Completed Projects

Project D​escription

The PATH project aims to unlock the full potential of digital health data for both individual care and public-health research.​ To achieve this, a GDPR-compliant, user-friendly platform is being develope​d to link personal health records with data from everyday devices such as smartwatches, home tests, and health apps.

At its core, the project focuses on creating secure infrastructures, known as “data hubs,” that integrate medically documented data with self-tracked health information—always under the full control of the individual user. A graphical dashboard provides users with a clear and accessible overview of t​​heir health data, empowering them to decide independently if and how their data is shared—whether for personal health benefits (primary use) or for research purposes (secondary use).

Innovation Through D​​igital Inte​​​gration

PATH establishes, for the first time, a bridge between traditional clinical data and personally generated health data. T​​his integration opens new possibilities for:

The platform is fully GDPR-compliant and offers open-source modules to manage consent, control data usage​​, and ensure transparency. Particular emphasis is placed on meaningful and traceable consent, building trust and enabling active participation by individuals.

Scientifically Grounded and Practically Tested

PATH is based on the hypothesis t​​hat the willingness to share personal health data largely depends on whether individuals retain control and are offered clear, meaningful ways to manage their data. To test this hypothesis, the developed solutions are validated through real-world clinical case studies involving patients with diabetes and mental health conditions.

The Medical Data Science dep​artment of the Faculty of Medicine at Leipzig University is responsible for one sub-project and is dedicated to developing a digital solution for managing consents.

​O​​utlook

In an evolving healthcare landscape, PATH creates an infrastructure that enables individuals to use their health da​ta safely, transparently, and meaningfully—for their own benefit and for the common good. The project makes a key contribution to the development of a European Health Data Space and to the responsible digital transformation of medicine.

Team members

​Project Partners

Resul​​ts

We have contributed to the novel Standard Health Consent (SHC) and worked on the its implementation r​​​​esulting in different software components. The figure below shows the high-level system architecture. The SHC Connect is a module that can be included in third party apps to capture app-specific consent. The SHC Connect points to the SHC Service managing and persisting all captured consent information.​

Public​ations

Project Descrip​​tion

The HeteroMRI project originated within the LEUKO-Expert project​ and continues as part of the Tag-White project. Magnetic Resonance Imaging (MRI) is widely used to analyze white matter abnormalities in the human brain. However, MRI data often vary considerably between scanners and acquisition protocols. This variability (heterogeneity) poses major challenges for automated image analysis and for training machine learning models, especially when data are limited—as is often the case in rare diseases.

This project addresses this issue by developing a deep learning method capable of robustly classifying white matter abnormaliti​​es across multi-scanner brain MRI datasets. The method has the potential to be trained and evaluated for classifying diseases that cause white matter changes in the brain, such as in leukodystrophies and multiple sclerosis.

To address this issue, the project ​developed HeteroMRI, a deep learning method designed to robustly classify white matter abnormalities across MRI data from multiple scanners. The goal is to enable reliable automated analysis of diseases that affect white matter, such as leukodystrophies and multiple sclerosis, while minimizing the influence of scanner-specific differences.

Team members

​Project Partners

Res​ults

Following the project’s aim, the team developed and evaluated HeteroMRI as a deep learning method for classifying MRI scans based on white matter abnormalities in the brain. Initiall​y, the method was tested for distinguishing between MRIs with and without white matter abnormalities.

HeteroMRI demonstrates that robust classification of white matter abnormalities is feasible even in heterogeneou​​​s, multi-scanner MRI data—without requiring additional preprocessing or harmonization methods. The approach shows a high degree of scanner and protocol independence and proves to be generalizable to previously unseen MRI acquisition settings.

The full implementation has been released as open-source software to promote transparency, reproducibility, an​​d further research.

Publicati​ons: 

Project Descriptio​​n

The use of medical MRI data for research purposes entails data protection risks, as patients can be re-identified through facial recognition. Advances in scan resolution and facial recognition software increase this risk, so the removal of sensitive metadata alone is insufficient. Defacing algorithms remove facial features but are prone to errors and may occasionally leave certain features identifiable. The project develops machine learning models that evaluate the effectiveness of defacing by detecting similarities between pre- and post-defaced scans. This approach allows MRI scans to be automatically classified as accepted or rejected, thereby preserving patients' privacy.​

Team Members​

• Maryam Khodaei Dolouei (maryam.khodaei@medizin.uni-leipzig.de)
• Dr. Sina Sadeghi (sina.sadeghi@medizin.uni-leipzig.de)​
• Prof. Dr. Toralf Kirsten (toralf.kirsten@medizin.uni-leipzig.de​)
  

Project Descriptio​​n

Computed tomography (CT) is used for diagnosis, staging, and treatment planning of liver diseases, where precise delineation of the liver and tumors is clinically critical. In this project, deep learning models for segmentation are first developed and evaluated in a centralized setup. To leverage larger, heterogeneous datasets from multiple centers, the models are then implemented in a federated learning setting. This allows training across institutions without sharing patient data. Training is conducted securely using the Personal Health Train (PHT) framework, enabling effective use of distributed data, preserving data privacy, and supporting the development of robust, clinically relevant models.

Team Members

National Research Data Infrastructure for Personal Health (NFDI4Health)​​

Project Descriptio​n

There is a large number of clinical trials, epidemiological studies and other medical research projects each year which collect data about probands and patients, their diagnoses and therapies, and the​ir environment according to a relevant research question. While the outcome of these research projects is often published, there is, however, no common way to publish metadata and data. Sometimes, metadata and data are not managed by a common format, such as Object Data Model or other available standards. Therefore, such data are often not findable, accessible, interoperable, and reusable. These FAIR criteria are immanent important making the research outcome transparent and reusable which is beneficial for medical research.

​The goal of NFDI4Health is overcom​​e these burdens by designing and implementing a concept for a federated research data infrastructure for personal health data. This work includes a common data structure for all this data, the federated data infrastructure itself, data protected / data privacy, and distributed analysis infrastructures allowing to include and compute the distributed data. We will show the functionality of all these aspects using a series of use cases.

Team memb​ers

Results and Public​​ations

Project Descriptio​​n

Rare diseases (RD) are individually rare but affect more than 30 million people across the EU. In Germany and the EU, specialized centers have been established to ensure appropriate care. However, delays in diagnosis are common, as physicians often lack experience with rare diseases. Providing targeted support to physicians in referrin​​g patients to specialized centers is therefore crucial to improve diagnostic pathways.

The PILEA proj​ect builds on the previous Leuko-Expert project (10/2020–02/2024) and pilots the LeukoExpert app – a digital support system designed to assist neurologists in decision-making for leukodystrophies. Its aim is to explore the foundation for a sustainable and scalable use in clinical practice.

For the first time, the app is being tested under real-world conditions at MVZ Mittweida, a medical care center with an affiliated n​​etwork of practices in Central Saxony. The study focuses on systematically examining technical, organizational, and legal aspects to capture the implementation process and to enable long-term integration into everyday clinical workflows.

The scientific support provided by IFDT Leipzig, Leipzig University, and University of Leipzig Medical Center is intended​​ to deliver additional insights that will contribute to the further development of the application and make it transferable to other digital solutions in the field of rare diseases.

Team Members

​Project Partners:

Resu​lts

The app was successfully integrated into the practice management system of the participating practice network, demonstrating the feasibility of embedding digital tools into existing clinical workflows. Due t​o legal requirements, the AI-based decision support component remained inactive during the pilot phase. While this temporarily limited the full functionality of the app, valuable insights were gained regarding user engagement and workflow integration.

The seamless technical integration was well received by participating neurologists. Feedback highlighted the importance of AI functionality for enhancing motivation and perceived efficiency, indicating that future implementation with the active AI component could significantly increase adoption and clinical impact. Although some users initially perceived the app as time-consuming, objective usage data showed efficient interaction, suggesting that perceptions of time pressure in routine practice may influence engagement.

Participation was also naturally limited by t​​he rarity of leukodystrophies and the short pilot duration of three months. Overall, the findings em​​phasize that careful design and integration of digital tools can support clinical workflows while minimizing disruption, and they provide clear guidance for optimizing future deployment to maximize usability, engagement, and clinical benefit.

A detailed summary of the study results is av​ailable here ​(German version).

Project Description

FAIR Data Spaces aims to create a shared, cloud-based data environment that connects science and industry. By integrating the European Gaia-X data infrastructure with Germany’s National Research Data Infrastructure (NFDI), the project establishes a secure, federated framework f​​​​or data exchange and use in line with the FAIR principles—findable, accessible, interoperable, and reusable.

Launched in 2021 and funded by t​he German Federal Ministry of Education and Research (BMBF), the project is driven by a broad consortium of research institutions, universities, and industry partners. It also supports Europe’s broader data strategy through close collaboration with Gaia-X and the EOSC Association.

Key objectives include developing a joint roadmap for Gaia-X and NFDI, clarifying ethical and legal requir​​ements for data sharing, and establishing a unified technical foundation. Several demonstrators illustrate practical applications:

FAIR Data Spaces demo​​nstrates how a secure, interoperable data environment can strengthen both scientific innovation and industrial applications.

Team membe​rs

​​Results

Publications within the FAIR DS Projects can be found on the FAIR Data Spaces Community of Zenodo​ here.​

Project Description

FAIR Data Spaces aims to create a shared, cloud-based data environment that connects science and industry. By integrating the European Gaia-X data infrastructure with Germany’s National Research Data Infrastructure (NFDI), the project establishes a secure, federated framework f​​​​or data exchange and use in line with the FAIR principles—findable, accessible, interoperable, and reusable.

Launched in 2021 and funded by t​he German Federal Ministry of Education and Research (BMBF), the project is driven by a broad consortium of research institutions, universities, and industry partners. It also supports Europe’s broader data strategy through close collaboration with Gaia-X and the EOSC Association.

Key objectives include developing a joint roadmap for Gaia-X and NFDI, clarifying ethical and legal requir​​ements for data sharing, and establishing a unified technical foundation. Several demonstrators illustrate practical applications:

FAIR Data Spaces demo​​nstrates how a secure, interoperable data environment can strengthen both scientific innovation and industrial applications.

Team membe​rs

​​Results

Publications within the FAIR DS Projects can be found on the FAIR Data Spaces Community of Zenodo​ here.​

Proj​ect Description​​

​​The SMITH consortium (Smart Medical Technology for Healthcare) aims to sustainably enhance patient care by improving the use of clinical data. To achieve this, healthcare data generated at various university hospitals are systematically processed, harmonized, and made available for defined research and analysis projects.

At the core of these efforts are the Data Integration Centers, established as new organizational units at the university medical sites. They serve as the central technological interface: collecting and structuring routine clinical data, converting them into standardized formats, and enabling privacy-compliant, cross-site use in medical research. At the same time, they coordinate agreements among the participating university hospitals to establish common standards for data formats, descriptive systems, and access ​​regulations. All data usage is based on the informed consent of patients, who make a significant contribution to advancing medical care by allowing their data to be used.

Within the SMITH consortium, a network of academic and university medical partners works to purposefully link researc​​h and healthcare. Clinical and methodological use cases are employed to test and demonstrate the added value of the IT solutions developed.

SMITH is one of four consortia funded by the Federal Ministry for Research, Technology and Aerospace (BMFTR) within the Medical Info​​rmatics Initiative (MII). During the expansion and extension phase from 2023 to 2026, the medical informatics infrastructure will be further developed, and collaboration with new partners—particularly in regional healthcare—will be strengthened. This expansion takes place in close cooperation with the Network University Medicine (NUM) to enable even broader data-driven health research in Germany.

Team member​

​Resu​​lts

Project Descrip​tion

The Leipzig Health Atlas (LHA) is a project funded by the German Federal Ministry of Education and Research (BMBF) that pro​​​vides an ontology-based online platform for publications, study data, as well as models and methods. Its aim is to offer researchers, clinicians, statisticians, and other interested parties a quality-assured data atlas to support research and reproducible analyses.

The LHA collects study data and publications, which are accessible upon request, and provides direct implementations for many models to facilitate practical use. The platform builds upon extensive data, methods, a​​​nd expertise from clinical and epidemiological studies, systems medicine research networks, bioinformatics projects, and ontological research initiatives led by partners in Leipzig.

Key objectives of the project include semantic dat​​​a integration, the development of ontologies and analytical services, the validation of source projects, and the integration of all content into a data-sharing platform. Particular focus is placed on the technical CMS platform, the metadata ontology, and the preparation and integration of exemplary project content into the LHA research database.

Team membe​r

Results and Publications​​​

Project Descript​​ion

PAREMIS was a BMBF funded project in which we designed a concept for model-based registries for clinical research. In particular, we developed a concept for rare diseases including Prader-Willi and Angelman Syndrome and others. PAREMIS was a cooperation between the Leipzig University (IMISE, LIFE), the University medical Center Leipzig (UZSE, IT-Management) and the Institute for Digital Information Technologies (IfDT).

Team member