Education
Studies of Medical Informatics at Heidelberg University and Heilbronn University of Applied Sciences
Habilitation in Medical Informatics at Private University for Health Sciences, Medical Informatics and Technology (UMIT, Hall in Tirol, Austria)
Scientific Career:
Deputy Director, Institute of Medical Informatics, Heidelberg University Hospital
apl. Professor for Medical Informatics at Heidelberg University
Medical Informatics Certificate of the German Society for Medical Informatics, Biometry and Epidemiology (GMDS) e.V. and the German Society for Informatics (GI) e.V.
Academic Honorary Positions
Since 2020 Member of Review Board Medicine of the German Research Association (DFG)
Since 2020 Editor in chief of the journal GMS Medizinische Informatik, Biometrie und Epidemiologie
Since 2012 Head of Working Group ‘Information Management in Medicine’ of the German Society for Biomedical Engineering (DGBMT)
2011- 2018 Representative of GMDS in the International Medical Informatics Association (IMIA)
Since 2011 member of the editorial board of the journal Methods of Information in Medicine
2006-2008 and 2010-2012 Elected Head of Medical Informatics board of GMDS, member of the board of GMDS
- Patient Participation in Research and Care
- Digitalisation in teaching
Cystic Fibrosis
- Benning NH, Knaup P, Rupp R. Measurement Performance of Activity Measurements with Newer Generation of Apple Watch in Wheelchair Users with Spinal Cord Injury. Methods Inf Med. 2021 Dec;60(S 02):e103-e110. doi: 10.1055/s-0041-1740236.
- Gietzelt M, Löpprich M, Karmen C, Knaup P, Ganzinger M. Models and Data Sources Used in Systems Medicine. A Systematic Literature Review. Methods Inf Med. 2016;55(2):107-13. doi: 10.3414/ME15-01-0151.
- Gietzelt M, Karmen C, Knaup-Gregori P, Ganzinger M. vivaGen - a survival data set generator for software testing. BMC Bioinformatics. 2020 Apr 29;21(1):167. doi: 10.1186/s12859-020-3478-x.
- Karmen C, Gietzelt M, Knaup-Gregori P, Ganzinger M. Methods for a similarity measure for clinical attributes based on survival data analysis. BMC Med Inform Decis Mak. 2019 Oct 21;19(1):195. doi: 10.1186/s12911-019-0917-6.
- Firnkorn D, Ganzinger M, Muley T, Thomas M, Knaup P. A Generic Data Harmonization Process for Cross-linked Research and Network Interaction. Construction and Application for the Lung Cancer Phenotype Database of the German Center for Lung Research. Methods Inf Med. 2015;54(5):455-60. doi: 10.3414/ME14-02-0030.
Dr. Urs Eisenmann | Technical Project lead | ||
Friedemann Ringwald | Data Scientist | ||
Niclas Hagen | Data Scientist |
Lung Research - Projects
1. Deep learning-based visualisation of perfusion defects to support MRI-based perfusion scoring of the lung in cystic fibrosis (CF-MRXAI)
Partner: Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany
Funding: DZL
The CF-MRXAI project will use Explainable Artificial Intelligence (XAI) methods to assist radiologists in CF scoring of perfusion MRIs to reduce intra- and inter-reader variability. The basic idea is to present the radiologist with visualisations generated by XAI in addition to the perfusion sequence to enable a more objective assessment of the perfusion score. In addition to the current case being scored, reference visualisations of similar cases with comparable, slightly less severe or slightly more severe perfusion deficits are presented. After the development of an intuitive software tool, it will be evaluated by radiological staff in order to draw conclusions on inter- and intra-reader variability.
2. Automated Quantification of Structural and Functional Abnormalities on Chest Magnetic Resonance Imaging in Cystic Fibrosis Based on Machine Learning
Partner: Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany
Funding: Vertex Innovation Award
In order to assist radiologists in scoring cystic fibrosis on MRI and to optimise the diagnostic process, a deep learning based approach will be used to perform a classification of the individual CF sub-scores. Our aim is to develop an automated computer-based scoring system that incorporates structural and functional lung abnormalities on chest MRI in CF patients of all ages. To this end, software will be developed to provide a rapid and user-independent objective quantitative measure of morpho-functional changes in the CF lung. In addition to automatic classification, the software will also provide intuitive decision support for human readers.
