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[From conventional to cutting edge imaging in rheumatology]. / Von der konventionellen Bildgebung zum Cutting Edge Imaging in der Rheumatologie.
Simon, David; Kemenes, Stephan; Minopoulou, Ioanna; Kleyer, Arnd.
Afiliación
  • Simon D; Medizinische Klinik 3 - Rheumatologie und Immunologie, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsklinikum Erlangen, Ulmenweg 18, 91054, Erlangen, Deutschland. david.simon@uk-erlangen.de.
  • Kemenes S; Deutsches Zentrum Immuntherapie (DZI), Friedrich-Alexander-Universität Erlangen-Nürnberg und Universitätsklinikum Erlangen, Erlangen, Deutschland. david.simon@uk-erlangen.de.
  • Minopoulou I; Medizinische Klinik 3 - Rheumatologie und Immunologie, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsklinikum Erlangen, Ulmenweg 18, 91054, Erlangen, Deutschland.
  • Kleyer A; Deutsches Zentrum Immuntherapie (DZI), Friedrich-Alexander-Universität Erlangen-Nürnberg und Universitätsklinikum Erlangen, Erlangen, Deutschland.
Z Rheumatol ; 82(8): 666-671, 2023 Oct.
Article en De | MEDLINE | ID: mdl-37606726
ABSTRACT
Imaging instruments, such as conventional X­ray, ultrasound and magnetic resonance imaging (MRI) are now fully established and highly valued in the care of rheumatology patients. However, the information provided by these imaging modalities in their current form is of limited utility for the prognostic prediction of individual patient outcomes. This article illuminates an important part of the development of imaging and shows that the vision of personalized medicine is becoming increasingly more tangible due to the further development of high-resolution imaging techniques, molecular imaging and artificial intelligence.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Reumatología / Inteligencia Artificial Tipo de estudio: Prognostic_studies Límite: Humans Idioma: De Revista: Z Rheumatol Año: 2023 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Reumatología / Inteligencia Artificial Tipo de estudio: Prognostic_studies Límite: Humans Idioma: De Revista: Z Rheumatol Año: 2023 Tipo del documento: Article
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