Riise Hutchinson (handfrost94)

rnative, or complementary examination to conventional FP-CEMRA in patients who have difficulty breath holding, or in situations where contrast bolus timing was poor.PURPOSE The purpose of this study was to validate the accuracy of an artificial intelligence (AI) prototype application in determining bone mineral density (BMD) from chest computed tomography (CT), as compared with dual-energy x-ray absorptiometry (DEXA). MATERIALS AND METHODS In this Institutional Review Board-approved study, we analyzed the data of 65 patients (57 female, mean age 67.4 y) who underwent both DEXA and chest CT (mean time between scans 1.31 y). From the DEXA studies, T-scores for L1-L4 (lumbar vertebrae 1 to 4) were recorded. Patients were then divided on the basis of their T-scores into normal control, osteopenic, or osteoporotic groups. An AI algorithm based on wavelet features, AdaBoost, and local geometry constraints independently localized thoracic vertebrae from chest CT studies and automatically computed average Hounsfield Unit (HU) values with kVp-dependent spectral correction. The Pearson correlation evaluated the correlation between the T-scores and HU values. Mann-Whitney U test wae comprehensive preventative care based on a single chest CT.Artificial intelligence (AI) algorithms are dependent on a high amount of robust data and the application of appropriate computational power and software. AI offers the potential for major changes in cardiothoracic imaging. Beyond image processing, machine learning and deep learning have the potential to support the image acquisition process. AI applications may improve patient care through superior image quality and have the potential to lower radiation dose with AI-driven reconstruction algorithms and may help avoid overscanning. This review summarizes recent promising applications of AI in patient and scan preparation as well as contrast medium and radiation dose optimization.Because the healthcare landscape is in a state of extreme disruption, the ability to adapt is essential for organizations and their leadership teams. Players outside the sector, changing workforce and patient demographics, new technologies, cost pressures, and other influences are challenging hospital and health systems' abilities to perform as expected.Amid this ambiguity, a lack of urgency is preventing organizations from adjusting to a business environment in flux. Never has it been more important for leaders to show courage, learn, and guide their organizations to the front lines of innovation. In today's world, being a learning organization-one in which leaders and team members lean together into change, rather than back away-is crucial to remaining relevant. To quote philosopher Eric Hoffer, "In times of change, learners inherit the earth while the learned find themselves beautifully equipped to deal with a world that no longer exists."At Virginia Mason, a health system based in Seattle, Washington, our management methodology-the Virginia Mason Production System-allows the organization to be a nimble responder to change. It also empowers individuals across the enterprise, regardless of job or title, to assume hands-on roles in accomplishing our collective vision to transform healthcare. This "we culture" shines a bright light on improvement opportunities and provides the framework needed for collaborative, interdisciplinary efforts to develop solutions.Quality and safety are the top priorities of every hospital and health system. Patients put their trust in us as healthcare leaders, often in the most vulnerable moments of their lives, and we must respond with high-quality, highly reliable, and compassionate care.Patients have the right to expect the best return for their healthcare dollars. Recognizing that expectation, progressive hospitals and health systems are embracing the move to value-based care and are enhancing affordability. The goal is to provide greater value to patients by improving quality, lowering costs