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https://www.selleckchem.com/products/mrt68921.html Health professionals extensively use 2D US videos and images to visualize and measure internal organs for various purposes including evaluation of muscle architectural changes. US images can be used to measure abdominal muscles dimensions for the diagnosis and creation of customized treatment plans for patients with LBP, however, they are difficult to interpret. Due to high variability, skilled professionals with specialized training are required to take measurements to avoid low intra-observer reliability. This variability stems from the challenging nature of accurately finding the correct spatial location of measurement endpoints in abdominal US images. In this paper, we use a DL approach to automate the measurement of the abdominal muscle thickness in 2D US images. By treating the problem as a localization task, we develop a modified FCN architecture to generate blobs of coordinate locations of measurement endpoints, similar to what a human operator does. We demonstrate that using the TrA400 US image dataset, our network achieves a MAE of 0.3125 on the test set, which almost matches the performance of skilled ultrasound technicians. Our approach can facilitate next steps for automating the process of measurements in 2D US images, while reducing inter-observer as well as intra-observer variability for more effective clinical outcomes.Non-used clinical information has negative implications on healthcare quality. Clinicians pay priority attention to clinical information relevant to their specialties during routine clinical practices but may be insensitive or less concerned about information showing disease risks beyond their specialties, resulting in delayed and missed diagnoses or improper management. In this study, we introduced an electronic health record (EHR)-oriented knowledge graph system to efficiently utilize non-used information buried in EHRs. EHR data were transformed into a semantic patient-centralized

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