Emphatic information on bone mineral loss using quantitative ultrasound sonometer for expeditious prediction of osteoporosis.

Osteoporosis (OP) and osteoarthritis (OA) are skeletal disorders characterized by a reduction in bone density and quality, resulting in increased fragility and susceptibility to fractures. These illnesses are exhibiting a higher prevalence among both males and females. Fracture risk is determined by using the BMD score (Bone Mineral Density). Looking at the bone loss that comes with osteoarthritis (OA) and osteoporosis (OP), this study also looks at the technological methods used to test for these conditions in order to improve therapies and treatment plans for older people. As a matter of consideration, the prevalence of osteoporosis is higher among postmenopausal women (20%) compared to premenopausal women (14.28%) and males (6.77%). The utilization of a preliminary calcaneal Quantitative Ultrasound (QUS) examination is warranted in order to effectively handle the matter of osteoporosis. The prompt assessment of a patient can provide valuable insights into potential fractures and aid in the prevention of bone injury. In a nutshell, it is imperative to comprehend the impact of OA (osteoarthritis) and OP (osteoporosis) on bone health in order to effectively manage the escalating apprehensions surrounding these conditions. Sophisticated diagnostic techniques, such as the calcaneal quantitative ultrasound (QUS) test, have the potential to enhance the well-being of older individuals by enabling early detection and treatment of many ailments.

Segmentation of Ocular Thermogram Using Level-set Algorithm for Analysis of Contralateral Portions in Healthy Eyes

OBJECTIVE: This work aimed to evaluate the level set segmentation algorithm on ocular surface thermograms. In addition, the vascularity functioning between the contralateral portions of two eyes (right and left) was identified using statistical analysis methods. METHODS: A total of 25 healthy participants with an average age of 35 years (20 men and 5 women) were selected in April 2022. Thermogram images were captured using a FLIR T series thermal camera. Conventional image processing techniques, such as filtering and edge detection, were used to preprocess thermograms. Next, the level set approach was used with the edge-detected pattern as an input to an automated segmented region of interest (ROI). RESULTS: Five metrics, namely Dice Coefficient, Tanimoto Index, Jaccard Index, Volume Similarity, and Structural Similarity, were used to assess the performance of the segmentation technique compared to ground truth, which showed 97.5%, 92.5%, 94.5%, 96.5%, and 96.5% correlation, respectively, between the segmented and the ground truth images with average values for both the eyes. Statistical analysis demonstrated that the contralateral portions of the ocular thermograms were significantly different in terms of vascular distribution between the left and right eyes (p < 0.005) CONCLUSION: The level set method efficiently segmented the ROI in ocular thermograms with maximum correlation. According to the segmentation's results, the model showed the dissimilarity between the contralateral parts of the left and right eyes in healthy cases.