| |  Online-Ressource |
|---|
| Verfasst von: | Palm, Viktoria [VerfasserIn]  |
|---|
| | Norajitra, Tobias [VerfasserIn] |
|---|
| | Stackelberg, Oyunbileg von [VerfasserIn] |
|---|
| | Heußel, Claus Peter [VerfasserIn] |
|---|
| | Skornitzke, Stephan [VerfasserIn] |
|---|
| | Weinheimer, Oliver [VerfasserIn] |
|---|
| | Kopytova, Taisiya G. [VerfasserIn] |
|---|
| | Klein, Andre [VerfasserIn] |
|---|
| | Almeida, Silvia D. [VerfasserIn] |
|---|
| | Baumgartner, Michael [VerfasserIn] |
|---|
| | Bounias, Dimitrios [VerfasserIn] |
|---|
| | Scherer, Jonas [VerfasserIn] |
|---|
| | Kades, Klaus [VerfasserIn] |
|---|
| | Gao, Hanno [VerfasserIn] |
|---|
| | Jaeger, Paul F. [VerfasserIn] |
|---|
| | Nolden, Marco [VerfasserIn] |
|---|
| | Tong, Elizabeth [VerfasserIn] |
|---|
| | Eckl, Kira [VerfasserIn] |
|---|
| | Nattenmüller, Johanna [VerfasserIn] |
|---|
| | Nonnenmacher, Tobias [VerfasserIn] |
|---|
| | Naas, Omar [VerfasserIn] |
|---|
| | Reuter, Julia [VerfasserIn] |
|---|
| | Bischoff, Arved Helge Hermann [VerfasserIn] |
|---|
| | Kroschke, Jonas [VerfasserIn] |
|---|
| | Rengier, Fabian [VerfasserIn] |
|---|
| | Schlamp, Kai [VerfasserIn] |
|---|
| | Debic, Manuel [VerfasserIn] |
|---|
| | Kauczor, Hans-Ulrich [VerfasserIn] |
|---|
| | Maier-Hein, Klaus H. [VerfasserIn] |
|---|
| | Wielpütz, Mark Oliver [VerfasserIn] |
|---|
| Titel: | AI-supported comprehensive detection and quantification of biomarkers of subclinical widespread diseases at chest CT for preventive medicine |
|---|
| Verf.angabe: | Viktoria Palm, Tobias Norajitra, Oyunbileg von Stackelberg, Claus P. Heussel, Stephan Skornitzke, Oliver Weinheimer, Taisiya Kopytova, Andre Klein, Silvia D. Almeida, Michael Baumgartner, Dimitrios Bounias, Jonas Scherer, Klaus Kades, Hanno Gao, Paul Jäger, Marco Nolden, Elizabeth Tong, Kira Eckl, Johanna Nattenmüller, Tobias Nonnenmacher, Omar Naas, Julia Reuter, Arved Bischoff, Jonas Kroschke, Fabian Rengier, Kai Schlamp, Manuel Debic, Hans-Ulrich Kauczor, Klaus Maier-Hein and Mark O. Wielpütz |
|---|
| E-Jahr: | 2022 |
|---|
| Jahr: | 29 October 2022 |
|---|
| Umfang: | 16 S. |
|---|
| Fussnoten: | Gesehen am 20.01.2023 |
|---|
| Titel Quelle: | Enthalten in: Healthcare |
|---|
| Ort Quelle: | Basel : MDPI, 2013 |
|---|
| Jahr Quelle: | 2022 |
|---|
| Band/Heft Quelle: | 10(2022), 11, Artikel-ID 2166, Seite 1-16 |
|---|
| ISSN Quelle: | 2227-9032 |
|---|
| Abstract: | Automated image analysis plays an increasing role in radiology in detecting and quantifying image features outside of the perception of human eyes. Common AI-based approaches address a single medical problem, although patients often present with multiple interacting, frequently subclinical medical conditions. A holistic imaging diagnostics tool based on artificial intelligence (AI) has the potential of providing an overview of multi-system comorbidities within a single workflow. An interdisciplinary, multicentric team of medical experts and computer scientists designed a pipeline, comprising AI-based tools for the automated detection, quantification and characterization of the most common pulmonary, metabolic, cardiovascular and musculoskeletal comorbidities in chest computed tomography (CT). To provide a comprehensive evaluation of each patient, a multidimensional workflow was established with algorithms operating synchronously on a decentralized Joined Imaging Platform (JIP). The results of each patient are transferred to a dedicated database and summarized as a structured report with reference to available reference values and annotated sample images of detected pathologies. Hence, this tool allows for the comprehensive, large-scale analysis of imaging-biomarkers of comorbidities in chest CT, first in science and then in clinical routine. Moreover, this tool accommodates the quantitative analysis and classification of each pathology, providing integral diagnostic and prognostic value, and subsequently leading to improved preventive patient care and further possibilities for future studies. |
|---|
| DOI: | doi:10.3390/healthcare10112166 |
|---|
| URL: | Volltext: https://doi.org/10.3390/healthcare10112166 |
|---|
| | Volltext: https://www.mdpi.com/2227-9032/10/11/2166 |
|---|
| | DOI: https://doi.org/10.3390/healthcare10112166 |
|---|
| Datenträger: | Online-Ressource |
|---|
| Sprache: | eng |
|---|
| Sach-SW: | artificial intelligence |
|---|
| | comorbidities |
|---|
| | computer assisted image analysis |
|---|
| | CT imaging postprocessing |
|---|
| | machine learning |
|---|
| | medical computing |
|---|
| | medical image processing |
|---|
| | preventive medicine |
|---|
| | radiomics |
|---|
| K10plus-PPN: | 1831605260 |
|---|
| Verknüpfungen: | → Zeitschrift |
|---|
| |
|
|---|
| | |
|---|
| Lokale URL UB: |  Zum Volltext |
|---|
AI-supported comprehensive detection and quantification of biomarkers of subclinical widespread diseases at chest CT for preventive medicine / Palm, Viktoria [VerfasserIn]; 29 October 2022 (Online-Ressource)
