Prevalence of impacted and transmigrated canines in orthodontic patients - A radiographic study.

Background: The canine plays a vital role in dentofacial aesthetics and function. It supports the base of the alar and upper lip, which are crucial for smile aesthetics. When impacted, these functions are lost, leading to low self-esteem and overall poor health-related quality of life. The present study was conducted to find the prevalence of impacted and transmigrated canines in orthodontic patients and also to find the most prevalent type of canine impaction. Materials and Methods: This retrospective study was conducted in a hospital setting at Dental College. A total of 3050 OPGs (Orthopantomagram) of patients who visited dental hospitals for orthodontic treatment constituted the final sample. Demographic details regarding age, gender, and place of residence were collected from the patients. Evaluation of sample radiographs on the standard light box was performed to collect data regarding impacted and transmigrated canines. Statistical analysis was performed using descriptive statistics and Chi-square test. Results: Prevalence of impacted canine was found to be 2.46%. Impacted canine prevalence of 1.53% and 2.85% was reported in males and females, respectively. Only two female patients had transmigrated mandibular impacted canines. Comparison of arch showed a statistically significant (P value 0.02) higher prevalence in the maxillary arch, which was 1.54%, and in the mandibular arch, it was 0.92%. The present study reported significantly more unilateral impactions (P value 0.00) than bilateral impactions. Conclusion: The overall prevalence for impacted canine was 2.46%. Prevalence was higher in female patients. Early diagnosis of impacted canines is vital for planning orthodontic treatment in such patients.

Deep learning with convolution neural network detecting mesiodens on panoramic radiographs: comparing four models.

The aim of this study was to develop an optimal, simple, and lightweight deep learning convolutional neural network (CNN) model to detect the presence of mesiodens on panoramic radiographs. A total of 628 panoramic radiographs with and without mesiodens were used as training, validation, and test data. The training, validation, and test dataset were consisted of 218, 51, and 40 images with mesiodens and 203, 55, and 61 without mesiodens, respectively. Unclear panoramic radiographs for which the diagnosis could not be accurately determined and other modalities were required for the final diagnosis were retrospectively identified and employed as the training dataset. Four CNN models provided within software supporting the creation of neural network models for deep learning were modified and developed. The diagnostic performance of the CNNs was evaluated according to accuracy, precision, recall and F1 scores, receiver operating characteristics (ROC) curves, and area under the ROC curve (AUC). In addition, we used SHapley Additive exPlanations (SHAP) to attempt to visualize the image features that were important in the classifications of the model that exhibited the best diagnostic performance. A binary_connect_mnist_LeNet model exhibited the best performance of the four deep learning models. Our results suggest that a simple lightweight model is able to detect mesiodens. It is worth referring to AI-based diagnosis before an additional radiological examination when diagnosis of mesiodens cannot be made on unclear images. However, further revaluation by the specialist would be also necessary for careful consideration because children are more radiosensitive than adults.

Evaluating the Diagnostic Accuracy of an AI-Driven Platform for Assessing Endodontic Treatment Outcomes Using Panoramic Radiographs: A Preliminary Study.

Background/Objectives: The purpose of this preliminary study was to evaluate the diagnostic performance of an AI-driven platform, Diagnocat (Diagnocat Ltd., San Francisco, CA, USA), for assessing endodontic treatment outcomes using panoramic radiographs (PANs). Materials and Methods: The study included 55 PAN images of 55 patients (15 males and 40 females, aged 12-70) who underwent imaging at a private dental center. All images were acquired using a Hyperion X9 PRO digital cephalometer and were evaluated using Diagnocat, a cloud-based AI platform. The AI system assessed the following endodontic treatment features: filling probability, obturation adequacy, density, overfilling, voids in filling, and short filling. Two human observers independently evaluated the images, and their consensus served as the reference standard. The diagnostic accuracy metrics were calculated. Results: The AI system demonstrated high accuracy (90.72%) and a strong F1 score (95.12%) in detecting the probability of endodontic filling. However, the system showed variable performance in other categories, with lower accuracy metrics and unacceptable F1 scores for short filling and voids in filling assessments (8.33% and 14.29%, respectively). The accuracy for detecting adequate obturation and density was 55.81% and 62.79%, respectively. Conclusions: The AI-based system showed very high accuracy in identifying endodontically treated teeth but exhibited variable diagnostic accuracy for other qualitative features of endodontic treatment.

Non-syndromic tooth agenesis in Latvian adolescent dental patients: a retrospective study with relevant literature review.

AIM: To investigate the prevalence of tooth agenesis and associated dental anomalies in Latvian adolescent dental patients and compare it to other European countries. DESIGN: Cross-sectional study of 2692 11-to-14-year-old patients (39.9% males and 60.1% females) attending Riga Stradins University Institute of Stomatology with panoramic radiographs taken between August 2020 and September 2021. Patients with any genetic syndromes were excluded. Data on tooth agenesis (excluding third molars) and other dental anomalies were recorded. RESULTS: The prevalence of tooth agenesis in Latvian adolescent dental patients was 9.3% with no statistically significant difference between genders (χ2 test, p = 0.472). The most commonly missing teeth were mandibular second premolars, followed by upper lateral incisors and upper second premolars. There was a statistically significant association with the presence of other dental anomalies in tooth agenesis patients (p < 0.001). CONCLUSIONS: This study found that the prevalence of non-syndromic tooth agenesis in Latvian adolescent dental patients was 9.3% with no statistically significant differences between the genders. Patients with tooth agenesis have a statistically significant possibility of the presence of other dental anomalies (p < 0.001).

CACSNet for automatic robust classification and segmentation of carotid artery calcification on panoramic radiographs using a cascaded deep learning network.

Stroke is one of the major causes of death worldwide, and is closely associated with atherosclerosis of the carotid artery. Panoramic radiographs (PRs) are routinely used in dental practice, and can be used to visualize carotid artery calcification (CAC). The purpose of this study was to automatically and robustly classify and segment CACs with large variations in size, shape, and location, and those overlapping with anatomical structures based on deep learning analysis of PRs. We developed a cascaded deep learning network (CACSNet) consisting of classification and segmentation networks for CACs on PRs. This network was trained on ground truth data accurately determined with reference to CT images using the Tversky loss function with optimized weights by balancing between precision and recall. CACSNet with EfficientNet-B4 achieved an AUC of 0.996, accuracy of 0.985, sensitivity of 0.980, and specificity of 0.988 in classification for normal or abnormal PRs. Segmentation performances for CAC lesions were 0.595 for the Jaccard index, 0.722 for the Dice similarity coefficient, 0.749 for precision, and 0.756 for recall. Our network demonstrated superior classification performance to previous methods based on PRs, and had comparable segmentation performance to studies based on other imaging modalities. Therefore, CACSNet can be used for robust classification and segmentation of CAC lesions that are morphologically variable and overlap with surrounding structures over the entire posterior inferior region of the mandibular angle on PRs.

Degenerative Temporomandibular Disorders: An Assessment of Bone Trabecular Structure Using Fractal Analysis in Digital Panoramic Radiographs.

Introduction Fractal analysis has proved to be a salient tool to quantitatively assess the qualitative changes in the bone trabeculae of patients with hyperparathyroidism, osteoporosis, and various temporomandibular disorders, including osteoarthritis (OA) and rheumatoid arthritis of temporomandibular joint (TMJ), in several previous studies. The purpose of this study was to use fractal analysis to assess alterations in the trabecular pattern of the mandibular condyle in patients with degenerative temporomandibular disorders. Materials and methods This study comprised 98 subjects with 49 subjects in the study group and 49 subjects in the control group, aged 18-50 years. Age and sex in the control group were matched to those in the case group. The subjects were assessed clinically with the Diagnostic Criteria for Temporomandibular Disorders. Digital panoramic X-ray equipment with set parameters of 70 kvp, 8 mA, and 16-second exposure duration was used to take panoramic radiographs. Fractal analysis was done and the calculated fractal dimension value was obtained using ImageJ version 1.48 software (National Institutes of Health, Bethesda, MD). The same observer used Muir and Goss's method to rate the total degenerative changes in the condylar surfaces, which were substantiated by the calculated fractal dimension value. The data were statistically analyzed. Results The results revealed a significant difference (p-value = 0.041) between the mean fractal value in the case group's (1.35) and the control group's (1.38) left sides whereas the differences in the mean fractal values between the case and control groups on the right side was not significant (p-value = 0.49). Conclusion It is recommended to use the fractal dimension value and the total degenerative severity score together to quantify degenerative changes in the TMJ OA rather than exclusively relying on fractal value.

Studying eruption patterns of mandibular third molars for forensic age assessment: Introducing current reference data from a population of black South Africans.

INTRODUCTION: Evaluation of the eruption of mandibular third molars in orthopantomograms (OPGs) is a method of forensic age assessment. The objective of our study was to provide valid reference data for this trait within a population of black South Africans. The study was guided by the criteria for reference studies in age assessment. MATERIALS AND METHODS: A study population from Pretoria, South Africa comprising 670 OPGs obtained from 338 black females and 332 black males aged between 15.00 and 25.97 years was analysed. All OPGs were performed for medical indication during the period from 2011 to 2022 and were retrospectively evaluated. From the 670 OPGs, a total of 1021 mandibular third molars were evaluated. The assessment of mandibular third molars was conducted using the staging scale presented by Olze et al. in 2012. Two experienced dentists evaluated the OPGs independently of each other. If the two examiners diverged in their assessments, a consensus stage was assigned. RESULTS: As expected, the mean, median and minimal age increased with higher stages for both teeth and both sexes. The minimum age recorded for stage D, indicating complete tooth eruption, was 15.79 years in females and 16.62 years in males. CONCLUSION: As it is the case for previous reference studies in other countries, placing exclusive reliance on the evaluation of mandibular third molar eruption is inadequate for ascertaining the age of majority among Black South Africans. Future studies need to examine if our results are transferable to other countries in Sub-Saharan Africa.

Comparison of the accuracy of bracket axial positioning with and without radiographic support and according to practitioner experience: A three-dimensional study.

INTRODUCTION: Accurate bracket positioning remains challenging. To avoid angulation errors, some recommend examining the panoramic radiograph during bonding. However, it can cause distortions. Cone-beam computed tomography (CBCT) provides a more precise panoramic reconstruction but with higher radiation doses. The main objective of this study is to compare the accuracy of axial positioning between direct bonding without radiography, with conventional panoramic radiograph, and with panoramic reconstruction from CBCT. The secondary objectives are to evaluate positioning accuracy of each tooth and to assess the influence of practitioner level of experience. METHODS: Thirty practitioners, divided into two groups based on their experience performed direct bonding on a model thrice: without radiography, then with the conventional panoramic radiograph, then with the panoramic reconstruction from CBCT. Models were scanned, and angulation errors were measured using OrthoAnalyzer. Values were compared using the Friedman's test followed by the Bonferroni correction for multiple comparisons (P-value = 0.05). RESULTS: For the low level of experience group, angulation errors were significantly greater than the accepted limit without radiographic reference, and significantly lower with CBCT reconstruction. For the high level of experience group, angulation errors were significantly lower than the accepted limit for the three bonding methods. For every tooth, using the panoramic reconstruction from CBCT as a reference, was the most accurate method, regardless of the level of experience. More experienced practitioners made fewer errors for the three methods. CONCLUSIONS: Panoramic reconstruction from CBCT is the most accurate method to limit angulation errors during direct bonding. Conventional panoramic radiography remains a reliable tool if used with caution. Bonding without any radiographic reference should be avoided especially for less experienced practitioners.

Diffuse idiopathic skeletal hyperostosis (DISH): Detection on dental radiologic examinations in two older adults and overview of the literature.

BACKGROUND: Diffuse idiopathic skeletal hyperostosis (DISH) is a potentially serious osteopathic disorder associated with coalescing ossifications of the anterior vertebrae and may be concomitant with a constellation of symptomatology and systemic comorbidities. There is limited dental literature describing this finding on panoramic radiographs and cone beam computed tomography (CBCT) scans. CASE PRESENTATIONS: Two case reports of DISH are provided. One patient manifested extensive ossifications along the cervical vertebrae and consequent episodes of dysphagia and hoarseness. The other affected patient's cervical ossification was found at an earlier stage of formation and without symptomatology. Panoramic radiography, cervical spine radiography, and CBCT examinations have been provided. CONCLUSION: Attending dental healthcare practitioners should carefully evaluate all areas of interest and surrounding fields of view when taking panoramic radiographs and CBCT scans for manifestations of DISH and other potential disorders of the cervical vertebrae. A suspected radiologic finding of DISH should prompt timely referral for comprehensive medical assessment to mitigate neurologic deficits and other comorbidities.

Periapical Lesions in Panoramic Radiography and CBCT Imaging-Assessment of AI's Diagnostic Accuracy.

Background/Objectives: Periapical lesions (PLs) are frequently detected in dental radiology. Accurate diagnosis of these lesions is essential for proper treatment planning. Imaging techniques such as orthopantomogram (OPG) and cone-beam CT (CBCT) imaging are used to identify PLs. The aim of this study was to assess the diagnostic accuracy of artificial intelligence (AI) software Diagnocat for PL detection in OPG and CBCT images. Methods: The study included 49 patients, totaling 1223 teeth. Both OPG and CBCT images were analyzed by AI software and by three experienced clinicians. All the images were obtained in one patient cohort, and findings were compared to the consensus of human readers using CBCT. The AI's diagnostic accuracy was compared to a reference method, calculating sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and F1 score. Results: The AI's sensitivity for OPG images was 33.33% with an F1 score of 32.73%. For CBCT images, the AI's sensitivity was 77.78% with an F1 score of 84.00%. The AI's specificity was over 98% for both OPG and CBCT images. Conclusions: The AI demonstrated high sensitivity and high specificity in detecting PLs in CBCT images but lower sensitivity in OPG images.

Accuracy in the Diagnostics of Styloid Process - Panoramic Radiograph vs. Computed Tomography: A Comparative Study.

BACKGROUND/AIM: The styloid process (SP) becomes clinically relevant when it shows enlargement (>30 mm) in the sense of an elongated SP (ESP) and/or increasing calcification leading to Eagle Syndrome (ES). Panoramic radiograph (PR) or computed tomography (CT) are part of the routine diagnostics in ES. Currently, CT is considered the gold standard. The aim of this study was to investigate the accuracy in the diagnostics/measurements of SP/ESP throughout a comparative study between PR and CT. Furthermore, in addition to measuring established parameters, this study aimed to determine the currently unexamined width in the base and tip of the SP. PATIENTS AND METHODS: The present study examined the radiological findings of bilateral SP in 100 patients who received both PR and CT on the same day. Measurements of the length of the SP and width at the basis and tip were performed. Furthermore, calcification patterns, Langlais classification and the prevalence of ESP were analyzed. RESULTS: There was a highly significant correlation between PR and CT measuring SP for every parameter. Males showed significantly longer SP than females among the age group between 18-75 years. The results of the length measurements of the SP (male: right SP=32.98 mm; left SP=35.21 mm; female: right SP=30.31 mm; left SP=30.92 mm) significantly exceeded the values of comparable studies. CONCLUSION: Consequently, it can be concluded that PR provides accurate measurements when compared to CT for measuring and diagnosing SP/ESP/Eagle syndrome. This study was one of the first to examine the width of the SP in the base and tip, thus these measurements can serve as a baseline for further studies. Since the mean lengths of SP exceeded 30.0 mm in the present study, these findings raise the question of whether the cut-off of 30.0 mm is adequate for the diagnosis of ESP.

Radiographic Diagnostic Choices of Palestinian Dentists for Implant Therapy.

Aim: To report the radiographic preferences during dental implant therapy in Palestine. Materials and Methods: Fourteen multiple-choice questions were delivered in electronic and hardcopy formats questionnaires during the Sixth International Implantology Conference (Palestine). The questions investigated the radiographic techniques that are mostly used based on various clinical scenarios and treatment phases. Results: One hundred and thirty-seven responses were captured. The majority of the participants were general dentists with implant experience (79.6%). Less than a third of the participants (27.2%) were members of the Palestinian Association of Dental Implantology. The majority (85.9%) of them have their practice in a city zone. Panoramic radiograph (PAN) combined with cone beam computed tomography (CBCT) was the most preferred radiographic technique during the planning stage. Conclusion: PAN and CBCT was the preferred choice during the planning stages. A PAN was preferred postoperatively and if no complications were associated. In the case of symptomatic patients, CBCT was the radiograph of choice.

Dental age estimation: A comparative study of convolutional neural network and Demirjian's method.

The aim of this study is to compare a technique using Convolutional Neural Network (CNN) with the Demirjian's method for chronological age estimation of living individuals based on tooth age from panoramic radiographs. This research used 5898 panoramic X-ray images collected for diagnostic from pediatric patients aged 4-17 who sought treatment at Antalya Oral and Dental Health Hospital between 2015 and 2020. The Demirjian's method's grading was executed by researchers who possessed appropriate training and experience. In the CNN method, various CNN architectures including Alexnet, VGG16, ResNet152, DenseNet201, InceptionV3, Xception, NASNetLarge, InceptionResNetV2, and MobieNetV2 have been evaluated. Densenet201 exhibited the lowest MAE value of 0.73 years, emphasizing its superior accuracy in age estimation compared to other architectures. In most age categories, the predicted age closely matches the actual age. The most inconsistent results are observed at ages 12 and 13. The results highlight correspondence between the age predicted by CNN and the Demirjian's approach. In conclusion, the results show that the CNN method is adequate to be an alternative to the Demirjian's age estimation method. We suggest that convolutional neural network can effectively optimize the accuracy of age estimation and can be faster than traditional methods, eliminating the need for additional learning from experts.

Combining public datasets for automated tooth assessment in panoramic radiographs.

OBJECTIVE: Panoramic radiographs (PRs) provide a comprehensive view of the oral and maxillofacial region and are used routinely to assess dental and osseous pathologies. Artificial intelligence (AI) can be used to improve the diagnostic accuracy of PRs compared to bitewings and periapical radiographs. This study aimed to evaluate the advantages and challenges of using publicly available datasets in dental AI research, focusing on solving the novel task of predicting tooth segmentations, FDI numbers, and tooth diagnoses, simultaneously. MATERIALS AND METHODS: Datasets from the OdontoAI platform (tooth instance segmentations) and the DENTEX challenge (tooth bounding boxes with associated diagnoses) were combined to develop a two-stage AI model. The first stage implemented tooth instance segmentation with FDI numbering and extracted regions of interest around each tooth segmentation, whereafter the second stage implemented multi-label classification to detect dental caries, impacted teeth, and periapical lesions in PRs. The performance of the automated tooth segmentation algorithm was evaluated using a free-response receiver-operating-characteristics (FROC) curve and mean average precision (mAP) metrics. The diagnostic accuracy of detection and classification of dental pathology was evaluated with ROC curves and F1 and AUC metrics. RESULTS: The two-stage AI model achieved high accuracy in tooth segmentations with a FROC score of 0.988 and a mAP of 0.848. High accuracy was also achieved in the diagnostic classification of impacted teeth (F1 = 0.901, AUC = 0.996), whereas moderate accuracy was achieved in the diagnostic classification of deep caries (F1 = 0.683, AUC = 0.960), early caries (F1 = 0.662, AUC = 0.881), and periapical lesions (F1 = 0.603, AUC = 0.974). The model's performance correlated positively with the quality of annotations in the used public datasets. Selected samples from the DENTEX dataset revealed cases of missing (false-negative) and incorrect (false-positive) diagnoses, which negatively influenced the performance of the AI model. CONCLUSIONS: The use and pooling of public datasets in dental AI research can significantly accelerate the development of new AI models and enable fast exploration of novel tasks. However, standardized quality assurance is essential before using the datasets to ensure reliable outcomes and limit potential biases.

Evaluation of Styloid Process Elongation in Madinah, Saudi Arabia: A Retrospective Radiographic Investigation.

OBJECTIVES: This study aimed to identify the prevalence of an elongated styloid process and analyze the presence of its calcification in the Saudi population using panoramic radiographs. METHODS: The Taibah Outpatient Dental Clinic's OPG radiographs for 962 patients who attended screening clinics between December 2022 and October 2023 were all included in the study. Patients' demographics, such as age, gender, and nationality, as well as radiological data, were included in the following study variables: the presence of an elongated styloid on both sides of a panoramic radiograph, right side styloid length, left side styloid length, right side distal end thickness, and left side distal end thickness. RESULTS: The study evaluated 438 (45.5%) processes found in individuals aged 16-80 years old. The elongated process length ranged from 30.0 to 40.1 mm, and the diameter ranged from 0.81 to 7.79 mm at the origin to 0.56-3.79 mm at the end. There was no statistically significant difference in process length across genders or age groups. The diameters of the styloid bones on the left side vary significantly across genders at the start and completion of the process. CONCLUSION: The prevalence of elongated styloids in the studied population was 4.26%. The radiological evaluation of the styloid process is a crucial stage in dental surgery planning.

Prevalence and Distribution of Dental Anomalies in Schoolchildren in Kuwait.

OBJECTIVE: Dental anomalies (DA) can affect paediatric patients' aesthetics, function, and psychological well-being. There is a lack of data about the prevalence of DA in children in Kuwait. This study aimed to investigate the prevalence and distribution of DA amongst schoolchildren aged 8 to 12 years. METHODS: A retrospective study was conducted using panoramic digital radiographs of children who attended a single dental center. All radiographs were evaluated by 2 calibrated and trained examiners. RESULTS: DA were present in 110 (20.1%) out of the 546 panoramic radiographs examined: 53.6% in females and 46.4% in males. The mean age of children with DA (9.83 ± 1.29) was similar to that of children with no anomalies (9.96 ± 1.46). The most prevalent anomaly was dental agenesis (9.3%), followed by taurodontism (6.6%) and ectopic eruption (EE, 2%). DA were more common in the maxilla (58.2%) compared to the mandible (41.8%, P = .042). Congenitally missing teeth were significantly more frequent in the mandible (56.9%) than in the maxilla (43.1%, P = .003). EE was significantly more common in the maxilla (90.9%) than in the mandible (9.1%, P = .024). Microdontia and root dilacerations were only present in males, whilst supernumerary teeth, transposition, and impacted teeth were noted in females only. CONCLUSIONS: The prevalence of DA amongst schoolchildren in Kuwait was considered to be relatively high. Certain DA were associated with gender. The significant prevalence of DA highlights the need for early diagnosis using panoramic radiographs, particularly during the ages of 9 and 10, in order to ensure effective patient management.

Panoramic imaging errors in machine learning model development: a systematic review.

OBJECTIVES: To investigate the management of imaging errors from panoramic radiography (PAN) datasets used in the development of machine learning (ML) models. METHODS: This systematic literature followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and used three databases. Keywords were selected from relevant literature. ELIGIBILITY CRITERIA: PAN studies that used ML models and mentioned image quality concerns. RESULTS: Out of 400 articles, 41 papers satisfied the inclusion criteria. All the studies used ML models, with 35 papers using deep learning (DL) models. PAN quality assessment was approached in 3 ways: acknowledgement and acceptance of imaging errors in the ML model, removal of low-quality radiographs from the dataset before building the model, and application of image enhancement methods prior to model development. The criteria for determining PAN image quality varied widely across studies and were prone to bias. CONCLUSIONS: This study revealed significant inconsistencies in the management of PAN imaging errors in ML research. However, most studies agree that such errors are detrimental when building ML models. More research is needed to understand the impact of low-quality inputs on model performance. Prospective studies may streamline image quality assessment by leveraging DL models, which excel at pattern recognition tasks.

Characteristics of CBCT versus panoramic signs of the surrounding bone of molar root protrusions into the maxillary sinus.

The study aimed to (a) investigate the amount and characteristics of the surrounding bone of protruded molar roots into the maxillary sinus using cone-beam computed tomography (CBCT) and (b) assess the correlation between the amount of bone with panoramic high-risk signs. Radiographs of 408 roots protruding beyond the sinus floor were evaluated. Axial CBCT images were used to investigate then classify eight characteristics of surrounding bone: no bone; bone < half the root girth in the proximal or buccal-palatal aspect; bone covering half the root girth in the proximal or buccal-palatal aspect; bone > half the root girth in the proximal or buccal-palatal aspect; and, complete bone. These were then grouped into four degrees of bone support: no bone; bone ≤ half the root girth; bone > half the root girth; and, complete bone. Panoramic signs were subclassified as: projection of root; interruption of the sinus floor; darkening of the root; upward curving of the sinus floor; absence of periodontal ligament space; and, absence of the lamina dura. Correlation between the degree of bone and the panoramic signs was evaluated using the Chi-square or Fisher's exact tests. Positive and negative predictive values, sensitivity, specificity, accuracy, and receiver operating characteristic analysis were calculated. Complete bone support was the most common. 'Projection of root' had a high negative predictive value and sensitivity. 'Absence of the periodontal ligament space and lamina dura' had a high positive predictive value, specificity, accuracy, and area under the curve. These two signs were significantly correlated with the degree of bone support.

Is there an association between molar incisor hypomineralization and developmental dental anomalies? A case-control study.

BACKGROUND: The aim of this study was to determine whether there is any association between molar incisor hypomineralization and developmental dental anomalies. METHODS: Two pediatric dentists evaluated panoramic radiographs of 429 children aged 8-14 years with molar incisor hypomineralization (study group) and 437 children without molar incisor hypomineralization (control group) in terms of developmental dental anomalies. Twelve different developmental dental anomalies were categorized into four types: size (microdontia, macrodontia); position (ectopic eruption of maxillary permanent first molars, infraocclusion of primary molars); shape (fusion, gemination, dilaceration, taurodontism, peg-shaped maxillary lateral incisors); and number (hypodontia, oligodontia, hyperdontia) anomalies. RESULTS: No significant difference was observed in the frequencies of developmental dental anomalies between the study and control groups in total, females, and males (p > 0.05). A statistically significant difference was found between the distribution of developmental size, position, shape, and number anomalies between the study and control groups (p = 0.024). The most common anomaly in both groups was hypodontia (6.3% and 5.9%, respectively). There was a significant difference between the study and control groups in terms of subtypes of shape anomaly in all children and females (p = 0.045 and p = 0.05, respectively). CONCLUSIONS: While a significant difference was observed between the distributions of types of developmental dental anomalies between individuals with and without molar incisor hypomineralization, there was no difference in terms of the frequency of developmental dental anomalies.