Projects

​​​​​​​​​​​​​Our department is involved in various research projects that we carry out with different partners at the Medical Center, in Germany, and at​ the international level. You can find a selection below.​​​

Current projects:​​

KIMed (04/2025 –​​ 12/2027)

KIMed – Network for Artificial Intelligence in Medicine

Project Period: 04/2025 –​​ 12/2027
Funding: Saxon State Ministry for Science, Culture, and Tourism (SMWK), Saxon Development Bank (SAB)

Project Descriptio​n

KIMed is a Saxon network project aimed at establishing a sustainable cooperation structure in the field of AI-supported medicine. By pooling expertise, resources, and infrastructure, it reduces structural and organizational barriers to the implementation of AI solutions from research into practice. The main objectives are to create a public metadata directory for data sets, AI applications, and stakeholders. The project also develops a secure Research Environment, or Secure Processing Environment (SPE), in line with the requirements of the European Health Data Space, as well as demonstrators for the SPE. In addition, training and education programs, consulting services, and public outreach activities are provided.​

Team Members

​Project Partners

  • Institut für Medizinische Informatik, Statistik und Epidemiologie, Universität Leipzig, Maryam Khodaei Dolouei, Prof. Dr. Toralf Kirsten, M. Sc. Christina Lohr, Dr. Frank Meineke, Dr. Alexandr Uciteli, Prof. Markus Löffler 
  • LIFE – Leipziger Forschungszentrum für Zivilisationserkrankungen​, Universität Leipzig, ​Ute Batz-Zweynert, Dipl-Inf. Matthias Reusche, Ronald Speer, Mathias Rühle, Sara Volc, Cornelia Dolling, Julia Jesser, Anja Sorge, Svea Pasalk, Ronald Lohann ​
  • Innovation Center Computer Assisted Surgery (ICCAS​), Universität Leipzig, Johann Berger, Dr. Stefan Bohn, Prof. Thomas Neumuth ​
  • Institut für Medizinische Informatik und Biometrie (IMB), Technische Universität Dresden, Dr. Eveline Prochaska​​​
  • ​​Zentrum für Medizinische Informatik, Technische Unversität Dresden, Karolin Hofmann, Marcel Dije, Dr. Claudia-Silvia Heine, Dr. Kathrin Sobe, Prof. Martin Sedlmayr
  • Sächsisches Institut für Computational Intelligence und Machine Learning, Hochschule Mittweida, Dr. Marika Kaden, M.Sc. Ronny Schubert, Prof. Dr. Thomas Villmann 
  • Zentrum für Klinische Studien (ZKS), Universität Leipzig, Dr. Wolf Oehrl 

LINCARE (01/2025 – 12/2027)

LINCARE – Interventional Care Research in Vascular Medicine

Project Period: 01/2025 – 12/2027
Funding: Federal Joint Committee (GBA)

Project Descriptio​n​

Around 2 to 3 million people in Germany are affected by arteriosclerotic lower extremity ischemia, also known as peripheral arterial disease (PAD). Given this high prevalence and the approximately 48,000 amputations performed annually—about one-third of which are considered avoidable—there is an urgent need for action to address this widespread condition.

The LINCARE project team is investigating the current state of patient care for critical stages of peripheral arterial disease (PAD). To this end, extensive health and cost data from the past ten years across inpatient, outpatient, and rehabilitation settings are being utilized. These data are provided by the consortium partner, the Scientific Institute of the AOK (WIdO). In addition, data from over 80,000 PAD patients treated at German university hospitals are included.

Based on the collected data, the LINCARE project team analyzes which factors contribute to adverse outcomes, particularly amputations. Through this analysis, we develop models that predict the impact of specific therapeutic interventions on the course of treatment. The focus is on identifying clinical and structural factors that lead to adverse outcomes in PAD patients. These models will ultimately be used to forecast the effects of various therapeutic approaches on treatment outcomes and to derive evidence-based recommendations for action. The overall goal is to improve treatment outcomes for PAD patients and reduce the amputation rate.​

Team Members

​Project Partners

  • Leipzig University, Department of Angiology (Priv.-Doz. Dr. med. habil. Eva Freisinger)
  • Wissenschaftliches Institut der AOK WIdO, Berlin (Dipl.-Math- Christian Günster)
  • Leipzig University, Department of Angiology (Univ.- Prof. Dr. med. Dierk Scheinert)
  • German Society of Angiology e.V., Berlin (Prof. Dr. med. Wulf Ito)
  • German Vascular League e.V., Brühl (PD Dr. med. Christoph Kalka)

Come2Data (11/2024 – 11/2026)

Come2Data – Competence Center for Interdisciplinary Data Sciences

Project Period: 11/2024 – 11/2026
Funding: Federal Ministry of Research, Technology, and Space (BFTR) & European Union

Project Description

As a data competence center (DKZ), Come2Data pursues a Saxonian-regional approach to convey practice-oriented data competences primarily to science, but also to the areas of administration and interested public and, in the long term, to the economy. Come2Data brings together existi​​ng data science training and support services as well as expertise and commitment to research data management, the National Research Data Infrastructure (NFDI), high-performance computing and analysis methods for data-intensive interdisciplinary research applications such as artificial intelligence and data modelling. 

The diverse local, regional and national activities that exist in Saxony will be consolidated and synergized into a sustainable offering. The basis is a comprehensive training and support program in the fields of data integration, data management, dat​​a analysis and data publication. Come2Data creates an open research, support, networking and learning center across all Saxon locations in order to make the consolidated training, support and knowledge offering available to researchers, teachers and learners as well as to the public via a central virtual platform.

Team M​embers

Project Partners

  • Center for Information Services and High-Performance Computing at Technische Universität Dresden
  • Technische Universität Dresden (Prof. Dr. Lars Bernard)
  • Technische Universität Chemnitz (Dr. Ralph Müller-Pfefferkorn)
  • Saxon State and University Library Dresden (SLUB)
  • State Initiative “SaxFDM – Research Data Management in Saxony”
  • ScaDS.AI Dresden/Leipzig – Center for Scalable Data Analytics and Artificial Intelligence Dresden/Leipzig (Prof. Dr. Erhard Rahm, Matthias Täuschner, Dr. Mike Berger, Pia Voigt)
  • DRESDEN-concept e.V.
  • NFDI4Earth – NFDI Consortium Earth System Sciences

Resul​​ts

Early results in the Come2Data pilot project „AI in Medicine“:

  • Design and implementation of a prototype for a Trusted Research Environment (TRE) in collaboration with the University Computing Center (URZ) to enable secure computation for research involving sensitive patient data.
  • Organization of a Python training for medical professionals in collaboration with ScaDS.AI and EKFZ to gain hands-on experience in data analysis and AI applications.
  • Development of a domain-specific knowledge database for the collection of methods, resources, and best practices in medical data science.
  • Support for the MII Academy to promote data literacy.
  • Collection of domain-specific data problems, which are fed into the internal ticket system and processed by the helpdesk/experts.​

Referenc​​​es

NutriScoPe (11/2024 – 10/2025)

NutriScoPe – Automated diagnosis of malnutrition (nutritional scoring) in inpatients

Project Period: 11/2024 – 10/2025 
Funding: TMF e.V.

Project Description​​​​

NutriScoPe is a retrospective study examining the occurrence of malnutrition in patients across va​​​rious hospital departments and assessing the impact of systematic nutritional assessment and therapy on treatment outcomes.

Malnutrition affects up to 20–30% of hospitalized patients and is associated with longer hospital stays, higher costs, and i​ncreased mortality—particularly in patients with malignant diseases or chronic organ failure. To address these risks, a dedicated nutrition team was established at the University of Leipzig Medical Center (UKL) to provide targeted support to patients during their inpatient stay.

At UKL, patients at increased risk ​​of malnutrition are identified using a standardized procedure called Nutritional Risk Screening (NRS). Upon hospital admission, patients undergo an initial prescreening, and if abnormalities are detected, a main screening follows. Patients diagnosed with malnutrition then receive targeted nutritional therapy from the nutrition team. In contrast, no systematic NRS screening is currently implemented at Jena University Hospital (UKJ).

The study utilizes medical documentation data from both hospitals to analyze the prevalence of malnutrition, its distribution ac​​ross different clinical departments, and the effects of automated Nutritional Risk Screening on treatment success, length of hospital stay, and complications. Additionally, associations with age, sex, and primary diagnoses are examined to derive targeted measures for improving patient care.

Team Members

Proj​​ect Partners

R​​esults

The data analysis showed that not all patients with a positive prescreening underwent a subsequent ​main screening at UKL, representing a deviation from the NRS protocol. In clinical practice, the main screening was performed predominantly in patients with more severe disease courses.

Since the prescreening is based on self-assessment at hospital admission, some bias was expected. Notable mai​​n screenings were particularly observed in the field of visceral surgery, often in patients with malignant comorbidities.

A higher NRS score in the main screening was strongly correlated with poorer clinical outcomes. These patients showed incr​​eased mortality, longer hospital stays, and higher readmission rates.

The following figure illustrates the ke​​y results of the analysis to date (as of September 2025):



Figure 1: Number of patients who underwent nutritional risk screening (NRS screening) at UKL between 2017 and 2023. Illustration by the author.



Figure 2:
 Length of inpatient stay depending on NRS score grouping
in the main screening



Figure 3:
 Body mass index depending
on NRS score grouping
in the main screening



Publicat​ions

  • Pirlich, M., Schütz, T., Norman, K., Gastell, S., Lübke, H. J., Bischoff, S. C., Bolder, U., Frieling, T., Güldenzoph, H., Hahn, K., Jauch, K. W., Schindler, K., Stein, J., Volkert, D., Weimann, A., Werner, H., Wolf, C., Zürcher, G., Bauer, P., & Lochs, H. (2006). The German hospital malnutrition study. Clinical nutrition (Edinburgh, Scotland), 25(4), 563–572. Read paper
  • Schuetz, P., Fehr, R., Baechli, V., Geiser, M., Deiss, M., Gomes, F., Kutz, A., Tribolet, P., Bregenzer, T., Braun, N., Hoess, C., Pavlicek, V., Schmid, S., Bilz, S., Sigrist, S., Brändle, M., Benz, C., Henzen, C., Mattmann, S., Thomann, R., … Mueller, B. (2019). Individualised nutritional support in medical inpatients at nutritional risk: a randomised clinical trial. Lancet (London, England), 393(10188), 2312–2321. Read paper

VICI – VTE (11/2024 – 04/2026)

VICI – VTE Venous Thromboembolism in Children and Adolescents

Project Period: 11/2024 – 04/2026
Funding: Budget-financed research

Project Description

The VICI-VTE project focuses on researchin​g venous thromboembolism (VTE) in children and adolescents under the age of 20 in Germany, with a particular emphasis on deep vein thrombosis (DVT) and pulmonary embolism (PE). VTE refers to blood clots that block the blood flow system in the veins and can be life-threatening in the worst case. Although VTE is rare in young people, it should be taken especially seriously as it often indicates particular risk factors or underlying conditions.

The project aims to comprehensively analyze the frequency, risk factors, and comorbidities of VTE to improve treatment outcomes ​​and therapies. To this end, inpatient case numbers from the Federal Statistical Office from 2021 to 2023 are being evaluated to identify differences by gender and age group. The project focuses particularly on in-hospital mortality from pulmonary embolism, complications such as cor pulmonale, and various treatment methods, including systemic thrombolysis and endoluminal procedures. 

Thus, the project provides important insights into the current care situation and supports the targeted optimiza​tion of VTE treatment for children and adolescents.

Team ​​Member


Project Partne​r

University of Leipzig Medical Center, Department of Angiology (Priv.-Doz. Dr. med. habil. Eva Freisinger)

MASLD (09/2024 – 04/2026)

MASLD – Implementation of the guideline-recommended screening algorithm for advanced liver fibrosis in patients with fatty liver disease due to metabolic dysfunction

Project Duration: 09/2024 – 04/2026 
Funding: Budget-financed research

Project Description

Metabolic dysfunction-associated steatotic liver disease (MASLD) is one of the most common causes of elevated liver function values in Western countries, affecting up to a quarter of the adult population and posing major challenges for the healthcare system. However, only a small proportion of those affected develop liver fibrosis or cirrhosis as the disease progresses, making it necessary to specifically identify patients at risk. 

​The project aims to evaluate the effectiveness of a simple screening algorithm recommended by the international professional society for the early detection of severe liver damage in MASLD​​​ patients in clinical practice. To this end, data from patients who have been referred to University of Leipzig Medical Center ​since 2014 will be evaluated retrospectively. The analysis will focus on the FIB-4 score, FAST score, and liver stiffness measurement as primary screening instruments, as well as on the patients’ subsequent clinical courses. 

This project therefore contributes to optimizing care and to the early identification of patients at high risk of severe liver disease.​

Team Member

Project Partners

  • University of Leipzig Medical Center, Medical Department II​, Division of Hepatology (Prof. Dr. Johannes Wiegand, Prof. Dr. Thomas Berg, Dr. Eva Messer)
  • University of Leipzig Medical Cente​r, Medical Department II​​, Division of Gastroenterology​ (Prof. Dr. Thomas Karlas)
  • Leipzig University, Center for Clinical Studies (Dr. David Petroff)
  • Leipzig University, Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics (Dr. Martin Federbusch)
  • University of Leipzig Medical Center, Data Integration Center​ (Dr. Thomas Wendt)

Leuko FTLD (09/2024 – 12/2026)

LeukoFTLD

Project Period: 09/2024 – 12/20216
Funding: Budget-financed research​

Project Description​​

The objective of the project is to investigate white matter alterations in the brain associated with frontotemporal lobar degeneration (FTLD) using magnetic resonance imaging (MRI) and deep l​earning techniques. White matter consists of nerve fibers that connect different regions of the brain; damage to this structure can lead to impairments in memory, language, or behavior.

The study focuses on identifying patterns linked to specific disease subtypes or phenotypes. This approach aims to detect​ potential overlaps or associations with leukodystrophies, which are rare genetic disorders affecting the white matter.

​Additionally, the project evaluates the applicability ​​​of the HeteroMRI method to classify MRI data from the FTLD consortium based on the presence of white matter abnormalities. The long-term goal is to develop novel diagnostic tools and deepen the pathophysiological understanding of this heterogeneous group of disorders.

Team members

​Project p​​artners

  • University of Leipzig Medical Center, Department of Neurology​ (Dr. med. Wolfgang Köhler)
  • Max Planck Institute for Human Cognitive and Brain Sciences (Prof. Dr. med. Dr. phil. Matthias Schroeter, Dr. Qiong Wu)
  • Department of Neurology, Medical University of Vienna, Austria (Dr. Markus Ponleitner)

Resu​lts​

N/A

Publicati​ons

  • ​​​Wu, Q., Ponleitner, M., Shekarchizadeh, N., Mueller, K., Zhang, X., Anderl-Straub, S., Danek, A., Diehl-Schmid, J., Fassbender, K., Fliessbach, K., Kornhuber, J., Jahn, H., Kassubeck, J., Lauer, M., Levin, J., Prudlo, J., Synofzik, M., Wiltfang, J., Weishaupt, J., FTLD Consortium Germany, Otto, M., Köhler, W., & Schroeter, M. L., Prevalence of white matter hyperintensities varies across the frontotemporal lobar degeneration & Alzheimer’s disease spectrum: A multicenter study. In submission.

Private AIM (04/2023 – 03/2027)

PrivateAIM — Privacy-Preserving Analytics In Medicine

Project Period: 04/2023–03/2027
Funding: Federal Ministry for Research, Technology and Space

Project Descript​​ion

PrivateAIM​ aims to develop a federated platform for the analysis of medical data within the framework of the M​edical Informatics Initiative (MII). The processing of patient data without their explicit consent in the data integration centers of MII partners is only permissible if the anonymity of the individuals concerned is preserved.

To address this challenge, PrivateAIM brings together expertise in federated data analysis and innovativ​​e data protection concepts. Currently available methods do not fully meet the requirements for data privacy and security. Therefore, PrivateAIM is working to extend these methods through multimodal data functionalities, comprehensive analytical capabilities, and a distributed infrastructure that ensures monitoring, control, and compliance with stringent data protection standards.

Through this innovative approach, PrivateAIM makes a significant contribution to medical research and aims to improve the quality of patient care in the long term.

​Team Mem​​​​​​bers

​Project Par​tners

  • University Medicine Berlin (Charité) (Prof. Dr. Fabian Prasser)
  • Helmholtz Center for Information Security (CISPA) (Prof. Dr. Mario Fritz)
  • German Cancer Research Center (DKFZ) (Dr. Ralf Omar Floca)
  • Eberhard Karls University of Tübingen (EKUT) (Prof. Dr. Nico Pfeifer)
  • Ludwig-Maximilians-University Munich (LMU) (Prof. Dr. Ulrich Mansmann)
  • TMF – Technologie- und Methodenplattform für die vernetzte medizinische Forschung e.V (Dr. Sebastian Claudius Semler)
  • Technical University of Munich (TUM) (Prof. Dr. Daniel Rückert)
  • University Hospital Erlangen (UKER) (Prof. Dr. Thomas Ganslandt)
  • University Medical Center Freiburg (UKFR) (Prof. Dr. Harald Binder)
  • University Hospital Heidelberg (UKHD) (Prof. Dr. Christoph Dieterich)
  • University of Cologne (UKK) (Prof. Dr. Oya Beyan)
  • University Hospital Tübingen (UKT) (Prof. Dr. Oliver Kohlbacher)
  • University Hospital Ulm (UKU) (Prof. Dr. Hans Kestler)
  • University Medical Center Mannheim, Heidelberg University (Prof. Dr. Martin Lablans)
  • University Medical Center Essen (UKE) (Dr. Michael Kamp)
  • University Medical Center Schleswig-Holstein (UKSH) (Prof. Dr. Björn Schreiweis)

​​Resu​lts: 


Figure 1: Overview over the FLAME architecture (​Source​)

The initial outcome is the development of a federated platform for the analysis of medical data, named FLAME. This platform can be broadly divided into a central component, the so-called Hub, and multiple decentralized components, referred to as Nodes. A user initiates an analysis via the Hub, which then generates a task and distributes it to all​​ participating Nodes. Each Node is part of the infrastructure of a hospital and has access to patient data, allowing these data to be used for analysis. The results are aggregated and sent back to the Hub, where they are made available to the analyst. Examples and further information regarding the FLAME platform can be found here.

The PrivateAIM project is c​​urrently ongoing and is expected to continue until 2027. Results will be updated as the project progresses.

​Publicatio​ns

  • Welten​​​ S, de Arruda Botelho Herr M, Hempel L, Hieber D, Placzek P, Graf M, Weber S, Neumann L, Jugl ​M, Tirpitz L, Kindermann K, Geisler S, Bonino da Silva Santos LO, Decker S, Pfeifer N, Kohlbacher O, Kirsten T A study on interoperability between two Personal Health Train infrastructures in leukodystrophy data analysis. Sci Data 11, 663, 2024. DOI:10.1038/s41597-024-03450-6.
  • Herr, M., Graf, M., Placzek, P., König, F., Bötte, F., Stickel, T., Hieber, D., Zimmermann, L., Slupina, M., Mohr, C., Biergans, S., Akgün, M., Pfeifer, N., & Kohlbacher, O. (2022)
  • Bringing the Algorithms to the Data - Secure Distributed Medical Analytics using the Personal Health Train (PHT-meDIC). arXiv (Cornell University). Read Paper​ 
  • Ziller, A., Trask, A., Lopardo, A., Szymkow, B., Wagner, B., Bluemke, E., Nounahon, J., Passerat-Palmbach, J., Prakash, K., Rose, N., Ryffel, T., Reza, Z. N., & Kaissis, G. (2021). PySyft: A Library for Easy Federated Learning. In Springer eBooks (pp. 111–139). Read Paper 
  • Kaissis, G., Ziller, A., Passerat-Palmbach, J., Ryffel, T., Usynin, D., Trask, A., Lima, I., Mancuso, J., Jungmann, F., Steinborn, M., Saleh, A., Makowski, M. R., Rueckert, D., & Braren, R. (2021). End-to-end privacy preserving deep learning on multi-institutional medical imaging. Nature Machine Intelligence, 3(6), 473–484. Read Paper
  • Prasser, F., Kohlmayer, F., Lautenschläger, R., & Kuhn, K. A. (2014). ARX - A comprehensive tool for anonymizing biomedical data. In AMIA Annual Symposium Proceedings (Vol. 2014, p. 984). American Medical Informatics Association. [Washington DC, 15.-19. November 2014: AMIA Annual Symposium, 2014] Read Paper
  • Wirth, F., Meurers, T., Johns, M., & Prasser, F. (2021). Privacy-preserving data sharing infrastructures for medical research: systematization and comparison. BMC Medical Informatics and Decision Making, 21(1). Read Paper 
  • Scherer, J., Nolden, M., Kleesiek, J., Metzger, J., Kades, K., Schneider, V., Bach, M., Sedlaczek, O., Bucher, A. M., Vogl, T. J., Grünwald, F., Kuhn, J., Hoffmann, R., Kotzerke, J., Bethge, O. T., Schimmöller, L., Antoch, G., Müller, H., Daul, A., . . . Maier-Hein, K. H. (2020). Joint Imaging Platform for Federated Clinical Data Analytics. JCO Clinical Cancer Informatics, 4, 1027–1038.

DiaClusT (01/2023 – 12/2026)

DiaClusT – Classification and Validation of Subphenotypes of Diabetes Mellitus Type 2

Project Period: 01/2023 – 12/2026
Funding: Budget-financed research

Project Description

Diabetes mellitus (DM) is a metabolic disease based on insulin resistance or deficiency, characterized by chronically elevated blood glucose levels and associated with an increased risk of severe concomitant and secondary diseases. There are two main forms, DM type 1 and type 2, the latter of which can have highly heterogeneous manifestations, so that a more refined classification can contribute to more individualized treatment of high-risk patients with a potentially worse inpatient outcome. With the help of DiaClusT, the published results of a cluster analysis by Ahlqvist et al. [2018, The lancet Diabetes & endocrinology] will be validated and verified using German cohorts. For this purpose, data from Leipzig University Hospital were requested via the newly created structural unit, the Data Integration Centre, and the Research Data Portal for Health.

Team ​​Member

Project Partne​r

  • ​Universität Leipzig, Prof. Dr. med. Thomas Ebert ​​
  • Hochschule Mittweida

rEsults

​The data is currently being processed. Evaluation and publications are planned for 2026.

MII-Academy (01/2023 – 12/2026)

MII-Academy

Project Period: 01/2023 – 12/2026
Funding: Budget-financed research

Project Description

​The Medical Informatics Initiative (MII) aims to make data from healthcare accessible to medical research. Medical questions are often developed by clinician scientists. The MII Academy aims to familiarise clinician scientists with the MII infrastructures, the services that have been set up and the established usage processes, as well as to provide them with further background knowledge and recommendations for action. The individual modules of the MII Academy are grouped into five subject areas. Didactically, the MII Academy is structured into small teaching and learning units, which are primarily conveyed via video formats. This enables clinician scientists to select the content according to their own needs, use it online multiple times regardless of time and location, and refer to it as needed. By the end of 2025, the MII Academy will contain more than 40 learning units.​

Team ​​Member

Project Partne​r

  • Institut für Medizinische Informatik, Statistik und Epidemiologie (IMISE)​, Universität Leipzig, Prof. Dr. Markus Löffler, Dr. Wolf Oehrl
  • LIFE Forschungszentrum, Universität Leipzig, Dr. Matthias Nüchter, Cornelia Dolling, Julia Jesser, Corinne Besenius​​​​

Results

Tag–White (12/2021 – 12/2026)

Tag–White - Bioinformatics Pipelines to Differentiate Leukodystrophy Subtypes

Project Period: 12/2021 –​​ 12/2026
Funding: Saxon State Ministry for Science, Culture, and Tourism (SMWK), Saxon Development Bank (SAB)

Project Description

Leukodystrophies are a group of rare diseases consisting of approx. 50-60 subtypes that differ in their symptoms and thus in definition and therapy. Differentiation is often reserved for medical experts only and requires genetic determination of the patient's genome. The project will set up analysis pipelines that integrate the genetic variants with private and publicly available annotations to support the medical experts at the Myelin Center at Leipzig University Medical Center in narrowing down subtypes in new leukodystrophy patients. Moreover, our recently developed deep learning method will be employed to analyze brain MRI images for distinguishing leukodystrophy patients from other differential diagnoses. The work takes place in close cooperation with international partners in Barcelona (Prof. A. Pujol, IDBELL) and Montreal (Prof. G. Bernard, McGill Univ.).

Team ​​Member

Project Partne​r

completed projects:

go to the overview page​​


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