number of images. U-Net is one of the deep learning networks that are showing promising performance in medical image segmentation. In this work, different U-Net models are applied to dental panoramic X-ray images to detect caries lesions. The Detection, Numbering, and Segmentation Panoramic Images (DNS) dataset, which includes 1500 panoramic X-ray images obtained from Ivisionlab, is used in this experiment. The major objective of this work is to extend the DNS Panoramic Images dataset by detecting the cavities in the panoramic image and generating the binary ground truth of this image to use as the ground truth for the evaluation of models. These ground truths are revised by experts to ensure their robustness and correctness. Firstly, we expand the Panoramic Images (DNS) dataset by detecting the cavities in the panoramic images and generating the images’ binary ground truth. Secondly, we apply U-Net, U-Net++ and U-Net3+ to the expanded DNS dataset to learn the hierarchical features and to enhance the cavity boundary. The results show that U-Net3+ outperforms the other versions of U-Net with 95% in testing accuracy.

Liability and Medico-Legal Implications in Estimating the Likelihood of Having Attained 14 Years of Age in Pediatric Clinical Practice: A Pilot Study

Accurate methods of age estimation are more essential than ever due to the rise in undocumented individuals without proper identification, often linked to illegal immigration and criminal activities. This absence of reliable records presents challenges within the legal systems, where age thresholds in the context of children’s rights vary across countries. Age 14 has global significance, as established by the UN Convention on the Rights of the Child and the EU for administrative purposes. Accurate age estimation is crucial in medical decisions, reproductive health, and forensics. This study focuses on age estimation via dental radiographs, proposing a method for estimating the likelihood of having attained the age of 14. Orthopantomograms were analyzed from two samples, 191 Italian children (aged 5–15) and 822 Chilean subjects (aged 11–22), using dental maturity indices. These indices evaluated open tooth apices and complete root development. Statistical analysis confirmed the method’s reliability in identifying individuals aged 14 or older, with sex-specific cut-offs. The proposed method particularly advocates an approach based on dental mineralization, which could surpass those relying on bone growth. The collaboration between medical experts, including pediatricians and diagnostic imaging specialists, is vital for standardized age estimation strategies. Ethical concerns regarding radiation exposure and accountability are recognized, although the method’s low radiation doses are deemed acceptable. The proposed method will help health professionals to accurately predict whether or not the 14-year threshold has been reached, opening up new avenues of medico-legal interest and laying the foundations for a legal framework that would allow the pediatrician, when involved, to use a valid and recognized diagnostic approach.

A novel collaborative learning model for mixed dentition and fillings segmentation in panoramic radiographs

IntroductionIt is critical for dentists to identify and differentiate primary and permanent teeth, fillings, dental restorations and areas with pathological findings when reviewing dental radiographs to ensure that an accurate diagnosis is made and the optimal treatment can be planned. Unfortunately, dental radiographs are sometimes read incorrectly due to human error or low-quality images. While secondary or group review can help catch errors, many dentists work in practice alone and/or do not have time to review all of their patients’ radiographs with another dentist. Artificial intelligence may facilitate the accurate interpretation of radiographs. To help support the review of panoramic radiographs, we developed a novel collaborative learning model that simultaneously identifies and differentiates primary and permanent teeth and detects fillings. MethodsWe used publicly accessible dental panoramic radiographic images and images obtained from the University of Missouri–Kansas City, School of Dentistry to develop and optimize two high-performance classifiers: (1) a system for tooth segmentation that can differentiate primary and permanent teeth and (2) a system to detect dental fillings. ResultsBy utilizing these high-performance classifiers, we created models that can identify primary and permanent teeth (mean average precision [mAP] 95.32 % and performance [F-1] 92.50 %), as well as their associated dental fillings (mAP 91.53 % and F-1 91.00 %). We also designed a novel method for collaborative learning that utilizes these two classifiers to enhance recognition performance (mAP 94.09 % and F-1 93.41 %). ConclusionsOur model improves upon the existing machine learning models to simultaneously identify and differentiate primary and permanent teeth, and to identify any associated fillings. Clinical SignificanceHuman error can lead to incorrect readings of panoramic radiographs. By developing artificial intelligence and machine learning methods to analyze panoramic radiographs, dentists can use this information to support their radiograph interpretations, help communicate the information to patients, and assist dental students learning to read radiographs.

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.

Prevalence and Severity of Ectopic Eruption of First Permanent Molars in Pediatric Patients in Makkah, Saudi Arabia.

Background Early detection and management of ectopic eruption (EE) of first permanent molars (FPMs) are crucial to avoid complicated treatments later. Aim This study aimed to assess the prevalence and severity of EE of FPMs among children in Makkah, Saudi Arabia. Methods This retrospective study was based on a radiographic evaluation of 1,008 dental panoramic radiographs performed for children attending the Dental Educational Hospital at Umm Al-Qura University and the Security Forces Hospital in Makkah. Patients' age, sex, tooth location, and severity of EE were assessed. The study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline for cross-sectional studies. Results Of the 1,008 reviewed cases, 18 (1.79%) were diagnosed with EE of FPMs. Among the 11 male patients, 81.82% showed severe EE, while 57.14% exhibited moderately severe EE among the seven female patients. The prevalences of EE in the maxilla and mandible were 1.59% and 0.20%, respectively. In contrast, the occurrence of EE of FPMs was similar between the right and left sides. Conclusion In this study, the prevalence of EE of FPMs among children in Makkah was 1.79%. The frequency and severity were both greater in male patients compared to female patients. While significantly more EE of FPMs was observed in the maxilla than in the mandible, there was no significant difference between the right and left sides.

Deep Recurrent Neural Network-Based Assessment of Human Dental Age and Gender from Dental Radiographs

The First Chapter's introduction discussed the ability to determine a person's gender and dental age with great accuracy and efficiency is made possible by this technology. It has also done a study that aims to leverage the groundbreaking advantages of deep learning in the dental age and gender evaluation by providing an accurate and automated approach that goes beyond the constraints of traditional methods. The Second Chapter's Literature Review explained Deep Learning Applications in Dental Radiography and Traditional Methods for Dental Age and Gender Assessment and Datasets and Annotations for Dental Radiographs. It has also done Temporal Dependencies in Dental Radiographs. The Third Chapter Methodology discussed that dental radiography data contains rich environmental information that necessitates a nuanced comprehension; this study employs an interpretivist research philosophy. It has also been done to examine pre-existing ideas and models in the context of tooth age and gender evaluation, a deductive approach is used in this study.

Dental Radiography in Age Determination: Contemporary Methods and Trends

The determination of an individual's age assumes paramount significance in forensic and legal contexts, necessitating the utilization of diverse techniques. Dental radiography emerges as a non-invasive approach for determining age-related dental changes. This method grants a comprehensive analysis of various dental features to identify an individual’s precise age, place them within designated age ranges, or define whether they exceed or subordinate to specific age thresholds. This review summarizes age estimation methodologies using dental radiography and conducts the investigations into contemporary trends by reviewing relevant studies published in Pubmed between 2020 and 2023. Age categorization delineates into three distinct phases: pre-natal, neo-natal, and post-natal; childhood and adolescence; and adulthood. Panoramic radiography becomes the predominant radiographic modality, with the Demirjian method is more commonly known for age estimation age in the initial two phases. In contrast, adulthood age estimation relies on anatomical changes. Significantly, artificial intelligence (AI) technology has recently attracted attention for age estimation, yielding promising results. AI demonstrates the potential to enhance the accuracy of conventional methodologies, diminishing human errors and mitigating associated workload burdens, offering inventive ground for future advancements.

Proper Timing and Manipulation of Self – Processing Dental Radiographs in Baghdad Weather (Clinical Comparative study)

Poor image quality in self processed films is not an uncommon problem despite following the manufacturer's instructions, this matter has its influence on the ultimate diagnosis when the radiographic examination is the backbone in this diagnosis. The present study was carried out to investigate this problem and to provide a guideline measure to overcome it. One hundred fifty films were processed using three different manipulations: message, shaking and a combination of both, the resultant film quality was measured and comparison was made the results showed that combination of message and shaking procedure yield significantly better results regarding image quality in comparison to the other two procedures.

Comparative study of 360° virtual reality and traditional two-dimensional video in nonface-to-face dental radiology classes: focusing on learning satisfaction and self-efficacy

BackgroundAcquiring adequate theoretical knowledge in the field of dental radiography (DR) is essential for establishing a good foundation at the prepractical stage. Currently, nonface-to-face DR education predominantly relies on two-dimensional (2D) videos, highlighting the need for developing educational resources that address the inherent limitations of this method. We developed a virtual reality (VR) learning medium using 360° video with a prefabricated head-mounted display (pHMD) for nonface-to-face DR learning and compared it with a 2D video medium.MethodsForty-four participants were randomly assigned to a control group (n = 23; 2D video) and an experimental group (n = 21; 360° VR). DR was re-enacted by the operator and recorded using 360° video. A survey was performed to assess learning satisfaction and self-efficacy. The nonparametric statistical tests comparing the groups were conducted using SPSS statistical analysis software.ResultsLearners in the experimental group could experience VR for DR by attaching their smartphones to the pHMD. The 360° VR video with pHMD provided a step-by-step guide for DR learning from the point of view of an operator as VR. Learning satisfaction and self-efficacy were statistically significantly higher in the experimental group than the control group (p < 0.001).ConclusionsThe 360° VR videos were associated with greater learning satisfaction and self-efficacy than conventional 2D videos. However, these findings do not necessarily substantiate the educational effects of this medium, but instead suggest that it may be considered a suitable alternative for DR education in a nonface-to-face environment. However, further examination of the extent of DR knowledge gained in a nonface-to-face setting is warranted. Future research should aim to develop simulation tools based on 3D objects and also explore additional uses of 360° VR videos as prepractical learning mediums.

Dental age assessment in the living: a comparison of two common stage classifications for assessing radiographic visibility of the root canals in mandibular third molars.

After dentition is complete, degenerative tooth characteristics can be used for dental age assessment. Radiological assessment of the visibility of the root canals of the mandibular third molars in dental panoramic radiographs (DPRs) is known to be one such suitable feature. Essentially, two different stage classifications are available for evaluating the visibility of the root canals of mandibular third molars in the DPR. The aim of this study was to determine if one method outperforms the other. Therefore, the 2010 method of Olze et al. was directly compared to the 2017 method of Lucas et al. in the 2020 modification of Al Qattan et al. To this end, 233 DPRs from 116 females and 117 males aged 20.0 to 40.9 years were evaluated by three independent experienced examiners. In addition, one examiner ran two independent evaluations. Correlation between age and stage was investigated, and the inter- and intra-rater reliability was estimated for both methods. Correlation between age and stage was higher with the Olze method (Spearman rho 0.388 [95% CI 0.309, 0.462], males and 0.283 [95% CI 0.216, 0.357], females) than the Lucas method (0.212 [95% CI 0.141, 0.284], males and 0.265 [95% CI 0.193, 0.340], females). The intra-rater repeatability of the Olze method (Krippendorff's α = 0.576 [95% CI 0.508, 0.644], males and α = 0.592 [95% CI 0.523, 0.661], females) was greater than that for the Lucas method (intra-rater α = 0.422 [95% CI 0.382, 0.502], males and α = 0.516 [95% CI 0.523, 0.661], females). Inter-rater reproducibility was also greater for the Olze method (α = 0.542 [95% CI 0.463, 0.620], males and α = 0.533 [95% CI 0.451, 0.615], females) compared to the Lucas method (α = 0.374 [95% CI 0.304, 0.443], males and α = 0.432 [95% CI 0.359, 0.505], females). The method of Olze et al. was found to present marginal advantages to the Lucas et al. method across all examinations and may be a more appropriate method for application in future studies.

Optimization of deep neural networks for multiclassification of dental X-rays using transfer learning

ABSTRACT In this work, the segmented dental X-ray images obtained by dentists have been classified into ideal/minimally compromised edentulous area (no clinical treatment needed immediately), partially/moderately compromised edentulous area (require bridges or cast partial denture) and substantially compromised edentulous area (require complete denture prosthesis). A total of 116 image dental X-ray dataset is used, of which 70% of the image dataset is used for training the convolutional neural network (CNN) while 30% is used sfor testing and validation. Three pretrained deep neural networks (DNNs; SqueezeNet, ResNet-50 and EfficientNet-b0) have been implemented using Deep Network Designer module of Matlab 2022. Each of these CNNs were trained, tested and optimised for the best possible accuracy and validation of dental images, which require an appropriate clinical treatment. The highest classification accuracy of 98% was obtained for EfficientNet-b0. This novel research enables the implementation of DNN parameters for automated identification and labelling of edentulous area, which would require clinical treatment. Also, the performance metrics, accuracy, recall, precision and F1 score have been calculated for the best DNN using confusion matrix.

Sydney Blackman (1898-1971): a pioneer in dental radiology and radiography.

Sydney Blackman, was a consultant radiologist at the Royal Dental Hospital of London between 1931 and 1967. He contributed much to the advance of dento-maxillofacial radiology. His development of the 'rotograph' as the first commercially available dental panoramic machine established dental radiology on a level with medical radiology. Sydney was a major contributor to the establishment of the British Society of Dental and Maxillofacial Radiology.

Do we need to improve the general oral health of our population to reduce the risk of Infective Endocarditis?

Abstract Background Emphasis is placed on the link between oral health and Infective Endocarditis (IE), particularly around odontogenic infections and specific dental procedures. The Adult Dental Health Survey in 2009 showed 45% of adults in the UK required dental intervention typically associated with developing IE. Antibiotic prophylaxis in this context is not without controversy. With gum and periodontic disease endemic, is the modern issue one of generalised poor oral health as a risk factor for IE? Purpose To determine the impact of oral health on risk and outcome in IE. Terminology SDCEP published dental guidelines of when to consider AP against IE for dental procedures. It identifies dental procedures that can cause transient bacteraemia. Methodology Data were collected from October 2020-Januaury 2023 in 123 patients with confirmed IE at a central London cardiac surgical centre. Patients had a focussed dental assessment with history, intraoral examination and dental panoramic radiograph to determine the risk and extent of oral disease. Results Average age was 54.2years. 77/123 (63%) had native IE (NVE) and 46 (37%) prosthetic (PVE). 17 (37%) cultured oral organisms from blood (Table 1); 8 (47%) of these organisms are associated with active dental disease (decay, periodontal disease, dental infection). Organisms can be found in Table 2. However, 93/123 (76%) had oral caries requiring dental treatment, irrespective of typical portal of entry for that organism. The proposed dental treatment posed a high risk for causing IE in 70%. 93/123 (76%) needed further dental treatment within six months rendering only 12% of patients orally stable. Oral organisms were seen more frequently in NVE (41/77) vs PVE (17/46). There was no difference in the incidence of dental disease requiring treatment (77% NVE vs 74% PVE). Whilst 27% of PVE had had recent (within 4 months) dental treatment compared to 73% NVE, only 1 PVE patient grew an oral commensal compared to 82% of NVE patients. Conclusion Prevention of IE is key to reducing the morbidity and mortality of this disease. We see oral commensals causing IE in 47%, with only 12% of this modern IE cohort dentally stable. Despite this, there was no difference in rates of dental disease requiring invasive treatment when stratified by oral vs non-oral organisms. A robust public health strategy is required to reduce the risk of oral disease driving IE.

Machine learning in the detection of dental cyst, tumor, and abscess lesions

Background and ObjectiveDental panoramic radiographs are utilized in computer-aided image analysis, which detects abnormal tissue masses by analyzing the produced image capacity to recognize patterns of intensity fluctuations. This is done to reduce the need for invasive biopsies for arriving to a diagnosis. The aim of the current study was to examine and compare the accuracy of several texture analysis techniques, such as Grey Level Run Length Matrix (GLRLM), Grey Level Co-occurrence Matrix (GLCM), and wavelet analysis in recognizing dental cyst, tumor, and abscess lesions.Materials & MethodsThe current retrospective study retrieved a total of 172 dental panoramic radiographs with lesion including dental cysts, tumors, or abscess. Radiographs that failed to meet technical criteria for diagnostic quality (such as significant overlap of teeth, a diffuse image, or distortion) were excluded from the sample. The methodology adopted in the study comprised of five stages. At first, the radiographs are improved, and the area of interest was segmented manually. A variety of feature extraction techniques, such GLCM, GLRLM, and the wavelet analysis were used to gather information from the area of interest. Later, the lesions were classified as a cyst, tumor, abscess, or using a support vector machine (SVM) classifier. Eventually, the data was transferred into a Microsoft Excel spreadsheet and statistical package for social sciences (SPSS) (version 21) was used to conduct the statistical analysis. Initially descriptive statistics were computed. For inferential analysis, statistical significance was determined by a p value < 0.05. The sensitivity, specificity, and accuracy were used to find the significant difference between assessed and actual diagnosis.ResultsThe findings demonstrate that 98% accuracy was achieved using GLCM, 91% accuracy using Wavelet analysis & 95% accuracy using GLRLM in distinguishing between dental cyst, tumor, and abscess lesions. The area under curve (AUC) number indicates that GLCM achieves a high degree of accuracy. The results achieved excellent accuracy (98%) using GLCM.ConclusionThe GLCM features can be used for further research. After improving the performance and training, it can support routine histological diagnosis and can assist the clinicians in arriving at accurate and spontaneous treatment plans.

Third Molar Eruption in Dental Panoramic Radiographs as a Feature for Forensic Age Assessment-Presentation of a New Non-Staging Method Based on Measurements.

The evaluation of third molar eruption in dental panoramic radiographs (DPRs) constitutes an evidence-based approach for forensic age assessment in living individuals. Existing methodologies involve staging morphological radiographic findings and comparing them to reference populations. Conversely, the existing literature presents an alternative method where the distance between third molars and the occlusal plane is measured on dental plaster models. The aim of this study was to adapt this measurement principle for DPRs and to determine correlation between eruption and chronological age. A total of 423 DPRs, encompassing 220 females and 203 males aged 15 to 25 years, were examined, including teeth 38 [FDI] and 48. Two independent examiners conducted the measurements, with one examiner providing dual assessments. Ultimately, a quotient was derived by comparing orthogonal distances from the mesial cementoenamel junctions of the second and third molars to a simplified radiological occlusal plane. This quotient was subsequently correlated with the individual's age. We estimated correlations between age and quotients, as well as inter- and intra-rater reliability. Correlation coefficients (Spearman's rho) between measurements and individuals' ages ranged from 0.555 to 0.597, conditional on sex and tooth. Intra-rater agreement (Krippendorf's alpha) ranged from 0.932 to 0.991, varying according to the tooth and sex. Inter-rater agreement ranged from 0.984 to 0.992, with distinctions drawn for different teeth and sex. Notably, all observer agreement values fell within the "very good" range. In summary, assessing the distance of third molars from a simplified occlusal plane in DPRs emerges as a new and promising method for evaluating eruption status in forensic age assessment. Subsequent reference studies should validate these findings.

Knowledge, Attitude And Perception About The Effect Of Radiation And Its Safety Protocol Among Dental Students

Dental radiography is indispensable for diagnosing and treating oral conditions, but it carries inherent radiation risks. Dental students must grasp radiation's effects and safety protocols to ensure patient and practitioner safety.