By 2001, the profiles reflected the vast majority of the world's population of dental hygienists. Rate of change varied across the countries examined; however, the nature of the change overall was consistent, resulting in a continuing homogeneity in the profession worldwide. Observed trends, changes and persistent issues have implications for service accessibility and technical efficiency and should continue to be monitored.
By 1998 the profiles reflected the vast majority of the world's population of dental hygienists. While rate of change varied across the countries examined, the nature of the change tended to be consistent, resulting in a continuing homogeneity in the profession worldwide. Changes and emerging trends should continue to be monitored in terms of improved access to quality oral health services and technical efficiency in the provision of those services.
Artificial intelligence (AI) shows tremendous promise in the field of medical imaging, with recent breakthroughs applying deep‐learning models for data acquisition, classification problems, segmentation, image synthesis, and image reconstruction. With an eye towards clinical applications, we summarize the active field of deep‐learning‐based MR image reconstruction. We review the basic concepts of how deep‐learning algorithms aid in the transformation of raw k‐space data to image data, and specifically examine accelerated imaging and artifact suppression. Recent efforts in these areas show that deep‐learning‐based algorithms can match and, in some cases, eclipse conventional reconstruction methods in terms of image quality and computational efficiency across a host of clinical imaging applications, including musculoskeletal, abdominal, cardiac, and brain imaging. This article is an introductory overview aimed at clinical radiologists with no experience in deep‐learning‐based MR image reconstruction and should enable them to understand the basic concepts and current clinical applications of this rapidly growing area of research across multiple organ systems.
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