A Cone-Beam Computed Tomography-Based Evaluation of Root Dilaceration in Permanent Premolars: A Retrospective Study.

Statement of the Problem: As a developmental disorder characterized by an abnormal bend and angle in the longitudinal axis of the tooth root, dilaceration can cause complications in routine dental procedures such as endodontics, orthodontics, and surgical treatments. Purpose: The purpose of this study was to investigate the prevalence of dilaceration in maxillary and mandibular premolar teeth in a population of Shiraz city based on cone-beam computed tomography (CBCT). Materials and Method: This is a retrospective cross-sectional study on 927 premolar teeth and 132 CBCT radiographs of patients obtained from four private radiology clinics in Shiraz (Iran). In this study, the presence, location, direction, and severity of dilaceration in premolar roots as well as its relationship with gender were investigated. Chi-square and Fisher tests were used to analyze the data. Results: The results showed that 17% of the studied 927 teeth had dilaceration. The prevalence of dilaceration was significantly higher in women than in men (20.3% vs. 13.6%, p= 0.005). The dilaceration rates were significantly higher in the mandibular first and second premolar teeth (31.6% and 26%, p= 0.002) than in the other teeth. In addition, the highest prevalence was in the distal direction with mild severity in the apical third of the root (p< 0.001). Conclusion: According to the results of this study, the prevalence of dilaceration was relatively high in mandibular premolar teeth especially in women.

Mandibular bone mass density in a medieval population and its relationship with stable isotopes δ13C and δ15N.

The aim of this study was to compare the level of bone mass in digital orthopantomograms in two populations (medieval and current) using two radiomorphometric indexes, and to correlate the mandibular bone mass value, in the medieval mandible population, with stable isotope data δ13C and δ15N. An observational, cross-sectional, and analytical study on mandibles from two diachronic groups, 15 mandibles from the medieval settlement of La Torrecilla (Granada, Spain) and 15 mandibles from current patients at the Faculty of Dentistry of the University of Granada (Spain), matched by age and sex was conducted. The bone mass density was determined using the Mandibular Cortical Width Index (MCW) and the Mandibular Panoramic Index (PMI) in digital panoramic radiographs. In the medieval group, the values of bone mass density were correlated with those of two stable isotopes (δ13C and δ15N). The mean value of MCW in mm in the medieval group was 3.96 ± 0.60 (mean ± standard deviation) and in the current group was 4.02 ± 1.01. The PMI was 0.33 ± 0.06 and 0.35 ± 0.08 in the medieval and current groups respectively, with similar results in both groups (p = 0.820 and p = 0.575). A negative correlation was found between both morphometric indices and the δ15N isotope (rs = 0.56, p = 0.030 and rs = 0.61, p = 0.016, respectively). The bone mass density in mandibles belonging to the two compared populations, determined by two quantitative radiomorphometric indices, is similar. Within the medieval population, there is an inverse correlation between the δ15N value and bone mass density.

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.

Elongated Styloid Process Evaluation Using Panoramic Radiography in Patients With Oral Submucous Fibrosis: A Retrospective Study.

Background Oral submucous fibrosis (OSMF) is a chronic condition primarily affecting the buccal mucosa, characterized by fibrotic changes, scarring, and precancerous lesions. Pathologically, it involves inflammation, collagen deposition, and muscular degeneration. Objective This study aimed to assess the prevalence and distribution of elongated styloid process (ESP) in patients diagnosed with OSMF, contributing to the understanding of anatomical variations in this population. Methods A retrospective study was conducted at the Department of Oral Medicine and Radiology of Saveetha Dental College. We collected records of OSMF patients from 2021 to 2023, which included panoramic radiographs. We evaluated the presence, type, and grading of ESP alongside OSMF grades using standardized classifications. Results Out of 125 OSMF patients, 67 (53.6%) had ESP. Type I ESP was most prevalent (83.58%). Bilateral occurrences were more common (52.2%) than unilateral (41.79%). On both sides, type I ESP predominated significantly. Among unilateral cases, left-side occurrences were slightly more frequent. Type I ESP remained predominant regardless of laterality. Conclusion The study highlights the high prevalence of ESP, predominantly type I, in OSMF patients, with bilateral occurrences more common. These findings provide valuable insights into the anatomical variations associated with OSMF, contributing to clinical understanding and potential future research directions.

EVALUATION OF MANDIBULAR TRABECULAR AND CORTICAL STRUCTURE BY FRACTAL ANALYSIS IN HYPERLIPIDEMIA AND HYPERTENSION PATIENTS.

INTRODUCTION: This study aimed to evaluate the mandibular trabecular and cortical changes in patients with hyperlipidemia (HL) and/or hypertension (HT) using fractal dimension (FD) analysis, mandibular cortical width (MCW), panoramic mandibular index (PMI) and mandibular cortical index (MCI). MATERIALS AND METHODS: Panoramic radiographs of 100 patients were evaluated. FD measurement of three region of interest (ROI) including the angulus, corpus and interdental bone area were made. MCW, PMI and MCI were also measured and noted. RESULTS: Angulus, corpus and interdental FD values were significantly lower in three disease groups than the control group. Angulus, corpus, and interdental FD values were significantly lower in the HL+HT group than in the HL group and HT group. MCW value was significantly lower in the HL group, HT group, and HL+HT group than the control group. The cortical index C1 was more common in the control group while C2 was more common in the HT, HL and HL+HT group. CONCLUSION: The fact that FD was significantly lower in the HL+HT group compared to the HL and HT groups indicates the positive effect of their association on bone loss and quality. FD measurements on images obtained using a direct digital panoramic system can be used for treatment planning and follow-up of patients with HL and/or HT.

A Deep Learning Approach to Automatic Tooth Caries Segmentation in Panoramic Radiographs of Children in Primary Dentition, Mixed Dentition, and Permanent Dentition.

OBJECTIVES: The purpose of this study was to evaluate the effectiveness of dental caries segmentation on the panoramic radiographs taken from children in primary dentition, mixed dentition, and permanent dentition with Artificial Intelligence (AI) models developed using the deep learning method. METHODS: This study used 6075 panoramic radiographs taken from children aged between 4 and 14 to develop the AI model. The radiographs included in the study were divided into three groups: primary dentition (n: 1857), mixed dentition (n: 1406), and permanent dentition (n: 2812). The U-Net model implemented with PyTorch library was used for the segmentation of caries lesions. A confusion matrix was used to evaluate model performance. RESULTS: In the primary dentition group, the sensitivity, precision, and F1 scores calculated using the confusion matrix were found to be 0.8525, 0.9128, and 0.8816, respectively. In the mixed dentition group, the sensitivity, precision, and F1 scores calculated using the confusion matrix were found to be 0.7377, 0.9192, and 0.8185, respectively. In the permanent dentition group, the sensitivity, precision, and F1 scores calculated using the confusion matrix were found to be 0.8271, 0.9125, and 0.8677, respectively. In the total group including primary, mixed, and permanent dentition, the sensitivity, precision, and F1 scores calculated using the confusion matrix were 0.8269, 0.9123, and 0.8675, respectively. CONCLUSIONS: Deep learning-based AI models are promising tools for the detection and diagnosis of caries in panoramic radiographs taken from children with different dentition.

Image segmentation of impacted mesiodens using deep learning.

This study aimed to evaluate the performance of deep learning algorithms for the classification and segmentation of impacted mesiodens in pediatric panoramic radiographs. A total of 850 panoramic radiographs of pediatric patients (aged 3-9 years) was included in this study. The U-Net semantic segmentation algorithm was applied for the detection and segmentation of mesiodens in the upper anterior region. For enhancement of the algorithm, pre-trained ResNet models were applied to the encoding path. The segmentation performance of the algorithm was tested using the Jaccard index and Dice coefficient. The diagnostic accuracy, precision, recall, F1-score and time to diagnosis of the algorithms were compared with those of human expert groups using the test dataset. Cohen's kappa statistics were compared between the model and human groups. The segmentation model exhibited a high Jaccard index and Dice coefficient (>90%). In mesiodens diagnosis, the trained model achieved 91-92% accuracy and a 94-95% F1-score, which were comparable with human expert group results (96%). The diagnostic duration of the deep learning model was 7.5 seconds, which was significantly faster in mesiodens detection compared to human groups. The agreement between the deep learning model and human experts is moderate (Cohen's kappa = 0.767). The proposed deep learning algorithm showed good segmentation performance and approached the performance of human experts in the diagnosis of mesiodens, with a significantly faster diagnosis time.

Accuracy of Gonial Angle Measurements Using Panoramic Imaging versus Lateral Cephalograms in Adults with Different Mandibular Divergence Patterns.

Introduction: Gonial angle is an important craniofacial parameter providing information about symmetry and vertical dimensions of the facial skeleton. It can be measured on panoramic radiographs and lateral cephalograms. Reliable assessment of the gonial angle is challenged by the superimpositions associated with lateral cephalograms. The aim of the current study was to assess the precision of panoramic imaging in measuring the gonial angles compared to lateral cephalograms in adult patients with different mandibular divergence patterns. Methods: Panoramic radiographs and lateral cephalograms of 448 adults (18-30 years old) were utilized in the study. The gonial angle was determined on the lateral cephalograms using an online AI-driven assessment tool (WebCephTM) and compared to the panoramic measurements among the different gender, malocclusion, and mandibular divergence groups. Results: Statistically significant differences were recorded between measurements taken on lateral cephalograms or panoramic radiographs (p=0.022). In addition, statistically significant differences were reported in gonial angle measurements on panoramic radiographs among the different mandibular divergence groups (p=0.004) for FMA (p=0.002) for Sn-GoMe. Conclusion: While cephalometry is considered the gold standard tool for reliable gonial angle assessment, panoramic radiographs were more accurate in detecting the differences between the divergence groups in the current study.

Evaluation of mandibular trabecular bone structure in growing children with Class I, II, and III malocclusions using fractal analysis: A retrospective study.

OBJECTIVE: The present study aimed to evaluate the mandibular bone structure of patients with different malocclusions by using fractal dimension (FD) analysis on dental panoramic radiographic images. MATERIAL AND METHODS: Pre-treatment panoramic radiographic images of 103 patients were included in the study and divided into 3 groups as Class I (group 1: 0

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.

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.

Prognostic risk factors for apical root resorption in orthodontic patients - Are the Kjær's morphologic characteristics of clinical relevance?

BACKGROUND: Patients undergoing orthodontic treatment (OT) face an increased risk of developing external apical root resorption (EARR). A prognostic risk assessment prior to OT can potentially be conducted through anatomical features in panoramic radiography. This retrospective study aimed to assess the significance of Kjær's morphological characteristics in analyzing the risk of EARR. METHODS: Panoramic radiographs of 1,156 patients (624 females, 532 males) were retrospectively analyzed. Anamnestic and treatment-related data were extracted from patient records. The mean age at the start of OT was 12.8 ± 2.2 years (min. 6.4 years, max. 22.3 years) and at the end of OT 15.9 years (min. 8.5 years, max. 24.1 years). The mean treatment duration was 3.1 ± 1.6 years. Panoramic radiographs with a minimum of two per patient were examined for the presence of Kjær's characteristics. The degree of EARR was registered defining resorption in four degrees of severity. Bivariate analysis and multivariate Poisson regression were performed to assess the association between Kjær's characteristics and EARR patient- and tooth- related (α = 0.05). RESULTS: In total, 72.8% of the patients showed EARR at the end of OT with lateral maxillary incisors most frequently affected. Short roots (p < 0.001) were significantly associated with EARR in patients. Tooth-related microdontia (#12, #22, lower second premolars), narrow crowns (#11, #21, lower incisors), short roots (upper incisors, lower first molars) and ectopia (#11, #21, #13), such as shorter distal roots of the mandibular first molar showed a significant association with EARR depending on severity degree. The type of orthodontic appliance (fixed: p < 0.001, fixed and removeable: p = 0.008), as well as treatment duration (p < 0.001) were also identified as risk factors for EARR. CONCLUSIONS: Although the risk assessment for EARR development through panoramic radiography analysis is limited, predisposition appears to be present in specific dental characteristics and treatment-related factors.

Radiographical Diagnostic Evaluation of Mandibular Cortical Index Classification and Mandibular Cortical Width in Female Patients Prescribed Antiosteoporosis Medication: A Retrospective Cohort Study.

Osteoporosis is often detected late and becomes severe because of a lack of subjective symptoms. Digital panoramic radiography (DPR) has been reported to be useful for osteoporosis screening based on the morphological classification of the mandibular inferior cortex. The purpose of this study was to evaluate the sensitivity and specificity of the mandibular cortical index (MCI) in the diagnosis of osteoporosis in a group of patients who were and were not using antiosteoporosis medication (AOM). Three hundred and fifty female patients aged 40 years or older who had DPR imaging performed during a 6-year period from December 2015 to February 2022 met the selection criteria. Two examiners recorded mandibular cortical width and MCI from the images. These results were statistically examined together with the patients' demographic data. Forty-nine patients were using AOM (13 nonbisphosphonate/denosumab and 36 bisphosphonate/denosumab). MCI type 3 was the most common in the AOM group. In the MCI classification, DPR imaging among the AOM group was more sensitive (0.95) than that of the control group. This method of estimating osteoporosis based on MCI classification using DPR images has high sensitivity, especially in patients using AOM, suggesting that this method is useful as a screening test.

Radiologic evaluation of associated symptoms and fractal analysis of unilateral dens invaginatus cases.

OBJECTIVE: The aim of this study is to determine the age-based prevalence of dens invaginatus cases and to learn the radiologic findings. In addition, fractal analysis of the periapical regions of unilateral dens invaginatus cases and contralateral teeth was performed to determine the effect of possible microleakage on fractal dimension. METHODS: A total of 136 patients (170 teeth) identified in panoramic radiographs taken for diagnostic purposes between January 2018 and December 2023 at our Department of Oral and Maxillofacial Radiology were included in the study. Data were analysed using IBM SPSS V23. The Chi-square test was used for comparing categorical variables between groups. The relationship between the FD values calculated around the apexes of the teeth with unilateral dens invaginatus and the contralateral teeth was analyzed by paired t test. A significance level of p < 0.050 was adopted. RESULTS: The mean age of the cases was calculated as 28.1 (8-63) years. 66 (48.5%) of the cases were female, and 70 (51.1%) were male. The lateral incisor was the most affected tooth among these cases. Bilateral cases were observed in 34 patients. Type 2 was detected in 93 patients, Type 1 in 35 patients, and Type 3 dens invagination in 8 patients. Dens invaginatus cases were observed in 134 maxillary and 2 mandibular teeth. Periapical lesions were seen in 22 of these cases. CONCLUSIONS: Dens invaginatus cases are a congenital anomaly encountered at any age. Early diagnosis is crucial to prevent the loss of pulp vitality due to these invaginations. It should be noted that these invaginations can affect both jaws. However, due to the prevalence of occurrence in the maxillary anterior teeth and asymptomatic nonvitality, particular attention should be paid to evaluating this region.

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.

Accuracy of CBCT in Preoperative Assessment of Impacted Canines vs. Panoramic Radiography.

Background: Accurate assessment of impacted canines is crucial for successful treatment planning. Radiographic techniques like Cone Beam Computed Tomography (CBCT) and Panoramic Radiography are commonly used, but their comparative accuracy remains under scrutiny. Materials and Methods: In this study, 50 patients diagnosed with impacted canines were enrolled. Both CBCT and Panoramic Radiography images were obtained for each patient using standard protocols. Two experienced dentists independently analyzed the images to determine the position of impacted canines and their relationship with neighboring structures. Results: The findings of this study revealed that CBCT provided superior accuracy in assessing the position of impacted canines compared to Panoramic Radiography. Specifically, CBCT demonstrated a mean accuracy rate of 89.5%, while Panoramic Radiography showed a mean accuracy rate of 72.3%. Moreover, CBCT allowed for better visualization of impacted canine angulation, depth, and spatial orientation. Panoramic Radiography, on the other hand, displayed limitations in precisely identifying the impacted canine's position. Conclusion: The study's outcomes underscore the higher accuracy of CBCT over Panoramic Radiography in the preoperative assessment of impacted canines. CBCT's detailed imaging provides valuable insights for treatment planning, potentially leading to improved surgical outcomes. Although CBCT entails greater radiation exposure and cost, its benefits in accurate diagnosis and treatment planning justify its use in cases of impacted canines.

Comparison of mandibular morphometric parameters in digital panoramic radiography in gender determination using machine learning.

OBJECTIVE: This study aimed to evaluate the usability of morphometric features obtained from mandibular panoramic radiographs in gender determination using machine learning algorithms. MATERIALS AND METHODS: High-resolution radiographs of 200 patients aged 20-77 (41.0 ± 12.7) were included in the study. Twelve different morphometric measurements were extracted from each digital panoramic radiography included in the study. These measurements were used as features in the machine learning phase in which six different machine learning algorithms were used (k-nearest neighbor, decision trees, support vector machines, naive Bayes, linear discrimination analysis, and neural networks). To evaluate the reliability, we have performed tenfold cross-validation and we repeated this 10 times for every classification process. This process enhances the reliability of the results for other datasets. RESULTS: When all 12 features are used together, the accuracy rate is found to be 82.6 ± 0.5%. The classification accuracies are also compared using each feature alone. Three features that give the highest accuracy are coronoid height (80.9 ± 0.9%), condyle height (78.2 ± 0.5%), and ramus height (77.2 ± 0.4%), respectively. When compared to the classification algorithms, the highest accuracy was obtained with the naive Bayes algorithm with a rate of 84.0 ± 0.4%. CONCLUSION: Machine learning techniques can accurately determine gender by analyzing mandibular morphometric structures from digital panoramic radiographs. The most precise results are achieved by evaluating the structures in combination, using attributes obtained from applying the MRMR algorithm to all features.

The 'D-M-C' strategy for conventional ameloblastoma of the mandible: a retrospective study.

The purpose of this multicentre study was to evaluate the efficacy of the 'dredging-marsupialization-curettage' (D-M-C) strategy in the treatment of conventional intraosseous ameloblastoma of the mandible. A total of 31 patients from three institutions, who had a pathological diagnosis of conventional ameloblastoma of the mandible, were treated with the D-M-C strategy. The surgical protocol comprised a dredging and marsupialization (D-M) step, with additional D-M steps as required. The patients then underwent curettage (C) once an obvious effect of the D-M step had been achieved during follow-up. Eight patients were followed up for ≥36 months but <60 months, while 23 were followed up for ≥60 months. Nineteen of the 23 patients followed up for ≥60 months were disease-free at the last follow-up, with no evidence of recurrence. The D-M step is effective for reducing the tumour size and preserving vital structures. The D-M-C surgical strategy may be a feasible treatment option for conventional ameloblastoma of the mandible.

Quantitative analysis of mandibular cortical morphology using artificial intelligence-based computer assisted diagnosis for panoramic radiography on underlying diseases and dental status in women over 20 years of age.

Background/purpose: Recently, an artificial intelligence-based computer-assisted diagnosis (AI-CAD) for panoramic radiography was developed to scan the inferior margin of the mandible and automatically evaluate mandibular cortical morphology. The aim of this study was to analyze quantitatively the mandibular cortical morphology using the AI-CAD, especially focusing on underlying diseases and dental status in women over 20 years of age. Materials and methods: 419 patients in women over 20 years of age who underwent panoramic radiography were included in this study. The mandibular cortical morphology was analyzed with an AI-CAD that evaluated the degree of deformation of the mandibular inferior cortex (MIC) and mandibular cortical index (MCI) automatically. Those were analyzed in relation to underlying diseases, such as diabetes, hypertension, dyslipidemia, rheumatism and osteoporosis, and dental status, such as the number of teeth present in the maxilla and mandible. Results: The degree of deformation of MIC in women under 51 years of age (21-50 years; n = 229, 16.0 ± 12.7) was significantly lower than those of over 50 years of age (51-90 years; n = 190, 45.1 ± 23.0), and the MCI was a significant difference for the different age group. Regarding the degree of deformation of MIC and MCI in women over 50 years of age, osteoporosis and number of total teeth present in the maxilla and mandible were significant differences. Conclusion: The results of this study indicated that the mandibular cortical morphology using the AI-CAD is significantly related to osteoporosis and dental status in women over 50 years of age.

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.