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BackgroundEarly diagnosis of osteoporosis can potentially decrease the risk of fractures and improve the quality of life. Detection of thin inferior cortices of the mandible on dental panoramic radiographs could be useful for identifying postmenopausal women with low bone mineral density (BMD) or osteoporosis. The aim of our study was to assess the diagnostic efficacy of using kernel-based support vector machine (SVM) learning regarding the cortical width of the mandible on dental panoramic radiographs to identify postmenopausal women with low BMD.MethodsWe employed our newly adopted SVM method for continuous measurement of the cortical width of the mandible on dental panoramic radiographs to identify women with low BMD or osteoporosis. The original X-ray image was enhanced, cortical boundaries were determined, distances among the upper and lower boundaries were evaluated and discrimination was performed by a radial basis function. We evaluated the diagnostic efficacy of this newly developed method for identifying women with low BMD (BMD T-score of -1.0 or less) at the lumbar spine and femoral neck in 100 postmenopausal women (≥50 years old) with no previous diagnosis of osteoporosis. Sixty women were used for system training, and 40 were used in testing.ResultsThe sensitivity and specificity using RBF kernel-SVM method for identifying women with low BMD were 90.9% [95% confidence interval (CI), 85.3-96.5] and 83.8% (95% CI, 76.6-91.0), respectively at the lumbar spine and 90.0% (95% CI, 84.1-95.9) and 69.1% (95% CI, 60.1-78.6), respectively at the femoral neck. The sensitivity and specificity for identifying women with low BMD at either the lumbar spine or femoral neck were 90.6% (95% CI, 92.0-100) and 80.9% (95% CI, 71.0-86.9), respectively.ConclusionOur results suggest that the newly developed system with the SVM method would be useful for identifying postmenopausal women with low skeletal BMD.

COVID - 19 (Corona Virus Disease-2019) is an infectious disease caused by a novel coronavirus, known as the acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This is a highly contagious disease that has already affected more than 220 countries globally, infecting more than 212 million people and resulting in the death of over 4.4 million people. This review aims to highlight the pertinent documentary evidence upon the adverse effects of the SARS-CoV-2 infection on several vital human organs. SARS-CoV-2 primarily targets the lung tissue by causing diffuse alveolar damage and may result in Acute Respiratory Distress Syndrome (ARDS). SARS-CoV-2 infects the cell via cell surface receptor, angiotensin-converting enzyme 2 (ACE2). Besides lungs, SARS-CoV-2 critically damage tissues in other vital human organs such as the heart, kidney, liver, brain, and gastrointestinal tract. The effect on the heart includes muscle dysfunction (acute or protracted heart failure), myocarditis, and cell necrosis. Within hepatic tissue, it alters serum aminotransferase, total bilirubin, and gamma-glutamyl transferase levels. It contributes to acute kidney injury (AKI). Localized infection of the brain can lead to loss or attenuation of olfaction, muscular pain, headaches, encephalopathy, dizziness, dysgeusia, psychomotor disorders, and stroke; while the gastrointestinal symptoms include the disruption of the normal intestinal mucosa, leading to diarrhea and abdominal pain. This review encompassed a topical streak of systemic malfunctions caused by the SARS-CoV-2 infection. As the pandemic is still in progress, more studies will enrich our understanding and analysis of this disease.

Our new computer-aided diagnosis system is a useful procedure in triage screening for osteoporosis.

PurposeTo prevent low bone mineral density (BMD), that is, osteoporosis, in postmenopausal women, it is essential to diagnose osteoporosis more precisely. This study presented an automatic approach utilizing a histogram-based automatic clustering (HAC) algorithm with a support vector machine (SVM) to analyse dental panoramic radiographs (DPRs) and thus improve diagnostic accuracy by identifying postmenopausal women with low BMD or osteoporosis.Materials and MethodsWe integrated our newly-proposed histogram-based automatic clustering (HAC) algorithm with our previously-designed computer-aided diagnosis system. The extracted moment-based features (mean, variance, skewness, and kurtosis) of the mandibular cortical width for the radial basis function (RBF) SVM classifier were employed. We also compared the diagnostic efficacy of the SVM model with the back propagation (BP) neural network model. In this study, DPRs and BMD measurements of 100 postmenopausal women patients (aged >50 years), with no previous record of osteoporosis, were randomly selected for inclusion.ResultsThe accuracy, sensitivity, and specificity of the BMD measurements using our HAC-SVM model to identify women with low BMD were 93.0% (88.0%-98.0%), 95.8% (91.9%-99.7%) and 86.6% (79.9%-93.3%), respectively, at the lumbar spine; and 89.0% (82.9%-95.1%), 96.0% (92.2%-99.8%) and 84.0% (76.8%-91.2%), respectively, at the femoral neck.ConclusionOur experimental results predict that the proposed HAC-SVM model combination applied on DPRs could be useful to assist dentists in early diagnosis and help to reduce the morbidity and mortality associated with low BMD and osteoporosis.