Aug 2026· Dento maxillo facial radiology· 0 citations
Medicine
TL;DR
Panoramic radiography-based radiomic analysis demonstrates high diagnostic performance in non-invasively distinguishing mandibular bone alterations among T1DM, T2DM, and healthy individuals, with potential as a clinical bone monitoring tool.
Abstract
Objective
This study aimed to evaluate the structural characteristics of mandibular alveolar bone in patients with Type 1 diabetes mellitus (T1DM), Type 2 diabetes mellitus (T2DM), and systemically healthy controls using panoramic radiography-based radiomic analysis combined with machine learning algorithms.
Materials And Methods
A total of 225 panoramic radiographs (75 T1DM, 75 T2DM, 75 healthy controls) were retrospectively analyzed. ROIs were segmented from eight anatomical mandibular segments per subject, and 107 radiomic features were extracted using PyRadiomics. Interobserver reliability was confirmed by two-way random-effects ICC (≥0.85). A leakage-free pipeline was applied. Four machine learning algorithms were evaluated: Random Forest, ExtraTrees, SVM-RBF, and Logistic Regression.
Results
Significant differences were identified in age (Kruskal-Wallis p = 0.0003) and sex (χ²=17.857, p = 0.0001). The best single-segment performance was achieved in the left mandibular corpus with Logistic Regression (Accuracy=0.833, F1=0.832, AUC=0.958). All segments showed significant radiomic differences (FDR q < 0.001). Sensitivity of 1.000 was achieved for T1DM and AUC=1.000 for T2DM. The feature glszm_SizeZoneNonUniformityNormalized showed the strongest discriminative power (H = 86.928, ε²=0.578).
Conclusion
Panoramic radiography-based radiomic analysis demonstrates high diagnostic performance in non-invasively distinguishing mandibular bone alterations among T1DM, T2DM, and healthy individuals, with potential as a clinical bone monitoring tool.
OBJECTIVE
Osteolytic jaw lesions are heterogeneous and share radiographic features. Radiomics from CBCT may provide quantitative biomarkers to aid differential diagnosis between developmental and inflammatory Odontogenic Cysts.
METHODS
A retrospective radiomic analysis was performed on CBCT scans of osteolytic jaw lesions from 52 patients; after quality control, 44 lesions were included (28 developmental and 16 inflammatory cysts). Lesions were segmented in 3D Slicer, and radiomic features were extracted with PyRadiomics. Unsupervised analyses used K-means and principal component analysis (PCA). Group differences were tested with ANOVA. For supervised classification, Logistic Regression, Random Forest, SVM, Multilayer Perceptron, and a stacking model were trained using PCA-based dimensionality reduction, mutual-information feature selection, SMOTE oversampling, and repeated stratified 5-fold cross-validation.
RESULTS
One thousand four hundred and thirty-seven features per lesion were extracted; univariate analysis identified 11 radiomic features showing differences between the two diagnostic groups (p < 0.05). However, none remained statistically significant after false discovery rate (FDR) correction, indicating the exploratory nature of these findings. Random Forest showed the best performance (AUC 0.76, accuracy 0.74, specificity 0.81, sensitivity 0.61). Unsupervised clustering did not reveal separated groups.
CONCLUSIONS
While individual radiomic features did not demonstrate significance, a multivariate machine learning approach may identify complex signatures with moderate discriminative performance.
Pierluigi Mariani, Francesco Fanelli, Diana Russo et al.· Oral Diseases· 0 citations
Introduction The comparative diagnostic utility of cone-beam computed tomography (CBCT) and intraoral dental radiography (IDR) for endodontal disease in dogs remains unclear. Accordingly, this study evaluated both imaging modalities against histopathologic findings as the gold standard. Methods In this prospective observational study, client-owned dogs undergoing evaluation for suspected endodontal disease were imaged with both CBCT and IDR under general anesthesia. Forty-six teeth from 16 dogs were included. Four radiographic features—periapical lucency, failure of pulp canal narrowing, external inflammatory root resorption, and loss of crown integrity—were independently scored by five blinded evaluators using a standardized qualitative scale. Histopathological assessment of pulp vitality was available for 18 teeth. Diagnostic performance, inter-rater agreement, and modality-related effects were analyzed using mixed-effects models and concordance statistics. Results CBCT was associated with significantly greater detection of imaging features associated with endodontal disease than IDR overall (p < 0.002), particularly for periapical lucency (p < 0.001). Both modalities demonstrated high sensitivity for identifying non-vital teeth, but limited specificity, reflecting disease prevalence within the histopathology subset. Inter-rater agreement was modest for both modalities and was affected primarily by scoring variability among evaluators rather than systematic differences in scoring patterns. Discussion CBCT demonstrated superior detection of periapical lucency compared with IDR in dogs. Nonetheless, inter-rater variability remains a significant limitation, emphasizing the need for standardized image interpretation and integration with clinical findings.
Serena Bonacini, Stephanie L Goldschmidt, B. Arzi et al.· Frontiers in Veterinary Scie...· 0 citations
This study aimed to assess the diagnostic capability of a YOLOv7 deep learning algorithm for the computerized detection of periapical lesions from pediatric panoramic radiographs. Its potential utility as a supportive diagnostic tool and accurate diagnosis in mixed dentition cases was further assessed by comparing the algorithm’s performance with the diagnoses made by dental students. In this study, a total of 408 panoramic radiographs were used, consisting of 333 original images and 75 images generated through feature-based preprocessing expansion. The YOLOv7 model was trained on 302 images, which included 227 original radiographs and 75 images specifically enhanced via grayscale conversion, noise reduction, and edge detection filters to emphasize structural pathological features. A relatively larger set was allocated for testing in order to enhance robustness despite the small sample size. The diagnostic capability of the algorithm and trainees was compared using accuracy, sensitivity, specificity, precision, F1 score, and error rate. YOLOv7 achieved higher diagnostic performance compared with the student group. Its sensitivity (76.1%) was also higher than that of the students (55.2%). The algorithm further demonstrated superior specificity (99.8% vs. 97.3%), precision (99.8% vs. 95.3%), and F1 score (86.4% vs. 69.9%). The findings suggest promising potential in the YOLOv7 algorithm’s performance for detecting periapical lesions in deciduous teeth on panoramic radiographs, compared with the diagnostic accuracy of the students.
Parmis Shahmaleki, Pelin Alcan Gezginci, Yasin Kırelli et al.· BMC Pediatrics· 0 citations
The maxillary sinus is closely related to the posterior maxillary teeth, making accurate radiographic evaluation essential for diagnosing odontogenic and non-odontogenic sinus pathologies and guiding appropriate treatment planning. This study aimed to evaluate the diagnostic performance of panoramic radiography (OPG) compared with cone beam computed tomography (CBCT) and to identify the maxillary sinus lesions that can be reliably detected on panoramic imaging. A retrospective cross-sectional study was conducted using 437 panoramic radiographs obtained from patients attending public dental clinics in Misurata, Libya. Among these, 316 patients underwent CBCT on the same day, allowing direct paired comparison between imaging modalities. CBCT served as the reference standard. Evaluated pathologies included mucosal thickening, odontogenic and non-odontogenic sinusitis, mucous retention cysts, mucosal polyps, foreign bodies, mucoceles, oroantral fistulas, fluid accumulation, and tumors. Statistical analysis was performed using the chi-square test and McNemar's test, with statistical significance set at P < 0.05. Mucosal thickening was the most common finding (26%), followed by non-odontogenic sinusitis (18%), odontogenic sinusitis (13%), mucosal polyps (10%), mucous retention cysts (8%), and foreign bodies (2%). OPG detection varied significantly according to lesion type (χ² = 71.38, P < 0.001). Compared with CBCT, panoramic radiography demonstrated significantly lower sensitivity for subtle inflammatory lesions, whereas well-defined lesions, including mucous retention cysts and mucosal polyps, were reliably identified. CBCT provided superior assessment of lesion morphology, disease extent, and tooth–sinus anatomical relationships. Panoramic radiography remains a valuable, accessible first-line imaging modality for the initial evaluation of maxillary sinus pathology. However, CBCT should be considered when subtle inflammatory disease, odontogenic extension, or detailed anatomical assessment is required. A stepwise imaging approach integrating both modalities may improve diagnostic accuracy and clinical decision-making.
Ibtisam Senussi, Antisar Ben Amer, Aya Krayem· AlQalam journal of medical a...· 0 citations
Introduction Accurate evaluation of alveolar bone density is essential for dental diagnosis and treatment planning. Panoramic radiographs offer relative estimates of bone density. Due to limited data in Iranian populations, this study assessed the radiographic density of normal periapical bone using panoramic imaging. Materials and Methods This descriptive cross‐sectional study analyzed digital panoramic radiographs at three anatomical regions—anterior, premolar, and molar—in both jaws. A standardized 1.5 mm × 1.5 mm region of interest (ROI) was positioned within 2 mm of the apical third of each tooth. Bone density was quantified as gray values (arbitrary units [AU]) using Digora software. Demographic data were collected, and statistical comparisons between groups were performed using independent t‐tests and one‐way ANOVA at a significance level of α = 0.05. Results In 458 panoramic radiographs (212 males, 246 females; ages 20–50), bone density was significantly higher in males, older individuals (35–50), and the mandible compared to the maxilla. Density decreased from anterior to molar regions. The anterior mandible showed the highest values, with notable differences from the anterior maxilla, while premolar and molar regions showed no significant interjaw differences. Conclusion Panoramic radiographs revealed consistent patterns in periapical bone density. Density was higher in males, older adults, and the mandible compared to the maxilla. The anterior mandible showed the highest values overall. In the maxilla, bone density increased from anterior to posterior regions. These findings support baseline assessment and suggest further validation with 3D imaging.
Motahare Baghestani, Amirhossein Zirehpour, M. Azimzadeh et al.· International Journal of Den...· 0 citations
INTRODUCTION
Periapical granulomas and radicular cysts cannot be reliably differentiated by clinical examination or conventional radiography. Histopathological examination remains the gold standard for definitive diagnosis. Because biopsy is invasive, cone-beam computed tomography (CBCT)-based radiomics approaches have been explored as noninvasive alternatives. However, their performance is affected by intensity variability inherent to CBCT imaging. The purpose of this study was to evaluate a dentin-based intensity normalization approach to improve the robustness of radiomics-based classification of periapical lesions.
MATERIALS AND METHODS
CBCT scans from 140 patients with histopathologically confirmed periapical lesions (100 granulomas, 40 cysts) were retrospectively analyzed. Lesion-adjacent root dentin was approximated as an internal reference region and used for affine intensity normalization. Radiomic features were extracted from segmented lesion volumes before and after normalization within the Periapical Lesion Differential Diagnosis System (PLDDS). Models were evaluated using morphology-only, first-order/texture-only, and full-radiomics feature settings with eXtreme Gradient Boosting and logistic regression classifiers. Performance was assessed using stratified 5-fold cross-validation, AUC (area under the curve), complementary classification metrics, confidence intervals, and statistical comparison of AUCs.
RESULTS
Dentin-normalized full radiomics with XGBoost achieved the highest overall performance, with an AUC of 0.78. Dentin normalization significantly improved AUC compared with raw full radiomics in the XGBoost model (0.68 vs 0.78; P<.05). AUC also improved after normalization in the first-order/texture setting for both classifiers, suggesting that normalization primarily benefited gray-value-dependent features.
CONCLUSIONS
Dentin-based intensity normalization improved the performance and stability of CBCT radiomics-based models for differentiating periapical granulomas and radicular cysts. The higher AUC of dentin-normalized full radiomics compared with morphology-only features suggest that lesion-adjacent dentin may serve as a practical internal reference for stabilizing gray-value-dependent radiomic features and that first-order and texture features provided complementary information beyond lesion morphology. Further validation across different CBCT systems and acquisition protocols is required before this approach may be considered for clinical implementation.
Yeonju Lee, R. Chen, Hao Yan et al.· Journal of Endodontics· 0 citations
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