Background: The mechanism behind parathyroid hormone (PTH) activation of bone remodeling is intimately dependent on the time of exposure of bone cells to hormone levels. Sustained high PTH levels trigger catabolism, while transitory elevations induce anabolism. The effects of hypoparathyroidism (PhPT) on bone are unknown. The objective was to study the impact of PhPT on bone mineral density (BMD), on the frequency of subclinical vertebral fracture and on mandible morphometry.Methods: The study comprised thirty-three postmenopausal women, 17 controls (CG) and 16 with PhPT (PhPTG) matched for age, weight and height. Bone mineral density (BMD) of lumbar spine, total hip and 1/3 radius, radiographic evaluation of vertebral morphometry, panoramic radiography of the mandible, and biochemical evaluation of mineral metabolism and bone remodeling were evaluated in both groups.Results: There were no significant differences in lumbar spine or total hip BMD between groups. There was marked heterogeneity of lumbar spine BMD in PhPTG (high = 4, normal = 9, osteopenia = 1, and osteoporosis = 2 patients). BMD was decreased in the 1/3 radius in PhPTGP < 0.005). The PhPTG group exhibited an increased frequency of morphometric vertebral fractures and decreased mandible cortical thickness.Conclusion: The study suggests that vertebral fragility occurs in PhPT despite normal or even high BMD. The current results encourage further studies to evaluate the use of panoramic radiography in the identification of osteometabolic disorders, such as PhPT and the development of a more physiological treatment for PhPT.

The aim of this study was to evaluate radiomorphometric indices in dental panoramic radiographs in order to identify possible interrelationships between these indices and the sex and age of the patients analyzed. The study included 1287 digital panoramic radiographic images of patients that were grouped into five age groups (1 = age 17–20; 2 = age 21–35; 3 = age 36–55; 4 = age 56–69; 5 = over age 70). Two indices—cortical width at the gonion (GI) and below the mental foramen (MI)—were measured bilaterally in all panoramic radiographs. Statistical analysis was performed with Kruskal-Wallis and Mann-Whitney tests (alpha = 0.05). Results for the indices measurements showed significant differences among patient age groups of both sexes, considering that groups 4 and 5 presented lower values for the cortical width of both indices. The present paper supports the role of sex- and age-related changes in mandibular radiomorphometric indices in identifying skeletal osteopenia.

A number of panoramic radiographic measurements have been associated with osteoporotic alterations. However, little is known about the differences in sensitivity and specificity among these measurements for screening low bone mineral density (BMD). Aim: To correlate and compare precision, sensitivity and specificity of panoramic radiomorphometric indices and fractal dimension (FD) for screening low BMD (i.e. osteopenia and osteoporosis). Methods: Sixty-eight female patients (42.78±15.59 years) were included in this study. Body mass index (BMI), mandibular cortical index (MCI), mandibular cortical width (MCW), FD and connectivity (C) were assessed. Low BMD was diagnosed by peripheral dual-energy X-ray absorptiometry (p-DXA). Non-parametric correlations were assessed among all variables. In addition, sensitivity and specificity of MCI, MCW and FD were estimated for screening low BMD. Results: Significant correlation was found between FD and BMI (p=0.013; r=0.269). In addition, FD was the most sensitive method for screening low BMD (70.8%, p=0.001). FD and MCI presented a significant and relatively high sensitivity, whereas MCW presented a high specificity for screening low systemic BMD Conclusions: Among the analyzed methods, FD and MCI offer a significant and relatively high sensitivity, whereas MCW offers a high specificity for screening low BMD.

Many studies in the last decades have correlated mandible bone structure with systemic diseases like osteoporosis. Mandible segmentation, as well as segmentation of other oral structures, is an essential step in studies that correlate oral structures’ conditions with systemic diseases in general. However, manual mandible segmentation is a time-consuming and training-required task that suffers from inter and intra-user variability. Further, the dental panoramic x-ray image (PAN), the most used image in oral studies, contains overlapping of many structures and lacks contrast on structures’ interface. Those facts make both manual and automatic mandible segmentation a challenge. In the present study, we propose a precise and robust set of deep learning-based algorithms for automatic mandible segmentation (AMS) on PAN images. Two datasets were considered. An in-house image dataset with 393 image/segmentation pairs was prepared using image data of 321 image patient data and the corresponding manual segmentation performed by an experienced specialist. Additionally, a publicly available third-party image dataset (TPD) composed of 116 image/segmentation pairs was used to train the models. Four deep learning models were trained using U-Net and HRNet architectures with and without data augmentation. An additional morphological refinement routine was proposed to enhance the models’ prediction. An ensemble model was proposed combining the four best-trained segmentation models. The ensemble model with morphological refinement achieved the highest scores on the test set (98.27%, 97.60%, 97.18%, ACC, DICE, and IoU respectively), with the other models scoring above 95% in all performance metrics on the test set. The present study achieved the highest ranked performance considering all the previously published results on AMS for PAN images. Additionally, those are the most robust results achieved since it was performed over an image set with considerable gender representativeness, a wide age range, a large variety of oral conditions, and images from different imaging scans.