This study aimed to identify whether healthy older adults are at a high risk of osteoporosis based on the association between the T-score values of bone mineral density (BMD) and three panoramic indices. All participants (50–64 years old) underwent calcaneal quantitative ultrasound (QUS) bone densitometry. In total, 371 panoramic images were included in the analysis. The mandibular cortical width (MCW), mandibular cortical index (MCI), and panoramic mandibular index (PMI) were the three parameters assessed. The data were examined using one-way analysis of variance or Kruskal–Wallis H and post hoc tests. Spearman’s rho was used to measure correlation coefficients. T-score distributions in the normal, osteopenia, and osteoporosis categories were 80 (21.6%), 236 (63.6%), and 55 (14.8%), respectively. T-scores were significantly related to age, MCW, PMI, and MCI (p < 0.001). Participants with osteopenia and a risk of progression to osteoporosis had an average MCW value of < 3.38 mm, a PMI of < 0.31, and an MCI class 2 and 3 distribution in 211 (56.9%) participants. The highest to lowest correlation coefficients with a significant relationship to the T-score were for age (r = -0.844), MCI (r = 0.456), MCW (r = 0.359), sex (r = 0.354), and PMI value (r = 0.292). This study found that healthy older people with Class 3 MCI, an MCW < 3 mm, and a PMI ratio < 0.3 had a significant association with lower BMD T-scores on QUS and were at higher risk of osteoporosis.

The purpose of this study is to evaluate the relationship between radiomorphometric indices and fractal dimension (FD) analysis in jawbone radiographic density using Cone-Beam Computed Tomography (CBCT). The study included 87 CBCT datasets from dental patients aged 50–79. Four radiomorphometric indices (Computed Tomography Cortical Index (CTCI), Computed Tomography Mental Index (CTMI), Computed Tomography Index-Superior (CTI-S), and Computed Tomography Index-Inferior (CTI-I)) along with age, gender, remaining teeth, and mandibular bone height were evaluated. FD was assessed in trabecular bone near the mental foramen (ROI 1) and on the mandibular cortical bone (ROI 2). Relationships between indices and variables were analyzed using Pearson chi-square, Fisher’s exact, one-way ANOVA, Kruskal–Wallis, and Spearman’s rho tests. ANOVA showed significant differences in mandibular bone height (p = .004) and FD at ROI 2 (p = .000) across three mandibular cortex types (C1, C2, C3). Spearman’s rho indicated significant correlations between FD at ROI 2 and CTMI, CTI-S, CTI-I (p = .000), and between mandibular bone height and CTMI (p = .010). The correlations of FD at ROI 2 ranged from strongest to weakest with CTCI, CTMI, CTI-S, and CTI-I. High-risk osteoporosis radiomorphometric indices are associated with FD analysis of cortical bone.

Introduction: To investigate protocols for volumetric measurement of alveolar defect in alveolar cleft cases, including innovations in Artificial Intelligence (AI). Methods: Searches were conducted using PubMed, Embase, and Scopus databases, along with a hand search. Based on inclusion and exclusion criteria, 17 studies were selected. Additionally, discussions on the protocols included workflow and anatomical landmarks associated with the measurement. Results: Thirty workflows were identified and categorized into virtual and 3D printing-based approaches. A 3D U-Net architecture was employed for segmentation and measurement using artificial intelligence. Various anatomical landmarks for defining alveolar cleft boundaries were described. The average volume of the alveolar cleft was 1.61 cm³. Discussion: The majority of the studies were published within 3 years of the article search, indicating an increased desire to optimize the utilization of 3D imaging beyond simple assessments. Recent developments in AI have simplified complex imaging tasks; hence, volumetric assessments are expected to increase in the future. Conclusion: The expert workflow with the most supporting evidence is manual tracing on the axial slice. Studies using AI are emerging and need to be explored. The anatomical landmarks advocated by this review are cementoenamel junction, anterior nasal spine, and continuity with the alveolar segments with adequate labio-palatal thickness as superior, inferior, and labio-palatal borders, respectively. Nonetheless, more studies are needed to help create a technical guideline.

Cone-beam computed tomography (CBCT) is becoming more widely used in the field of dentomaxillofacial radiography, but its utility for bone quality assessment is still limited. This study was conducted to describe the use of radiomorphometric examination and fractal dimensions (FDs) for osteoporosis risk detection through CBCT in elderly patients. Medical databases (PubMed, Scopus, Elsevier, and Directory of Open Access Journals (DOAJ)) were searched using the keywords osteoporosis, radiomorphometric, fractal dimension, and fractal analysis. The search limits applied were available full-text articles, publication years 2012-2021, and articles published available in English. Then, the articles included were systematically reviewed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. A total of four studies were included in this review. Seven radiomorphometric indices were used, and most indices were adopted from panoramic radiographs, such as the computed tomographycortical index, computed tomography mental index, computed tomography index, and four other indices along the mandible, which are the S (symphysis), A (anterior), M (molar), and P (posterior) indices. All of the radiomorphometric studies show similar results. These indices can identify osteoporosis-related changes and are useful as osteoporosis screening tools on CBCT. However, all FD studies show different methods and discover heterogeneous results. Radiomorphometric measurement methods in CBCT can be used to detect patients at risk for osteoporosis. The FD analysis method still finds heterogeneous research results, so it is recommended to standardize the method in terms of the shape, size, and location of the region of interest.

Introduction: Temporomandibular joint (TMJ) disorders encompass a wide range of conditions impacting the joint and its surrounding structures, often leading to pain, dysfunction, and structural alterations. Understanding the predictors of condylar changes is crucial for early diagnosis and effective management, particularly in patients experiencing significant functional limitations. Objectives: This study aimed to examine the combined impact of sex and cortication classification on mandibular condylar changes, as detected by cone‐beam computed tomography (CBCT), to identify key predictors for TMJ degeneration. Methods: A cross‐sectional study was conducted on 64 patients (33 men, 31 women) aged 30 years and older, who underwent CBCT imaging. Demographic data (age, sex), dental factors (remaining teeth count, posterior tooth loss), and anatomical measurements (condylar height, width, length, and cortication on the mandibular condyle [CMC]) were analyzed. Condylar changes were classified as osteophytes, flattening, sclerosis, erosion, or pseudocysts. Chi‐square tests, independent t ‐tests, and logistic regression were applied to identify predictors of condylar changes. Results: Women showed a significantly higher likelihood of condylar changes than men, with an odds ratio (OR) of 0.18, suggesting men were 82% less likely to exhibit these changes. CMC classification also played a significant role; individuals with CMC classifications of 0 or 1 had a 7.5 times higher risk of condylar changes than those with CMC class 2. Additionally, reduced condylar height was significantly associated with degenerative changes (mean height: 20.49 ± 2.5 mm in affected individuals versus 22.34 ± 3.2 mm in unaffected individuals, p = 0.017). Conclusion: The study concluded that both sex and CMC classification are significant predictors of condylar changes in TMJ disorders.