Radiomorphometric indices of the mandible as indicators of decreased bone mineral density and osteoporosis - meta-analysis and systematic review.

This review aims to evaluate the accuracy of various mandibular radiomorphometric indices in comparison with DEXA BMD measurements in the diagnosis of osteopenia and osteoporosis based on a meta-analysis of the sensitivity and specificity of the indices. PRISMA statement was followed. The materials for analysis were collected in August 2023 by searching three databases: PubMed Central, Web of Science, and Scopus. The selection of studies consisted of three selection stages, and 64 articles were finally obtained. Quality assessment was performed with the QUADAS-2 tool, and the general methodological quality of retrieved studies was low. Statistical analysis was performed based on 2 × 2 tables and estimated sensitivity and specificity were obtained using SROC curves. The most used indices were MCI, MCW and PMI. The best results in detecting reduced BMD obtained for MCW ≤ 3 mm, estimated sensitivity and specificity were 0.712 (95% CI, 0.477-0.870) and 0.804 (95% CI, 0.589-0.921), respectively. The most prone to the risk of bias is the MCI due to the examiner's subjectivism. Radiomorphometric indices of the mandible can be useful as a screening tool to identify patients with low BMD, but should not be used as a diagnostic method. Further research needs to focus on analysing the ability of the indices to detect osteoporosis and also in combination the indices with clinical parameters.

Fractal characteristics of the trabecular pattern of the mandible in patients with renal transplantation.

OBJECTIVE: In this study, we examined the mandibular trabecular bone structures by performing fractal dimension (FD) analysis in patients who underwent renal transplantation (RTx). METHODS: Our study is an observational study with 69 RTx patients and 35 control group patients. The mean FD values of the patient and control groups were calculated and compared. In addition, biochemical parathyroid hormone (PTH), serum calcium, phosphorus, alkaline phosphatase (ALP), and vitamin-D parameters and FD values of both groups were analyzed. RESULTS: FD values were significantly lower in the patient group than in the healthy group (p < .05). In the RTx group compared to the control group, ALP (90.71 ± 34.25-66.54 ± 16.8, respectively) (p < .001) and PTH (75.76 ± 38.01-38.17 ± 12.39, respectively) (p < .001) values were higher. There was a positive correlation between the FD values and ALP (rspearman  = .305, p = .011) and a negative correlation between FD values and vitamin-D (rspearman  = .287, p = .017) of patients with RTx. CONCLUSION: FD values were found to be lower in patients who underwent RTx compared to the control group. It should be considered that FD analysis can be a method that can be used to evaluate trabecular bone structure in patients undergoing RTx.

Radiographic diagnosis of periodontal diseases - Current evidence versus innovations.

Accurate diagnosis of periodontal and peri-implant diseases relies significantly on radiographic examination, especially for assessing alveolar bone levels, bone defect morphology, and bone quality. This narrative review aimed to comprehensively outline the current state-of-the-art in radiographic diagnosis of alveolar bone diseases, covering both two-dimensional (2D) and three-dimensional (3D) modalities. Additionally, this review explores recent technological advances in periodontal imaging diagnosis, focusing on their potential integration into clinical practice. Clinical probing and intraoral radiography, while crucial, encounter limitations in effectively assessing complex periodontal bone defects. Recognizing these challenges, 3D imaging modalities, such as cone beam computed tomography (CBCT), have been explored for a more comprehensive understanding of periodontal structures. The significance of the radiographic assessment approach is evidenced by its ability to offer an objective and standardized means of evaluating hard tissues, reducing variability associated with manual clinical measurements and contributing to a more precise diagnosis of periodontal health. However, clinicians should be aware of challenges related to CBCT imaging assessment, including beam-hardening artifacts generated by the high-density materials present in the field of view, which might affect image quality. Integration of digital technologies, such as artificial intelligence-based tools in intraoral radiography software, the enhances the diagnostic process. The overarching recommendation is a judicious combination of CBCT and digital intraoral radiography for enhanced periodontal bone assessment. Therefore, it is crucial for clinicians to weigh the benefits against the risks associated with higher radiation exposure on a case-by-case basis, prioritizing patient safety and treatment outcomes.

Artificial intelligence and radiomics in the diagnosis of intraosseous lesions of the gnathic bones: A systematic review.

BACKGROUND: The purpose of this systematic review (SR) is to gather evidence on the use of machine learning (ML) models in the diagnosis of intraosseous lesions in gnathic bones and to analyze the reliability, impact, and usefulness of such models. This SR was performed in accordance with the PRISMA 2022 guidelines and was registered in the PROSPERO database (CRD42022379298). METHODS: The acronym PICOS was used to structure the inquiry-focused review question "Is Artificial Intelligence reliable for the diagnosis of intraosseous lesions in gnathic bones?" The literature search was conducted in various electronic databases, including PubMed, Embase, Scopus, Cochrane Library, Web of Science, Lilacs, IEEE Xplore, and Gray Literature (Google Scholar and ProQuest). Risk of bias assessment was performed using PROBAST, and the results were synthesized by considering the task and sampling strategy of the dataset. RESULTS: Twenty-six studies were included (21 146 radiographic images). Ameloblastomas, odontogenic keratocysts, dentigerous cysts, and periapical cysts were the most frequently investigated lesions. According to TRIPOD, most studies were classified as type 2 (randomly divided). The F1 score was presented in only 13 studies, which provided the metrics for 20 trials, with a mean of 0.71 (±0.25). CONCLUSION: There is no conclusive evidence to support the usefulness of ML-based models in the detection, segmentation, and classification of intraosseous lesions in gnathic bones for routine clinical application. The lack of detail about data sampling, the lack of a comprehensive set of metrics for training and validation, and the absence of external testing limit experiments and hinder proper evaluation of model performance.

Evaluation of Bone Mineral Changes in Panoramic Radiographs of Hypothyroid and Hyperthyroid Patients Using Fractal Dimension Analysis.

Objective Hyperthyroidism and hypothyroidism are endocrinopathies that cause a decrease in bone mineral density. The aim of this study is to investigate possible bone changes in the mandible caused by hyperthyroidism and hypothyroidism using fractal analysis (FA) on panoramic radiographs. Material and Methods Panoramic radiographs of a total of 180 patients, including 120 patient groups (60 hyperthyroid, 60 hypothyroid) and 60 healthy control groups, were used. Five regions of interests (ROI) were determined from panoramic radiographs and FA was performed. ROI1: geometric midpoint of mandibular notch and mandibular foramen, ROI2: geometric midpoint of mandibular angle, ROI3: anterior of mental foramen, ROI4: basal cortical area from distal mental foramen to distal root of first molar, ROI5: geometric center of mandibular foramen and mandibular ramus. Results While a significant difference was observed between the patient and control groups regarding ROI1 and ROI2 (p < 0.05); there was no significant difference between the groups in relation to ROI3, ROI4, and ROI5. All FA values were lower in the hyperthyroid group than in the hypothyroid group. Conclusion Fractal analysis proves to be an effective method for early detection of bone mass changes. In the present study, it was concluded that while the mandibular cortical bone was intact, trabecular rich regions were affected by osteoporosis caused by thyroid hormones. Necessary precautions should be taken against the risk of osteoporosis in patients with thyroid hormone disorders.

Is cone-beam computed tomography (CBCT) an alternative to plain radiography in assessments of dental disease? A study of method agreement in a medically compromised patient population.

OBJECTIVES: Poor oral health and dental infections can jeopardize medical treatment and be life-threatening. Due to this, patients with head and neck malignancies, generalized tumor spread, organ transplant, or severe infection are referred for a clinical oral and radiographic examination. The aim of this study was to compare the diagnostic agreement of three radiographic modalities: intraoral radiographs (IO), panoramic radiographs (PX), and cone beam computed tomography (CBCT) for diagnosis of dental diseases. MATERIALS AND METHODS: Three hundred patients were examined with IO, PX, and CBCT. Periapical lesions, marginal bone level, and caries lesions were diagnosed separately by four oral radiologists. All observers also assessed six teeth in 30 randomly selected patients at two different occasions. Kappa values and percent agreement were calculated. RESULTS: The highest Kappa value and percent agreement were for diagnosing periapical lesions (0.76, 97.7%), and for the assessment of marginal bone level, it varied between 0.58 and 0.60 (87.8-89.3%). In CBCT, only 44.4% of all teeth were assessable for caries (Kappa 0.68, 93.4%). The intra-observer agreement, for all modalities and diagnoses, showed Kappa values between 0.5 and 0.93 and inter-observer agreement varied from 0.51 to 0.87. CONCLUSIONS: CBCT was an alternative to IO in diagnosing periapical lesions. Both modalities found the same healthy teeth in 93.8%. All modalities were performed equally regarding marginal bone level. In caries diagnosis, artifacts were the major cause of fallout for CBCT. CLINICAL RELEVANCE: Intraoral radiography is the first-hand choice for diagnosing dental disease. For some rare cases where intraoral imaging is not possible, a dedicated panoramic image and/or CBCT examination is an alternative.

Performance of a commercially available Generative Pre-trained Transformer (GPT) in describing radiolucent lesions in panoramic radiographs and establishing differential diagnoses.

OBJECTIVES: To evaluate the performance of a commercially available Generative Pre-trained Transformer (GPT) in describing and establishing differential diagnoses for radiolucent lesions in panoramic radiographs. MATERIALS AND METHODS: Twenty-eight panoramic radiographs, each containing a single radiolucent lesion, were evaluated in consensus by three examiners and a commercially available ChatGPT-3.5 model. They provided descriptions regarding internal structure (radiodensity, loculation), periphery (margin type, cortication), shape, location (bone, side, region, teeth/structures), and effects on adjacent structures (effect, adjacent structure). Diagnostic impressions related to origin, behavior, and nature were also provided. The GPT program was additionally prompted to provide differential diagnoses. Keywords used by the GPT program were compared to those used by the examiners and scored as 0 (incorrect), 0.5 (partially correct), or 1 (correct). Mean score values and standard deviation were calculated for each description. Performance in establishing differential diagnoses was assessed using Rank-1, -2, and - 3. RESULTS: Descriptions of margination, affected bone, and origin received the highest scores: 0.93, 0.93, and 0.87, respectively. Shape, region, teeth/structures, effect, affected region, and nature received considerably lower scores ranging from 0.22 to 0.50. Rank-1, -2, and - 3 demonstrated accuracy in 25%, 57.14%, and 67.85% of cases, respectively. CONCLUSION: The performance of the GPT program in describing and providing differential diagnoses for radiolucent lesions in panoramic radiographs is variable and at this stage limited in its use for clinical application. CLINICAL RELEVANCE: Understanding the potential role of GPT systems as an auxiliary tool in image interpretation is imperative to validate their clinical applicability.

Risk factors for impacted lower third molar root perforation through the undercut-shaped lingual plate: preoperative insights from panoramic radiography.

This study aimed to identify risk factors associated with perforation of the undercut (U)-shaped lingual plate (LP) by the lower third molar (LM3) root using panoramic radiography (PAN). We retrospectively examined 468 impacted LM3s from 468 individuals, categorizing LM3-LP associations and LP morphology in the coronal section of cone-beam computed tomography as perforation or nonperforation and U-type or non-U-type, respectively. The outcome was the combination of perforation and U-type, and study variables included patient demographics (age and sex) and PAN-associated features (Winter's classification, Pell-Gregory classification, and two major Rood signs). Multivariate logistic regression methods were used for analysis. Perforated and U-type LPs were observed in 205 (43.8%) and 212 (45.3%) cases, respectively. The double-positive outcome was observed in 126 LM3s (26.9%). In the multivariate model, age ≥ 26 years [odds ratio (OR), 2.66; p = 0.002], men (OR, 2.01; p = 0.002), mesioangular (OR, 2.74; p = 0.038) and horizontal impaction (OR, 3.05; p = 0.019), and root darkening (OR, 1.73; p = 0.039) were independently associated with the risk. Class III impaction (OR, 0.35; p = 0.021) and interruption of the white line (OR, 0.55; p = 0.017) were negatively correlated with the risk. In conclusion, this study highlights the importance of identifying the higher probability of U-type LP perforation by the LM3 root in men aged over their midtwenties with Class I/II impaction and mesioangularly or horizontally impacted LM3s, along with root darkening and no interruption of the white line on PAN.

Exploring the potential of artificial intelligence in paediatric dentistry: A systematic review on deep learning algorithms for dental anomaly detection.

BACKGROUND: Artificial intelligence (AI) based on deep learning (DL) algorithms has shown promise in enhancing the speed and accuracy of dental anomaly detection in paediatric dentistry. AIM: This systematic review aimed to investigate the performance of AI systems in identifying dental anomalies in paediatric dentistry and compare it with human performance. DESIGN: A systematic search of Scopus, PubMed and Google Scholar was conducted from 2012 to 2022. Inclusion criteria were based on problem/patient/population, intervention/indicator, comparison and outcome scheme and specific keywords related to AI, DL, paediatric dentistry, dental anomalies, supernumerary and mesiodens. Six of 3918 initial pool articles were included, assessing nine DL sub-systems that used panoramic radiographs or cone-beam computed tomography. Article quality was assessed using QUADAS-2. RESULTS: Artificial intelligence systems based on DL algorithms showed promising potential in enhancing the speed and accuracy of dental anomaly detection, with an average of 85.38% accuracy and 86.61% sensitivity. Human performance, however, outperformed AI systems, achieving 95% accuracy and 99% sensitivity. Limitations included a limited number of articles and data heterogeneity. CONCLUSION: The potential of AI systems employing DL algorithms is highlighted in detecting dental anomalies in paediatric dentistry. Further research is needed to address limitations, explore additional anomalies and establish the broader applicability of AI in paediatric dentistry.

DMAF-Net: deformable multi-scale adaptive fusion network for dental structure detection with panoramic radiographs.

OBJECTIVES: Panoramic radiography is one of the most commonly used diagnostic modalities in dentistry. Automatic recognition of panoramic radiography helps dentists in decision support. In order to improve the accuracy of the detection of dental structural problems in panoramic radiographs, we have improved the You Only Look Once (YOLO) network and verified the feasibility of this new method in aiding the detection of dental problems. METHODS: We propose a Deformable Multi-scale Adaptive Fusion Net (DMAF-Net) to detect 5 types of dental situations (impacted teeth, missing teeth, implants, crown restorations, and root canal-treated teeth) in panoramic radiography by improving the YOLO network. In DMAF-Net, we propose different modules to enhance the feature extraction capability of the network as well as to acquire high-level features at different scales, while using adaptively spatial feature fusion to solve the problem of scale mismatches of different feature layers, which effectively improves the detection performance. In order to evaluate the detection performance of the models, we compare the experimental results of different models in the test set and select the optimal results of the models by calculating the average of different metrics in each category as the evaluation criteria. RESULTS: About 1474 panoramic radiographs were divided into training, validation, and test sets in the ratio of 7:2:1. In the test set, the average precision and recall of DMAF-Net are 92.7% and 87.6%, respectively; the mean Average Precision (mAP0.5 and mAP[0.5:0.95]) are 91.8% and 63.7%, respectively. CONCLUSIONS: The proposed DMAF-Net model improves existing deep learning models and achieves automatic detection of tooth structure problems in panoramic radiographs. This new method has great potential for new computer-aided diagnostic, teaching, and clinical applications in the future.

Identification and classification of pulp calcifications in posterior teeth according to dental condition using digital panoramic radiography and cone beam CT.

OBJECTIVE: To compare digital panoramic radiography (DPR) and cone beam CT (CBCT) in the detection and classification of pulp calcifications in posterior teeth in relation to tooth condition and its location. METHODS: Two hundred and fifty patients with simultaneous DPR and CBCT scans were selected from a dental image bank. For each posterior tooth, its condition was registered (healthy, restored, or decayed). The presence of calcifications was assessed and classified according to location (coronal or radicular) and type (total obliteration, partial obliteration, narrowing, and no calcification). Chi-square and McNemar tests were used to compare DPR and CBCT results, with a significance level of 5%. DPR diagnostic values were calculated using CBCT as reference. RESULTS: Decayed and restored teeth showed a significantly (P < .001) higher prevalence of pulp calcifications than healthy teeth in both imaging exams. There was a significant discrepancy between the imaging modalities in the identification of calcifications (P < .001), especially for radicular calcifications of maxillary teeth (DPR = 55.2%; CBCT = 30.9%). In the case of coronal calcifications, there was a greater discrepancy between CBCT and DPR in the mandibular teeth (10.7%) than in the maxillary teeth (7.7%). Overall, DPR presents higher sensitivity (0.855) than specificity (0.483) for pulp calcifications detection. CONCLUSION: DPR considerably overestimates pulp calcifications in posterior teeth, as compared to CBCT. A higher prevalence of pulp calcifications is related to decayed and restored teeth.

Detection and classification of mandibular fractures in panoramic radiography using artificial intelligence.

PURPOSE: This study aimed to assess the performance of a deep learning algorithm (YOLOv5) in detecting different mandibular fracture types in panoramic images. METHODS: This study utilized a dataset of panoramic radiographic images with mandibular fractures. The dataset was divided into training, validation, and testing sets, with 60%, 20%, and 20% of the images, respectively. An equal number of control panoramic radiographs, which did not contain any fractures, were also randomly distributed among the three sets. The YOLOv5 deep learning model was trained to detect six fracture types in the mandible based on the anatomical location including symphysis, body, angle, ramus, condylar neck, and condylar head. Performance metrics of accuracy, precision, sensitivity (recall), specificity, dice coefficient (F1 score), and area under the curve (AUC) were calculated for each class. RESULTS: A total of 498 panoramic images containing 673 fractures were collected. The accuracy was highest in detecting body (96.21%) and symphysis (95.87%), and was lowest in angle (90.51%) fractures. The highest and lowest precision values were observed in detecting symphysis (95.45%) and condylar head (63.16%) fractures, respectively. The sensitivity was highest in the body (96.67%) fractures and was lowest in the condylar head (80.00%) and condylar neck (81.25%) fractures. The highest specificity was noted in symphysis (98.96%), body (96.08%), and ramus (96.04%) fractures, respectively. The dice coefficient and AUC were highest in detecting body fractures (0.921 and 0.942, respectively), and were lowest in detecting condylar head fractures (0.706 and .812, respectively). CONCLUSION: The trained algorithm achieved promising performance metrics for the automated detection of most fracture types, with the highest performance observed in detecting body and symphysis fractures. Machine learning can provide a potential tool for assisting clinicians in mandibular fracture diagnosis.

Comparison of Quantitative Radiomorphometric Predictors of Healthy and MRONJ-Affected Bone Using Panoramic Radiography and Cone-Beam CT.

OBJECTIVES: To determine the most distinctive quantitative radiomorphometric parameter(s) for the detection of MRONJ-affected bone changes in panoramic radiography (PR) and cone-beam computed tomography (CBCT). METHODS: PR and sagittal CBCT slices of 24 MRONJ patients and 22 healthy controls were used for the measurements of mandibular cortical thickness (MCT), fractal dimension (FD), lacunarity, mean gray value (MGV), bone area fraction (BA/TA), trabecular thickness (Tb.Th), trabecular separation (Tb.Sp), trabecular number (Tb.N). MCT was measured in the mental foramen region. While FD and lacunarity were measured on mandibular trabecular and cortical regions of interest (ROIs), the remaining parameters were measured on trabecular ROIs. The independent samples t-test was used to compare the measurements between MRONJ and control group for both imaging modalities (p = 0.05). RESULTS: MCT was the only parameter that differentiated MRONJ-affected bone in both PR and CBCT (p < 0.05). None of the remaining parameters revealed any difference for MRONJ-affected bone in CBCT (p > 0.05). FD, lacunarity, MGV, BA/TA, and Tb.Sp could distinguish MRONJ-affected trabecular bone in PR (p < 0.05). The correspondent ROI for both imaging methods that was reliable for detecting MRONJ-affected bone was the trabecular bone distal to the mental foramen above the inferior alveolar canal (ROI-3). CONCLUSIONS: MCT is a reliable parameter for the discrimination of MRONJ-affected bone in both PR and CBCT images. PR may be used to detect MRONJ-affected trabecular bone using FD, lacunarity, MGV, BA/TA, and Tb.Sp measurements as well.

Detection of periodontal bone loss patterns and furcation defects from panoramic radiographs using deep learning algorithm: a retrospective study.

BACKGROUND: This retrospective study aimed to develop a deep learning algorithm for the interpretation of panoramic radiographs and to examine the performance of this algorithm in the detection of periodontal bone losses and bone loss patterns. METHODS: A total of 1121 panoramic radiographs were used in this study. Bone losses in the maxilla and mandibula (total alveolar bone loss) (n = 2251), interdental bone losses (n = 25303), and furcation defects (n = 2815) were labeled using the segmentation method. In addition, interdental bone losses were divided into horizontal (n = 21839) and vertical (n = 3464) bone losses according to the defect patterns. A Convolutional Neural Network (CNN)-based artificial intelligence (AI) system was developed using U-Net architecture. The performance of the deep learning algorithm was statistically evaluated by the confusion matrix and ROC curve analysis. RESULTS: The system showed the highest diagnostic performance in the detection of total alveolar bone losses (AUC = 0.951) and the lowest in the detection of vertical bone losses (AUC = 0.733). The sensitivity, precision, F1 score, accuracy, and AUC values were found as 1, 0.995, 0.997, 0.994, 0.951 for total alveolar bone loss; found as 0.947, 0.939, 0.943, 0.892, 0.910 for horizontal bone losses; found as 0.558, 0.846, 0.673, 0.506, 0.733 for vertical bone losses and found as 0.892, 0.933, 0.912, 0.837, 0.868 for furcation defects (respectively). CONCLUSIONS: AI systems offer promising results in determining periodontal bone loss patterns and furcation defects from dental radiographs. This suggests that CNN algorithms can also be used to provide more detailed information such as automatic determination of periodontal disease severity and treatment planning in various dental radiographs.

Prevalence and correlation with sex, age, and dental status of bone apposition at the mandibular angle and radiographic alterations of the temporomandibular joints: a retrospective observational study in an adult Swiss population.

BACKGROUND: The purpose of this study was to determine the prevalence of radiographic changes in the mandibular angle (bone apposition) and osseous alterations in the temporomandibular joints (TMJs) in the adult population of Switzerland. In addition, the study intended to investigate possible correlations between the two sites of contour bone changes (mandibular angle and TMJ) and to analyze various patient-related factors, including sex, age, dental status, and medical history. METHODS: Panoramic radiographs of 600 patients distributed into six age groups (283 females, 317 males, aged 20 to 79 years) were included to evaluate radiographic changes. The bone in the mandibular angle region and the shape of the condylar heads were examined for contour changes (bone apposition at the jaw angles and osseous changes of the TMJs). General estimating equations, binormal tests, and chi-squared tests were used for statistical analysis. RESULTS: Approximately half of the mandibular angles (47.8%) showed bone apposition, mostly bilateral. TMJ alterations were less common (27%), often unilateral, with flattening being the most frequent finding. No significant correlation was found between the two sites. Bone apposition at the mandibular angle showed a significant male predominance, whereas TMJ changes did not differ by sex. Alterations in both sites increased with age, and were not related to dental status or analgesic use. CONCLUSIONS: Bone apposition at the mandibular angle should be interpreted as part of the natural functional adaptation of the bone associated with aging. Assuming that parafunctional habits may influence the development and progression of alterations in the mandibular angle or TMJs, the presence of radiographic changes in these areas should prompt dental clinicians to investigate further in this direction. TRIAL REGISTRATION: The study was approved by the Swiss Association of Research Ethics Committees (swissethics), BASEC reference number: 2020-00963 (25.05.2020).

Changes in mandibular radiomorphometric indices in osteoporosis patients treated with denosumab: a retrospective case-control study.

BACKGROUND: Radiomorphometric indices measured on Dental Panoramic Radiography (DPR) can reflect Bone Mineral Density (BMD). The aim of our study is to evaluate changes in DPR radiographic markers in patients undergoing antiresorptive therapy with denosumab and correlate them to BMD and serum bone turnover markers (BTM). METHODS: We evaluated two radiomorphometric indices: Mandibular Cortical Width (MCW) and Panoramic Mandibular Index (PMI), in patients undergoing antiresorptive therapy with denosumab at T0 (before starting the therapy) and at T1 (after 12 months), comparing results with a control group of healthy patients who performed two DPRs at a one-year time distance. Correlation analysis was performed in the denosumab group, as well as ROC curves were obtained for both indices. RESULTS: The study included 18 patients and 21 controls according to specific inclusion and exclusion criteria, matched by gender and age. Both MCW and PMI were significantly lower at T0 in the denosumab group, consistently with lower BMD. MCW showed significant correlation with femoral and lumbar DEXA and was significantly lower in patients with osteoporosis compared to osteopenia. Only PMI index increased significantly in the denosumab group from T0 to T1. After one year (T1), there weren't any differences between patients and controls for both indices. No significant correlations were found with BTMs. Sensitivity and specificity for MCW and PMI were also calculated. CONCLUSIONS: Our results show how CMW shows sufficient sensitivity and specificity to be used as a radiographic marker to screen and intercept patients with osteoporosis. PMI seems to be able to reflect changes in response to antiresorptive therapy with denosumab. Further studies are needed to confirm our hypothesis.

Investigation of the relationship between the mandibular third molar teeth and the inferior alveolar nerve using posteroanterior radiographs: a pilot study.

BACKGROUND: The most severe complication that can occur after mandibular third molar (MM3) surgery is inferior alveolar nerve (IAN) damage. It is crucial to have a comprehensive radiographic evaluation to reduce the possibility of nerve damage. The objective of this study is to assess the diagnostic accuracy of panoramic radiographs (PR) and posteroanterior (PA) radiographs in identifying the association between impacted MM3 roots and IAN. METHODS: This study included individuals who had PR, PA radiographs, and cone beam computed tomography (CBCT) and who had at least one impacted MM3. A total of 141 impacted MM3s were evaluated on CBCT images, and the findings were considered gold standard. The relationship between impacted MM3 roots and IAN was also evaluated on PR and PA radiographies. The data was analyzed using the McNemar and Chi-squared tests. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy of PR and PA radiographies were determined. RESULTS: Considering CBCT the gold standard, the relationship between MM3 roots and IAN was found to be statistically significant between PR and CBCT (p = 0.00). However, there was no statistically significant relationship between PA radiography and CBCT (0.227). The study revealed that the most prevalent limitation of the PR in assessing the relationship between MM3 roots and IAN was the identification of false-positive relationship. CONCLUSIONS: PA radiography may be a good alternative in developing countries to find out if there is a contact between MM3 roots and IAN because it is easier to get to, cheaper, and uses less radiation.

Diminutive compound odontoma in a child: a case report with emphasis on early detection.

The odontoma is regarded as a hamartomatous process of the jaws. Most are discovered as an incidental radiographic finding, averaging 15 mm in size. This report describes a case of a diminutive odontoma that was surgically removed before the onset of eruptive and pathologic consequences. A compilation of documented complications and syndromes associated with odontomas is also presented.

Optimizing the use of panoramic radiography for determining the position of impacted permanent maxillary canines.

The purpose of this research was to test the reliability of a modified magnification method for determining the position of an impacted canine from a single panoramic radiograph. This retrospective study evaluated 114 panoramic radiographs showing 136 impacted maxillary canines. The widths of the impacted canines, contralateral erupted canines, and ipsilateral maxillary incisors were measured, and ratios for the canine-incisor index (CII) and canine-canine index (CCI) were calculated. The impacted canines were also classified according to their location in the vertical plane (apical, middle, or coronal zone) relative to the contralateral central incisor. Continuous data were analyzed for normal distribution, and logistic and multivariate logistic regression models were conducted. The Benjamini-Hochberg procedure with a false discovery rate of 0.05 was used to account for multiple testing. The intrarater reliability was excellent for impacted canine, central incisor, and contralateral canine measurements (intraclass correlation coefficient > 0.9). The CII and vertical zone were strong predictors of an impacted canine position with clinically useful sensitivity and specificity values (0.69 and 0.74, respectively, based on an area under the curve concordance statistic of 0.75). A predictive range was evident for the CII of palatally (1.10-1.39) and buccally (0.90-1.19) impacted canines in the middle and coronal zones, respectively. The occurrence of palatal or buccal positioning was not significantly associated with the CCI (P = 0.2). The CII and vertical zone identified from a single panoramic radiograph can be used to determine the buccopalatal position of an impacted canine, with more reliability if the impacted canine crown is in the middle or coronal zone of the contralateral central incisor.

A comparison of estimated age based on pulp volume from cone beam computed tomography (CT) images and panoramic radiography data with chronological age.

This retrospective study was conducted to evaluate different methods for dental age estimation in children and to examine the feasibility of using cone beam computed tomography (CBCT) data for age estimation. A total of 200 radiographic records (both digital panoramic radiographs and CBCTs) were acquired from 100 children aged 9 to 16 years, all taken on the same dates. Radiographic data was acquired from archived records and included both panoramic radiography and CBCT data belonging to the same individual. CBCT was used when panoramic radiographic data was insufficient. The pulp volume and pulp/tooth volume ratio of the left first molar teeth in the mandible were calculated from the CBCT data using MIMICS software. In addition, age was estimated by the Demirjian and Willems methods from data obtained from panoramic radiography images. Statistical analyses and linear regression analysis were performed as necessary. There was a statistically significant difference between the mean difference between the Demirjian method and chronological age, and between the Willems method and chronological age (p < 0.001). Statistically significance was achieved in a linear regression model created from pulp volume (R2 = 0.098) and pulp/tooth volume ratio (R2 = 0.395) data for the estimated dental age analysis (p < 0.001) and a negative correlation was observed with chronological age. When compared estimated dental age from CBCT data with chronological age, the pulp/tooth volume ratio method yielded results closer to chronological age than using only pulp volume data. When considering both panoramic radiographic age estimation methods and age estimation methods using CBCT data, we found that the results obtained with the Willems method, a panoramic radiographic age estimation technique, provided the closest results to the chronological age. More contributions should be made to the literature regarding the feasibility of age estimation using pulp and tooth volume as an alternative method.