Bone mineral density (BMD) is a key feature in diagnosing bone diseases. Although computational tomography (CT) is a common imaging modality, it seldom provides bone mineral density information in a clinic owing to technical difficulties. Thus, a dual-energy X-ray absorptiometry (DXA) is required to measure bone mineral density at the expense of additional radiation exposure. In this study, a deep learning framework was developed to estimate the bone mineral density from an axial cut of the L1 bone on computational tomography. As a result, the correlation coefficient between bone mineral density estimates and dual-energy X-ray absorptiometry bone mineral density was .90. When the samples were categorized into abnormal and normal groups using a standard (T-score =−1.0), the maximum F1 score in the diagnostic test was .875. In addition, it was identified using explainable artificial intelligence techniques that the network intensively sees a local area spanning tissues around the vertebral foramen. This method is well suited as an auxiliary tool in clinical practice and as an automatic screener for identifying latent patients in computational tomography databases.
Vam Cong Cable Stayed Bridge which has 450m main span length is one of the Central Mekong Delta Region Connectivity Project and is located in Cuu Long Delta Region. It has steel-concrete composite girder with 4 lane and the type of cable is multi strand cable. The improved H-shape pylon and cast-in-place bored piles were applied. High strength concrete is applied for pylon, precast concrete slab and Cast-in-Situ concrete pile to ensure the structural safety. The present paper describe the design specifications and main features of Vam Cong Cable Stayed Bridge design.
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