Panoramic Radiographs Research Articles

Investigating the relationship between the type of occlusion and mandibular radiomorphometric indices in patients with normal facial height.

The ability to predict the extent and direction of horizontal growth of the patient's face at an early age allows clinicians to timely identify cases that require treatment during the growth period and to ensure that appropriate treatment can be administered. The aim of this study was to investigate the role of the antegonial angle, gonial angle, antegonial notch depth, and the type of the antegonial notch on predicting horizontal facial growth in individuals with normal facial height. In this descriptive analytic study, lateral and panoramic radiographs of 180 patients aged 17-30 years with normal facial height who were referred to the School of Dentistry of Qazvin University of Medical Sciences and private dental clinics were investigated. The indices of the gonial angle, antegonial angle, type of antegonial notch, and antegonial notch depth were plotted bilaterally on tracing paper and subsequently measured using panoramic radiographs. Additionally, the relationship between the desired radiomorphometric indices and the occlusion class in the samples was investigated. There was a statistically significant relationship between the occlusion class and the antegonial notch depth (analysis of variance (ANOVA), p = 0.048), as well as the antegonial angle (Kruskal-Wallis test, p = 0.002). The results of this study indicate that the antegonial notch depth and angle indices can be clinically valuable in predicting the process of mandibular growth and developing appropriate treatment plans.

Observation of peri-implant bone loss rates in patients visiting dentist—A retrospective evaluation of patients of a faculty hospital for one year

Background Many studies have been published on the relationship between different parameters with marginal bone loss around implants. The results of these studies vary, but some variables such as smoking or certain systemic conditions are more likely to predispose or exacerbate the resorption around implants. The aim of this study was to determine the rates of implants with radiographically detected marginal bone loss and to determine whether there is a relationship between the severity of destruction and certain risk factors like location of implant, restoration type, systemic condition, age, sex or smoking habits. Materials and Methods Panoramic radiographs obtained for 1 year were examined. Patients with bone loss around the implant were classified according to the region of implant placement, type of restoration on implants, systemic diseases, and smoking habits. The rate of bone loss around the implants was recorded as the resorption score. Double and multiple comparison tests were applied to observe whether the resorption scores were related to the variables. Results Of 17,352 patients, 1,465 had at least one implant, and 1,116 of these had no bone loss. A total of 181 patients (863 implants) included in the study, there was a weak correlation between age (p = 0.017) and resorption rate. Implants supporting bridge restorations had higher resorption scores. Gender, age, and systemic conditions alone are not effective in increasing peri-implant bone loss (p < 0.05); therefore, placing implants in the mandible (p = 0.020) or using implants to support bridge restorations(p = 0.027) may make implants more vulnerable to resorption.

Association between dental agenesis and facial morphology. A cross-sectional study in France.

Knowing the features of dental evolution and facial morphology, marked by an increase in the prevalence of agenesis and a tendency towards verticalization of the face, the main objective of this cross-sectional observational study was to assess the correlation between the occurrence of agenesis (of 3rd molars or other teeth) and facial morphology in the French population. The study was conducted at the University Hospital of Rennes, France, from June 2022 to October 2022. Patients aged 12-18 years who underwent a global orthodontic assessment were included. Data collected from medical examinations, panoramic, anteroposterior, and lateral x-rays were analyzed for cephalometric and dental features. The association between morphological parameters and agenesis of wisdom teeth or other teeth was assessed by univariate and multivariate analysis. The study included 259 patients, of whom 89 presented agenesis. Logistic regression analyses identified several morphological parameters associated with agenesis. In the multivariate model, a negative correlation was found between tooth agenesis and FMA (OR = 0.85; p < 0. 001), facial axis (OR = 0.92; p = 0.040), and a positive one with SNB (OR = 1.17; p = 0.023), ANS-Xi-Pm (OR = 1.12; p = 0.013). This study highlights the correlation between agenesis occurrence and specific cephalometric parameters in the French population. The findings suggest that transverse constriction of the maxilla, facial divergence, and anterior projection of the chin symphysis are associated with agenesis. Trial registration number: Opinion n°22.103, dated 06.04.2022.

Cone-beam computed tomographic evaluation of styloid process: a retrospective study of 498 patients with maxillofacial diseases

PurposeThe purpose of this study was to assess the structural characteristics of the styloid process (SP) using cone-beam computed tomography (CBCT) examination in patients with maxillofacial diseases. The study aimed to determine the prevalence of elongated styloid process (ESP) and its relationship to gender in the study population. Radiographic records of 498 subjects were evaluated retrospectively. Radiological examinations including measurements of the structure, length, volume, and angulations of styloid process were performed on CBCT images. Males had significantly longer styloid process in both sides than females in the study population and a strong positive linear relationship was found between left and right sides’ process length (r = 0.83; p < 0.001). The mean internal oblique angle of styloid process differed between genders, but there were no statistically significant differences in the mean anterior oblique angle. Out of 498 subjects, 62 (24.13%) females and 101 (41.91%) males had elongated left styloid process (≥ 30 mm), while 75 (29.18%) and 90 (37.34%) had right side respectively.MethodsRadiographic records of 498 subjects were evaluated retrospectively. Radiological examinations including measurements of the structure, length, volume, and angulations of styloid process were performed on CBCT images.ResultsMales had significantly longer styloid process in both sides than females in the study population and a strong positive linear relationship was found between left and right sides’ process length (r = 0.83;p < 0.001). The mean internal oblique angle of styloid process differed between genders, but there were no statistically significant differences in the mean anterior oblique angle. Out of 498 subjects, 62 (24.13%) females and 101 (41.91%) males had elongated left styloid process (≥30 mm), while 75 (29.18%) and 90 (37.34%) had right side respectively.ConclusionsThis study presents the CBCT as an alternative method to CT or panoramic radiographs for the measurement and the assessment of the styloid process. Within the study in 498 subjects in China, it was observed that the males, on average, had significantly longer styloid process and narrower internal oblique angle than females either in left or right side. Around 33% of the study population had ESP.

Adapting SAM2 Model from Natural Images for Tooth Segmentation in Dental Panoramic X-Ray Images

Dental panoramic X-ray imaging, due to its high cost-effectiveness and low radiation dose, has become a widely used diagnostic tool in dentistry. Accurate tooth segmentation is crucial for lesion analysis and treatment planning, helping dentists to quickly and precisely assess the condition of teeth. However, dental X-ray images often suffer from noise, low contrast, and overlapping anatomical structures, coupled with limited available datasets, leading traditional deep learning models to experience overfitting, which affects generalization ability. In addition, high-precision deep models typically require significant computational resources for inference, making deployment in real-world applications challenging. To address these challenges, this paper proposes a tooth segmentation method based on the pre-trained SAM2 model. We employ adapter modules to fine-tune the SAM2 model and introduce ScConv modules and gated attention mechanisms to enhance the model’s semantic understanding and multi-scale feature extraction capabilities for medical images. In terms of efficiency, we utilize knowledge distillation, using the fine-tuned SAM2 model as the teacher model for distilling knowledge to a smaller model named LightUNet. Experimental results on the UFBA-UESC dataset show that, in terms of performance, our model significantly outperforms the traditional UNet model in multiple metrics such as IoU, effectively improving segmentation accuracy and model robustness, particularly with limited sample datasets. In terms of efficiency, LightUNet achieves comparable performance to UNet, but with only 1.6% of its parameters and 24.0% of the inference time, demonstrating its feasibility for deployment on edge devices.

Detection of three-rooted mandibular first molars on panoramic radiographs using deep learning

This study aimed to develop a deep learning system for the detection of three-rooted mandibular first molars (MFMs) on panoramic radiographs and to assess its diagnostic performance. Panoramic radiographs, together with cone beam computed tomographic (CBCT) images of the same subjects, were retrospectively collected from 730 patients, encompassing a total of 1444 MFMs (367 teeth were three-rooted and the remaining 1077 teeth were two-rooted). Five convolutional neural network (CNN) models (ResNet-101 and − 50, DenseNet-201, MobileNet-v3 and Inception-v3) were employed to classify three- and two-rooted MFMs on the panoramic radiographs. The diagnostic performance of each model was evaluated using standard metrics, including accuracy, sensitivity, specificity, precision, negative predictive value, and F1 score. Receiver operating characteristic (ROC) curve analyses were performed, with the CBCT examination taken as the gold standard.Among the five CNN models evaluated, ResNet-101 demonstrated superior diagnostic performance, and the AUC value attained was 0.907, significantly higher than that of all other models (all P < 0.01). The accuracy, sensitivity, and specificity were 87.5%, 83.6%, and 88.9%, respectively. DenseNet-201, however, showed the lowest diagnostic performance among the five models (all P < 0.01), with an AUC value of 0.701 and an accuracy of 73.2%. Overall, the performance of the CNNs diminished when using image patches containing only the distal half of MFMs, with AUC values ranging between 0.680 and 0.800. In contrast, the diagnostic performance of the two clinicians was poorer, with AUC values of only 0.680 and 0.632, respectively. In conclusion, the CNN-based deep learning system exhibited a high level of accuracy in the detection of three-rooted MFMs on panoramic radiographs.

Morphometric Changes in a Sample of Various Age Groups of the Thai Population: A Panoramic Radiographic Study

Objective The current investigation was conducted to examine the alterations in oral conditions and panoramic image parameters that occur with age. Methods The study sample consisted of 300 male participants and 300 female participants, ranging in age from 10 to 69 years. The age-related changes in intraoral circumstances and morphometric parameters, as observed in panoramic photographs, were explored. A two-way parametric analysis of variance was used to examine the effects of age group and sex. The frequencies and percentages were calculated and compared among groups using the chi-squared test for category variables. A p-value below 0.05 was considered to be statistically significant. Results The prevalence of intraoral conditions showed a positive correlation with advancing age. Males exhibited significantly higher crown, root, and tooth lengths in both the upper and lower canines. There was a significant difference between males and females in terms of the pulp area, the pulp-tooth ratio of the upper first molars, and the pulp area of the lower first molars (p &lt; 0.05). The study found a substantial correlation between the radiomorphometric parameters and age, with similar trends observed in both males and females. Conclusion The findings of our research have the potential to contribute to the evaluation of an individual's age category and offer insights into the patterns of several observable factors on panoramic radiographs.

Development and validation of a deep learning algorithm for the classification of the level of surgical difficulty in impacted mandibular third molar surgery

The aim of this study was to develop and validate a convolutional neural network (CNN) algorithm for the detection of impacted mandibular third molars in panoramic radiographs and the classification of the surgical extraction difficulty level. A dataset of 1730 panoramic radiographs was collected; 1300 images were allocated to training and 430 to testing. The performance of the model was evaluated using the confusion matrix for multiclass classification, and the actual scores were compared to those of two human experts. The area under the precision–recall curve of the YOLOv5 model ranged from 72% to 89% for the variables in the surgical difficulty index (level of occlusion, available retromolar space, impaction angle, number of roots, root curvature, and relationship to the second molar). The area under the receiver operating characteristic curve showed promising results of the YOLOv5 model for classifying third molars into three surgical difficulty levels (micro-average AUC 87%). Furthermore, the algorithm scores demonstrated good agreement with the human experts. In conclusion, the YOLOv5 model has the potential to accurately detect and classify the position of mandibular third molars, with high performance for every criterion in radiographic images. The proposed model could serve as an aid in improving clinician performance and could be integrated into a screening system.

Enhanced Panoramic Radiograph-Based Tooth Segmentation and Identification Using an Attention Gate-Based Encoder–Decoder Network

Background: Dental disorders are one of the most important health problems, affecting billions of people all over the world. Early diagnosis is important for effective treatment planning. Precise dental disease segmentation requires reliable tooth numbering, which may be prone to errors if performed manually. These steps can be automated using artificial intelligence, which may provide fast and accurate results. Among the AI methodologies, deep learning has recently shown excellent performance in dental image processing, allowing effective tooth segmentation and numbering. Methods: This paper proposes the Squeeze and Excitation Inception Block-based Encoder–Decoder (SE-IB-ED) network for teeth segmentation in panoramic X-ray images. It combines the InceptionV3 model for encoding with a custom decoder for feature integration and segmentation, using pointwise convolution and an attention mechanism. A dataset of 313 panoramic radiographs from private clinics was annotated using the Fédération Dentaire Internationale (FDI) system. PSPL and SAM augmented the annotation precision and effectiveness, with SAM automating teeth labeling and subsequently applying manual corrections. Results: The proposed SE-IB-ED network was trained and tested using 80% training and 20% testing of the dataset, respectively. Data augmentation techniques were employed during training. It outperformed the state-of-the-art models with a very high F1-score of 92.65%, mIoU of 86.38%, and 92.84% in terms of accuracy, precision of 92.49%, and recall of 99.92% in the segmentation of teeth. Conclusions: According to the results obtained, the proposed method has great potential for the accurate segmentation of all teeth regions and backgrounds in panoramic X-ray images.

Investigation of the association between COX-2 polymorphisms and external apical root resorption in orthodontically treated patients.

To evaluate the association between single nucleotide polymorphisms (SNPs) in cyclooxygenase-2 (COX-2) and external apical root resorption (EARR) after orthodontic treatment. This study analyzed the orthodontic records of patients from Regensburg University Hospital and two collaborating private practices. EARR was analyzed in the maxillary central incisors and first mandibular molars using pre- and post-treatment cephalometric and panoramic radiographs, respectively. Genomic DNA was obtained from salivary oral cells. Two SNPs in COX-2, rs5275 (3 prime untranslated region variant) and rs689466 (upstream transcript variant, downstream transcript variant), were genotyped using real-time Polymerase chain reaction (PCR). The linear regression models were fitted to assess the effects of the evaluated SNPs at a significance level of 5%. A total of 143 biologically unrelated patients (mean age: 13.5 ± 4.5 years) were included in the study. The F-test showed that only the regression for EARR in the mandibular molars in the dominant model for rs5275 G allele had a good fit (F = 3.50, P = 0.034). The model showed that individuals carrying at least one G allele for rs5275 had a 17% increase in EARR in the molars compared to common AA homozygotes (exp(B) = 1.17; 95% CI: 1.04, 1.32; P = 0.010). Although an effect of the G allele (rs5275) was detected, this effect did not explain much of the variability in EARR (adjusted R2 = 0.04). The results suggested that patients carrying the polymorphic form of the rs5275 were more likely to have EARR in the mandibular molars; therefore, COX-2 could have an effect on EARR. The knowledge of the genetic background concerning EARR after orthodontic treatment would aid in clinical practice to screen for associated genetic factors and potentially aid its prevention and management, considering that patients' genetic profiles would soon be available.

DAC: Differentiable Auto-Cropping in Deep Learning

Auto-cropping, the process of automatically adjusting the boundaries of an image to focus on the region of interest, is crucial to improving the diagnostic quality of dental panoramic radiographs. Its importance lies in its ability to standardize the size of different input images with minimal loss of information, thus ensuring consistency and improving the performance of subsequent image-processing tasks. Despite the widespread use of CNNs in many studies, research on auto-cropping for different-sized images remains limited. This study aims to explore the potential of differentiable auto-cropping in dental panoramic radiographs. A unique dataset of 20,973 dental panoramic radiographs, mostly with a resolution of 2836×1536 or close, divided into five classes by 3 dentists, was used, which is the same dataset from the previous study (Top et al. 2023). ResNet-101 model, which was the most successful network for the dataset (Top et al. 2023), was used for the evaluation. To reduce variance, the model was evaluated using 10-fold cross-validation for both non-auto-cropped and auto-cropped trainings. Data augmentation was also used to produce more accurate and robust results. For auto-cropped training, it was adjusted to be much less effective than the non-auto-cropped one. Accuracy was improved by 1.8%, from 92.7% to 94.5%, thanks to the proposed auto-crop optimization developed to reduce dataset-related issues. Its macro-average AUC was also raised from 0.989 to 0.993. The proposed auto-crop optimization can be implemented as a trainable network layer in an end-to-end CNN and can be used for other problems as well. Increasing the accuracy from 92.7% to 94.5% is a very challenging task due to diminishing returns, as there is little room for improvement. The results show the potential of the proposed differentiable auto-crop algorithm and encourages its use in different fields.

Evaluation of Panoramic Radiography Diagnostic Accuracy in the Assessment of Interdental Alveolar Bone Loss Using CBCT.

Alveolar bone loss (ABL) and periodontal lesions are common diseases that have an undeniable effect on teeth maintenance and health. Current diagnostic methods include probing, intraoral radiography, and panoramic radiography; each has its limitations. In this study, we aimed to assess the diagnostic accuracy of panoramic radiography in the diagnosis of interdental ABL. In this cross-sectional study, panoramic and cone-beam computed tomography (CBCT) images from 80 patients were collected from the archives of an oral and maxillofacial radiology center. The amount of ABL was obtained by measuring the distance from the Cemento-Enamel Junction (CEJ) to the alveolar bone crest on both panoramic and CBCT images. Patients were divided into healthy (ABL ≤ 2 mm) and diseased (2 mm < ABL) groups in terms of periodontal disease. There was no statistically significant difference in the average ABL in the premolar, maxillary molar, and mandibular molar areas between the two techniques. However, in other areas, the ABL size was significantly lower in the panoramic view (p < 0.05). Also, the panoramic technique correctly recognized 89.1% of normal and 88.4% of ABL cases. The overall accuracy of panoramic radiography in the diagnosis of ABL was 85%, indicating the good accuracy of this technique. In maxilla, the highest diagnostic accuracy of the panoramic technique was in the molars, and the lowest was in the incisors. In the mandible, the highest and lowest diagnostic accuracy of the panoramic technique was related to molars and premolars, respectively. According to the kappa statistic, there was a significant good to very good agreement between the two types of techniques in all maxillary areas (p < 0.001). Panoramic radiography is accurate in showing ABL. Measuring ABL in the posterior mandibular areas in panoramic radiographs is quite reliable; however, in general, digital panoramic radiography shows less ABL than the actual amount.

Mapping the Use of Artificial Intelligence-Based Image Analysis for Clinical Decision-Making in Dentistry: A Scoping Review.

Artificial intelligence (AI) is an emerging field in dentistry. AI is gradually being integrated into dentistry to improve clinical dental practice. The aims of this scoping review were to investigate the application of AI in image analysis for decision-making in clinical dentistry and identify trends and research gaps in the current literature. This review followed the guidelines provided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). An electronic literature search was performed through PubMed and Scopus. After removing duplicates, a preliminary screening based on titles and abstracts was performed. A full-text review and analysis were performed according to predefined inclusion criteria, and data were extracted from eligible articles. Of the 1334 articles returned, 276 met the inclusion criteria (consisting of 601,122 images in total) and were included in the qualitative synthesis. Most of the included studies utilized convolutional neural networks (CNNs) on dental radiographs such as orthopantomograms (OPGs) and intraoral radiographs (bitewings and periapicals). AI was applied across all fields of dentistry - particularly oral medicine, oral surgery, and orthodontics - for direct clinical inference and segmentation. AI-based image analysis was use in several components of the clinical decision-making process, including diagnosis, detection or classification, prediction, and management. A variety of machine learning and deep learning techniques are being used for dental image analysis to assist clinicians in making accurate diagnoses and choosing appropriate interventions in a timely manner.

Root Growth and Apical Foramen Closure Velocity of Maxillary Permanent Central Incisor in Korean Children

This study aimed to evaluate the growth velocity of root elongation and the timing of apical foramen closure in maxillary permanent central incisors using panoramic radiographs. The study also examined the timing of alveolar eruption and its relationship with root development while considering potential sex-specific differences. The study included 176 patients (94 males, 82 females) from the Seoul National University Dental Hospital, each with three or more panoramic radiographs taken between 2014 and 2023 and analyzed to measure the crown length, total tooth length, root length, and mesiodistal width of the apical foramen. Root lengths were expressed as percentages of the fully developed stage. Scatter plots and polynomial trend lines were used to evaluate the relationship between age and tooth development, with intraobserver reliability assessed using Cohen’ s kappa. Root growth velocity peaked during the early stages of development, with females generally exhibiting earlier and faster apical foramen closure than males. Significant correlations were found between the root length and apical foramen width (Pearson correlation coefficients, males, -0.907; females, -0.887, &lt;i&gt;p&lt;/i&gt; &lt; 0.0001). The mean age for alveolar eruption was 7.27 years for males (N = 16) and 7.10 for females (N = 17), predominantly at Demirjian stage F. The study presents the critical periods of rapid root elongation and apical foramen closure in the maxillary central incisors, underscoring their importance for clinical dental practice. Future research is warranted to provide a more comprehensive understanding of human dental development.

A Rare Case of Peripheral Osteoma of the Alveolar Bone of the Maxilla in a 13-Year-Old Boy

Background: This report aims to augment the presently limited knowledge on the characteristics of jawbone osteomas in children by presenting an exceptionally rare case of this tumor located on the buccal aspect of the alveolar process of the maxilla in a 13-year-old boy. Methods: A well-defined, painless, bony, hard, spherical enlargement on the maxillary alveolar ridge was identified and thoroughly evaluated through clinical examination, panoramic radiographs, CBCT (Cone Beam Computed Tomography) scans, and histopathological analysis. The tumor was surgically removed, and the patient participated in postoperative clinical follow-ups for eight years. Results: Based on the clinical characteristics and CBCT scan findings, a jawbone tumor was suspected. After histopathological analysis, the definitive diagnosis was a peripheral trabecular osteoma. There were no signs of tumor recurrence during the postoperative follow-up period. Conclusions: This report presents the youngest documented case of peripheral osteoma in the maxillary alveolar ridge, and highlights the need to consider this rare lesion in the differential diagnosis of similar pathological changes in this region, even in pediatric patients. The absence of clinical signs of recurrence over eight years of follow-up underscores the long-term stability and favorable prognosis of peripheral jawbone osteoma in children.

Bilateral symmetry-based augmentation method for improved tooth segmentation in panoramic X-rays

Panoramic X-rays are crucial in dental radiology, providing detailed images that are essential for diagnosing and planning treatment for various oral conditions. The advent of automated methods that learn from annotated data promises to significantly aid clinical experts in making accurate diagnoses. However, these methods often require large amounts of annotated data, making the generation of high-quality annotations for panoramic X-rays both challenging and time-consuming. This paper introduces a novel bilateral symmetry-based augmentation method specifically designed to enhance tooth segmentation in panoramic X-rays. By exploiting the inherent bilateral symmetry of these images, our proposed method systematically generates augmented data, leading to substantial improvements in the performance of tooth segmentation models. By increasing the training data size fourfold, our approach proportionately reduces the effort required to manually annotate extensive datasets. These findings highlight the potential of leveraging the symmetrical properties of medical images to enhance model performance and accuracy in dental radiology. The effectiveness of the proposed method is evaluated on three widely adopted deep learning models: U-Net, SE U-Net, and TransUNet. Significant improvements in segmentation accuracy are observed with the proposed augmentation method across all models. For example, the average Dice Similarity Coefficient (DSC) increases by over 8%, reaching 76.7% for TransUNet. Further, comparisons with existing augmentation methods, including rigid transform-based and elastic grid-based techniques, show that the proposed method consistently outperforms them with additional improvements up to 5% in terms of average DSC, with the exact improvement varying depending on the model and training dataset size. We have made the data augmentation codes and tools developed based on our method available at https://github.com/wathoresanket/bilateralsymmetrybasedaugmentation.

Evaluating the Patterns of Impaction in Lower Third Molars and Associated Radiolucent Lesions: An Observational Cross-sectional Study.

The aim of this study was to evaluate radiolucent lesions associated with impacted lower third molars, focusing on their pathological diagnosis and the distribution of sex and age among the affected individuals. This retrospective cross-sectional study was conducted at King Khalid Dental University Hospital, Riyadh, Saudi Arabia, between January 2020 and August 2023. A total of 312 orthopantomography (OPGs) records were examined, covering 549 teeth. Data regarding impaction depth, ramus relationship, angulation classification, and the presence of lesions were analyzed. Out of 549 teeth studied, 103 exhibited radiolucent lesions. These lesions were more frequently observed in male patients compared to female patients, and in individuals aged ≤30 years compared to those aged >30 years, showing significant differences. Most impacted teeth were classified as impaction depth A, indicating a position at the same or higher level relative to the occlusal plane of the second molar. The study found significant associations between the pattern of lower third molar impaction and dental characteristics (impaction depth classification, ramus relationship, angulation classification, and tooth type) with age. There was no significant association between gender and dental characteristics, except for the presence of a lesion. Understanding the patterns of impaction and associated radiolucent lesions can help guide clinical decision-making regarding the management of impacted third molars, particularly in younger patients who may exhibit a higher risk of associated pathologies. How to cite this article: Alshalawi HS, Alkhamees SA, Almuqrin R, et al. Evaluating the Patterns of Impaction in Lower Third Molars and Associated Radiolucent Lesions: An Observational Cross-sectional Study. J Contemp Dent Pract 2024;25(8):790-793.

Investigation of changes caused by Rapid Maxillary Expansion in mandibular bone and temporomandibular joint trabecular structure using fractal analysis

BackgroundThe aim of this study is to evaluate the changes in the trabecular structure of the mandibular bone and temporomandibular joint following Rapid Maxillary Expansion.MethodsThirty-nine patients who received Rapid Maxillary Expansion treatment in the Department of Orthodontics of the Faculty of Dentistry were selected from the archieve for the study. Trabecular structure changes of the angulus mandible, corpus mandible, and condyle regions were examined using fractal analysis on the dental panoramic radiographs. Radiographs taken before the treatment (T0) and at the end of the 3-month retention period (T1) of the patients.ResultsThere was no statistically significant difference between T0 and T1 in the condyle, ramus mandible, and corpus mandible regions depending on time and sex. (p < 0.05)ConclusionThe results of this study showed that Rapid Maxillary Expansion treatment does not have a significant impact on the trabecular structure of the mandible and temporomandibular joint depending on time and gender.

Knowledge and experience in using 2D and 3D diagnostic imaging among dentists in Surabaya

Background: Radiography is essential in dentistry for diagnosis and treatment planning, with two-dimensional imaging such as panoramic and periapical radiographs being commonly used. The advancement to three-dimensional imaging, specifically Cone Beam Computed Tomography (CBCT), provides more detailed visualization but remains underutilized due to limited availability and knowledge among dentists in Indonesia. Purpose: This research is aimed to determine the knowledge and experience of using 2D and 3D diagnostic imaging among dentists in Surabaya. Methods: This research is using an observational descriptive study with a cross-sectional design consisting of 100 dentists who are domiciled in Surabaya and registered with PDGI in Surabaya. Results: The result showed that dentists’ knowledge of 2D diagnostic imaging with a good category has a percentage of 18%, quite good category with 49% and poor category with 33%. Meanwhile, the dentists’ knowledge of 3D diagnostic imaging with a good category has a percentage of 9%, quite good category with 36% and poor category with 55%. Around 88% of respondents have referred their patients for panoramic and periapical radiographs, while only about 22% of dentists have referred their patients for CBCT examinations. Conclusion: The knowledge of dentists regarding 2D diagnostic imaging is quite good, while the knowledge of dentists regarding 3D diagnostic imaging is low. The majority of dentists in Surabaya often refer their patients for panoramic and periapical radiographic examinations compared to referring them for CBCT examinations.

Rehabilitation of the Atrophic Maxillary Arch with Tilted Implants Protocol: A Proposed Classification of Various Situations

Purpose The majority of classifications for implant placement are based on the morphology of the available bone. The positions and angulations of implants in all forms of atrophy are not emphasized. Clinicians find it relatively difficult to insert the posterior tilted fixtures such that maximum anchorage of the bone can be obtained for enhanced primary stability, and there is unpredictability since the exit points (implant apex position) of these implants are not known and visualized prior to treatment. The purpose of this article is to classify the entry points, angulations, and exit points for 6 tilted maxillary implants in anterior-posterior distribution for prompt decision-making and ease of surgery as well as to capitalize on the available bone width. Methods The retrospective radiographic analysis was performed for 150 maxillary arch edentulous patients restored with fixed prosthesis supported by six tilted implants from April 2021 to April 2023. Post-operative Orthopantomograph (OPG) tracings were performed for maxillary sinus borders to classify the sinus pneumatization, and the entry and exit points of implants were noted with respect to the prosthetic teeth, basal anchoring bone, and implant angulations. Results Of the arches treated, 63 were SP1, 34 were SP2, and 16 were SP3 in the maxilla. Many combination arches were encountered as well, including 32 cases of SP1 + SP2 and 5 of SP2 + SP3 combinations. The common preferred entry points were canine, second premolar, and second molar areas. Conclusions A functional surgical classification aids in placing all-6 tilted bicortical implants for immediate function while being versatile enough to be used in delayed cases as well. The knowledge of this classification helps to visualize the entry/fixation points, minimizing unpredictable and morbid grafting surgical procedures. It also aids in efficiently delivering patient-centric, minimally-invasive, predictably stable, biomechanically sound, and fixed implant treatments without cantilever in complete edentulism.