Zusammenfassung
In search of prognostic markers for Covid-19 disease outcome, we propose a workflow that integrates short-termchanges in longitudinal CT imaging and laboratory data with disease outcome. For longitudinal imaging data analysis, we use deformable registration and quantify the change in status (healthy, ground glas opacity and consolidation) of the lung parenchyma at a voxel level.We identify lung tissue transformed toworse (pathological) status and increasing inflammatory parameters (i.e., CRP and IL-6) to be prognostic of extended hospital stay and worsened patient outcome. We apply the methodology to compute the predictive value of these features in the first and the second Covid-19 wave.
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© 2022 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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De Benetti, F. et al. (2022). Longitudinal Analysis of Disease Progression Using Image and Laboratory Data for Covid-19 Patients. In: Maier-Hein, K., Deserno, T.M., Handels, H., Maier, A., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2022. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-36932-3_39
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DOI: https://doi.org/10.1007/978-3-658-36932-3_39
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