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Analysis of Volume and Topography of Adipose Tissue in the Trunk: Results of MRI of 11,141 Participants in the German National Cohort
Abstract: This research addresses the assessment of adipose tissue (AT) and spatial distribution of visceral (VAT) and subcutaneous fat (SAT) in the trunk from standardized magnetic resonance imaging at 3 T, thereby demonstrating the feasibility of deep learning (DL)-based image segmentation in a large population-based cohort in Germany (five sites). Volume and distribution of AT play an essential role in the pathogenesis of insulin resistance, a risk factor of developing metabolic/cardiovascular diseases. Cross-validated training of the DL-segmentation model led to a mean Dice similarity coefficient of >0.94, corresponding to a mean absolute volume deviation of about 22 ml. SAT is significantly increased in women compared to men, whereas VAT is increased in males. Spatial distribution shows age- and body mass index-related displacements. DL-based image segmentation provides robust and fast quantification of AT (≈15 s per dataset versus 3 to 4 hours for manual processing) and assessment of its spatial distribution from magnetic resonance images in large cohort studies
(Vor)Sorge und (Un)Gleichheiten in pandemischen Zeiten
Die COVID-19-Pandemie und die Bemühungen zu ihrer Eindämmung wirken stark auf Ungleichheits- und Sorgeverhältnisse ein. Wie aber unterscheiden sich die Möglichkeiten, sich vor einer Infektion mit dem Virus und einer schweren Erkrankung zu schützen? Welchen Logiken folgen Präventionsmaßnahmen und welche Konsequenzen haben sie? Mit den Klammern »Rationalitäten« und »Geschichten« untersuchen die interdisziplinären Beiträge das pandemische Geschehen und damit verbundene Präventionsmaßnahmen. Mit ihrer Fokussierung auf Fragen zur politischen Steuerung sowie der Berücksichtigung von subjektiven Narrationen und historischen Reflexionen machen sie Ambivalenzen und Spannungen sichtbar.
Susanne Levy
None
Fat-Referenced MRI
The amount and distribution of adipose and lean tissues has been shown to be predictive of mortality and morbidity in metabolic disease. Traditionally these risks are assessed by anthropometric measurements based on weight, length, girths or the body mass index (BMI). These measurements are predictive of risks on a population level, where a too low or a too high BMI indicates an increased risk of both mortality and morbidity. However, today a large part of the world’s population belongs to a group with an elevated risk according to BMI, many of which will live long and healthy lives. Thus, better instruments are needed to properly direct health-care resources to those who need it the most....
Artificial Intelligence in Medical Imaging
This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relat...
Clustering Algorithms
Shows how Galileo, Newton, and Einstein tried to explain gravity. Discusses the concept of microgravity and NASA's research on gravity and microgravity.
Benn's Media
None
Geospatial Health Data
Geospatial health data are essential to inform public health and policy. These data can be used to quantify disease burden, understand geographic and temporal patterns, identify risk factors, and measure inequalities. Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny describes spatial and spatio-temporal statistical methods and visualization techniques to analyze georeferenced health data in R. The book covers the following topics: Manipulate and transform point, areal, and raster data, Bayesian hierarchical models for disease mapping using areal and geostatistical data, Fit and interpret spatial and spatio-temporal models with the Integrated Nested Laplace Approximati...
The Lumbar Spine
The official publication of the International Society for the Study of the Lumbar Spine, this volume is the most authoritative and up-to-date reference on the lumbar spine. This edition provides more balance between basic science and clinical material and has been completely reorganized for easy reference. New chapters cover gene therapy, outcomes assessment, and alternatives to traditional nonoperative treatment. The editors have also added chapters on preparation for surgery, surgical approaches, spinal instrumentation, and bone grafts. Chapters on specific disorders have a consistent structure—definition, natural history, physical examination, imaging, nonoperative treatment, operative treatment, postoperative management, results of surgery, and complications.
