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Federated network medicine for laboratory data in paediatric oncology
(Third Party Funds Group – Overall project)
Term: 1. November 2024 - 31. October 2026
Funding source: BMBF / Verbundprojekt
In FLabNet, we will harness the potential of algorithmic network biology and distributed machine learning to address two exemplary unmet needs in paediatric oncology: prediction ofchemotherapy side effects like neutropenic fever and early-stage detection of rare malignantdiseases such as myeloproliferative neoplasms. Based on >54 million laboratory test resultsfrom >500,000 patients from the Core Dataset of the German Medical Informatics Initiative (MII),we will create personalised networks, where nodes represent individual laboratory measurementsand edges encode patient-specific relationships. We hypothesise the emerging personal graph representations to capture the unique spectra and dependencies of the individual patients’ health anddisease characteristics. The networks will be used as signatures for label-efficient graph-based pre-dictors such as graph kernels; and we will provide privacy-preserving federated implementationsof our predictors that are fully interoperable with MII standards. To achieve its objectives, ourconsortium combines expertise in algorithmic systems biology (FAU), paediatric oncology (UKER),quantitative analysis of laboratory data (UKER), federated learning for biomedicine (Bitspark GmbH& FAU), and professional software development (Bitspark GmbH). These synergistic skill sets willenable us to combine laboratory diagnostics, computational systems medicine, and privacy-preserving machine learning, advancing the state of the art in quantitative analysis of laboratory data for precision medicine in paediatric oncology and beyond.
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High-resolution protein-protein interaction networks for biomedical research
(Third Party Funds Group – Overall project)
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Center for AI in Medicine
(Own Funds)
Term: since 1. May 2024
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Embedded AI for neuromuscular orthoses
(Third Party Funds Single)
Term: 1. March 2024 - 28. February 2027
Funding source: Bundesministerium für Bildung und Forschung (BMBF)
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Bridging the gap in ACL injury prevention with FAME: Field-based Athlete Motion Evaluation and simulation
(Third Party Funds Single)
Term: since 15. January 2024
Funding source: Deutsche Forschungsgemeinschaft (DFG)
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Untersuchung von In-situ-Gewebeschemata mittels volumetrischer Bildgebung und maschinellem Lernen für die Bildanalyse
(Third Party Funds Group – Sub project)
Overall project: Immun-Checkpoints der Kommunikation zwischen Darm und Gehirn bei entzündlichen und neurodegenerativen Erkrankungen (GB.com)
Term: 1. January 2024 - 31. December 2027
Funding source: DFG / Klinische Forschungsgruppe (KFO)
Entzündung beginnt vor allem in Geweben, und ihre Ausprägung und ihr Ausmaß werden weitgehend von diesen kontrolliert. Infiltrierende Immunzellen sind hochgradig plastisch und integrieren sowohl molekulare als auch biophysikalische Informationen aus ihrer Umgebung, die allesamt ihre Effektor-Funktionen sowie ihr weiteres Schicksal bestimmen. Mit dem Fortschritt hochdimensionaler OMICs-Ansätze haben wir begonnen, die zelluläre Heterogenität komplexer entzündlicher Infiltrate besser zu verstehen. Während jüngste analytische Ansätze zelluläre Interaktionen auf der Grundlage von Transkriptionsprofilen vorhersagen können, bleiben ihre tatsächlichen funktionellen Interaktionen und die Einwirkung ihrer mikroanatomischen Umgebung jedoch weiterhin spekulativ. Um die Beziehung zwischen Gewebestruktur, Architektur der Mikroumgebung und Zellfunktionen verstehen zu können, müssen multidimensionale Ex vivo-Zellanalysen durch quantitative In-situ-Bildgebung ergänzt werden. Neueste 2D-Hyperplex-Mikroskopieverfahren erlauben mittlerweile mehrere Parameter mit räumlichem Kontext zu erfassen. Mit diesen Techniken lässt sich jedoch die komplexe 3D-Architektur von strukturell vielschichtigen und kompartimentierten Geweben wie dem Darm oder dem Gehirn nicht adäquat abbilden. Gleichzeitig erfordert die Fülle von Daten, die durch Bildgebungs- und OMICs-Techniken erzeugt werden, nachfolgende integrative Analyseschritte, die bei einer manuellen Auswertung in der Regel nicht zu bewältigen sind. Um diese Lücken zu schließen, werden wir neue Mikroskopieanwendungen und quantitative, durch maschinelles Lernen unterstützte Analysemethoden für Gewebe entlang der Darm-Hirn-Achse mit hoher Granularität sowohl in 2D als auch in 3D entwickeln. Dieses explorative Vorhaben dient der Etablierung innovativer Methoden und wird dazu beitragen, räumliche Daten mit zellulären Profilen zu integrieren, um Organisationsschemata zu entschlüsseln, die Gesundheit und Krankheit zugrunde liegen.
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Federated virtual twins for privacy-preserving personalised outcome prediction of type 2 diabetes treatment
(Third Party Funds Single)
Term: 1. January 2024 - 31. December 2028
Funding source: EU - 9. Rahmenprogramm - Horizon Europe
URL: https://daibetes.eu/
Virtual twins have the potential to be used as prognostic tools in precision medicine to support individualized disease management. However, training these models requires large volumes of data from various sources, which is challenging due to privacy regulations like the General Data Protection Regulation (GDPR). Recently, privacy-preserving computational methods, such as federated learning, have emerged, offering a way to utilize extensive data effectively while protecting sensitive patient information.
In dAIbetes, our main medical objective is to provide personalized predictions of treatment outcomes for type 2 diabetes, a condition affecting 1 in 10 adults globally and leading to annual costs of approximately 893 billion EUR. Although healthcare professionals are improving at addressing diabetes risk factors like diet and exercise, there are currently no guidelines for predicting treatment outcomes tailored to individual patients.
dAIbetes brings together advaned expertise in federated learning, artificial intelligence, cybersecurity, diabetes data standardization, clinical validation, as well as in legal and ethical evaluation of applying advanced federated machine learning to personalized medicine. 13 Partners from 13 European countries and the US will jointly implement the project which is structured into 9 Work Packages (WP1-WP9). At FAU, we are working on WP3, i.e., the development of virtual twin apps for training of virtual twin models that will use data from type 2 diabetes patients.
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Automatisierte Kuratierung von in-vivo erfassten Zeitreihen
(Third Party Funds Single)
Term: 1. January 2024 - 31. December 2026
Funding source: Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie (StMWi) (seit 2018)
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Restoring hand function with neuromuscular restrictions using an intelligent neuroorthosis
(Third Party Funds Single)
Term: 1. December 2023 - 31. May 2026
Funding source: Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie (StMWi) (seit 2018)
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A Platform for Dynamic Exploration of the Cooperative Health Research in South Tyrol Study Data via Multi-Level Network Medicine
(Third Party Funds Single)
The Cooperative Health Research in South Tyrol (CHRIS) study offers a comprehensive overview of the health state of >13,000 adults in the middle and upper Val Venosta. It is the largest population-based molecular study in Italy with a longitudinal lookout to investigate the genetic and molecular basis of age-related common chronic conditions and their interaction with lifestyle and environment in the general population. In CHRIS, the combination of molecular profiling data, such as genomics and metabolomics, together with important baseline clinical and lifestyle data offers vast opportunities for understanding physiological changes that could lead to clinical complications or indicate the prevalence or early onset of diseases together with their molecular underpinnings.
Where disease-focused studies often have a clear hypothesis that dictates the necessary statistical analyses, population-based cohorts such as CHRIS are more versatile and allow both testing existing hypotheses as well as generating new hypotheses that arise from statistically significant associations of the available data. Ideally, this type of explorative analysis is open to biomedical researchers that do not necessarily have experience with data analysis or machine learning. Network-based approaches are ideally suited for studying heterogeneous biomedical data, giving rise to the field of network medicine. However, network medicine techniques have so far mainly been used in the context of studies focusing on individual diseases. Network-based platforms for the explorative analysis of population-based cohort data do not exist.
In DyHealthNet, we will close this gap and develop a network-based data analysis platform, which will allow to integrate heterogeneous data and support explorative data analytics over dynamically generated subsets of the CHRIS study data. To fully leverage the potential of the available multi-level data, the DyHealthNet platform combines (1) data integration using standardized medical information models (HL7 FHIR), (2) innovative index structures for scalable dynamic analysis, (3) machine learning, and (4) visual analytics. DyHealthNet will render the CHRIS population cohort data accessible for state-of-the-art privacy-preserving, network-based data analysis. DyHealthNet will hence enable mining of context-specific pathomechanisms for precision medicine, and will serve as a blueprint for dynamic explorative analysis of multi-level cohort data worldwide. To achieve these objectives, the DyHeathNet consortium combines expertise in population-based cohort studies (Fuchsberger) and in the development of complex algorithms for the analysis of molecular networks (Blumenthal), applied biomedical AI and software systems (List), and customized index structures for scalable data management (Gamper).
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Development of an innovative neurobandage with an integrated brain-computer interface for testing hand function
(Third Party Funds Single)
Term: 1. October 2023 - 31. October 2026
Funding source: Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie (StMWi) (seit 2018)
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Smart Wound Dressing incorporating Dye-based Sensors Monitoring of O2, pH and CO2 under the wound dressing and smart algorithms to assess the wound healing process
(Third Party Funds Single)
Term: 1. September 2023 - 31. July 2026
Funding source: Bayerische Forschungsstiftung
In Germany alone, the number of patients with chronic wound healing disorders is estimated at around 2.7 million. According to projections, the treatment of chronic wounds accounts for € 23 - 36 billion per year. Of the treatment costs for chronic wounds, 4.6 to 7.2 billion € alone are accounted for by the associated cost-intensive dressing materials. The aim of the SWODDYS project is to research the fundamentals for a new type of intelligent wound dressing for the treatment of acute and chronic wounds, which can monitor the energy-metabolic tissue and wound healing status individually for each patient and online by integrating fluorescent dye-based oxygen, pH and CO2 sensors.
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Teilprojekt A2
(Third Party Funds Group – Sub project)
Overall project: Quantitative diffusionsgewichtete MRT und Suszeptibilitätskartierung zur Charakterisierung der Gewebemikrostruktur
Term: 1. September 2023 - 31. August 2027
Funding source: DFG / Forschungsgruppe (FOR)
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Quantitative diffusionsgewichtete MRT und Suszeptibilitätskartierung zur Charakterisierung der Gewebemikrostruktur
(Third Party Funds Group – Sub project)
Overall project: FOR 5534: Schnelle Kartierung von quantitativen MR bio-Signaturen bei ultra-hohen Magnetfeldstärken
Term: 1. September 2023 - 31. August 2027
Funding source: DFG / Forschungsgruppe (FOR)
Dieses Projekt ist Teil der Forschungsgruppe (FOR) "Schnelle Kartierung von quantitativen MR bio-Signaturen bei ultrahohen Magnetfeldstärken". Es konzentriert sich auf die Erweiterung, Beschleunigung und Verbesserung der Diffusions- und quantitativen Suszeptibilitäts-Magnetresonanztomographie. Das Arbeitsprogramm ist in zwei Teile gegliedert. Im ersten Teil wird ein beschleunigtes Protokoll für die klinischen Projekte der FOR vorbereitet. Im zweiten Teil sollen eine weitere Beschleunigung sowie Qualitätsverbesserungen erreicht werden. Konkret werden wir eine lokal niedrigrangig regularisierte echoplanare Bildgebungssequenz für die diffusionsgewichtete Bildgebung implementieren. Sie nutzt Datenredundanzen bei Akquisitionen mit mehreren Diffusionskodierungen, um das Signal-Rausch-Verhältnis effektiv zu erhöhen und damit den Akquisitionsprozess zu beschleunigen. Die Sequenz wird im Wesentlichen beliebige Diffusionskodierungsmöglichkeiten ermöglichen (z.B. b-Tensor-Kodierung). In einem zweiten Schritt werden wir eine verschachtelte Mehrschuss-Version dieser Sequenz entwickeln, um Bildverzerrungen zu reduzieren, die bei der 7-Tesla echoplanaren Bildgebung störend sind. Für die quantitative Suszeptibilitätskartierung (QSM) werden wir eine Sequenz mit einer Stack-of-Stars-Aufnahmetrajektorie implementieren. Da die Magnitudenbilder von Gradientenechosequenzen, die zu unterschiedlichen Echozeiten akquiriert werden, Datenredundanzen aufweisen, die mit denen von diffusionskodierten Bildern vergleichbar sind, werden wir bei der Bildrekonstruktion ebenfalls eine lokale Regularisierung niedrigen Ranges verwenden. Die radialen Trajektorien dieser Sequenz sollten für eine unterabgetastete und damit beschleunigte Bildrekonstruktion gut geeignet sein. In einem zweiten Schritt werden wir die Fähigkeiten unserer Sequenz durch eine quasi-kontinuierliche Echozeit-Abtastung erweitern, bei dem jede Speiche ihre eigene optimierte Echozeit hat. Dies wird eine verbesserte Qualität der QSM ermöglichen, wenn Fett im Bild vorhanden ist, wie es häufig bei Muskeluntersuchungen und in der Brustbildgebung der Fall ist. Bezüglich der QSM-Rekonstruktion werden wir Verfahren des tiefen Lernens entwickeln, um eine qualitativ hochwertige Rekonstruktion mit einer geringeren Menge an Bilddaten als bei herkömmlichen Rekonstruktionsansätzen zu ermöglichen. Wir werden bestehende neuronale Netzwerke von niedrigeren Feldstärken auf 7 T anpassen und deren Fähigkeiten so erweitern, dass wir auch atemzyklusabhängige Feldkarten. Dieses Projekt wird parallele Sendemethoden (pTx) vom pTx-Projekt der FOR erhalten. Wir werden die entwickelten Sequenzen nach dem ersten Jahr an die klinischen Projekte der FOR liefern. Darüber hinaus werden wir wesentliche Auswerte- und Bildrekonstruktionsmethoden an die anderen Projekte der FOR transferieren.und quasi-kontinuierliche Echozeiten in die Rekonstruktion integrieren können.
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Medical Image Analysis with Normative Machine Learning
(Third Party Funds Single)
Term: 1. September 2023 - 30. September 2028
Funding source: Europäische Union (EU)
As one of the most important aspects of diagnosis, treatment planning, treatment delivery, and follow-up, medical imaging provides an unmatched ability to identify disease with high accuracy. As a result of its success, referrals for imaging examinations have increased significantly. However, medical imaging depends on interpretation by highly specialised clinical experts and is thus rarely available at the front-line-of-care, for patient triage, or for frequent follow-ups. Very often, excluding certain conditions or confirming physiological normality would be essential at many stages of the patient journey, to streamline referrals and relieve pressure on human experts who have limited capacity. Hence, there is a strong need for increased imaging with automated diagnostic support for clinicians, healthcare professionals, and caregivers.
Machine learning is expected to be an algorithmic panacea for diagnostic automation. However, despite significant advances such as Deep Learning with notable impact on real-world applications, robust confirmation of normality is still an unsolved problem, which cannot be addressed with established approaches.
Like clinical experts, machines should also be able to verify the absence of pathology by contrasting new images with their knowledge about healthy anatomy and expected physiological variability. Thus, the aim of this proposal is to develop normative representation learning as a new machine learning paradigm for medical imaging, providing patient-specific computational tools for robust confirmation of normality, image quality control, health screening, and prevention of disease before onset. We will do this by developing novel Deep Learning approaches that can learn without manual labels from healthy patient data only, applicable to cross-sectional, sequential, and multi-modal data. Resulting models will be able to extract clinically useful and actionable information as early and frequent as possible during patient journeys.
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Human Impedance control for Tailored Rehabilitation
(Third Party Funds Single)
Term: 3. July 2023 - 30. June 2026
Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
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Privacy-preserving analysis of distributed medical data
(Own Funds)
Term: since 1. July 2023
Recent legislative development, such as the European Health Data Space, expand access to anonymizied health data for various entities. While these advances offer opportunities for medical research and innovation, they also increase the risk of compromising individuals' privacy.
This project addresses the critical tension between the growing utility of health data and the need to protect individual privacy through organizational, infrastructural, and technical approaches. A key component of the technical solutions is privacy-enhancing technologies (PETs), such as secure multi-party computation and (local) differential privacy, which safeguard individuals' privacy while enabling the statistical analysis of aggregate data.
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AI4MDD: AI-Powered Prognosis of Treatment Response in Major Depression Disorder
(Third Party Funds Single)
Term: 1. July 2023 - 30. September 2026
Funding source: Industrie
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Teilvorhaben: Friedrich-Alexander-Universität Erlangen-Nürnberg
(Third Party Funds Group – Sub project)
Overall project: Entwicklung und Kontroller personalisierter Neurorehabilitation für die Hand durch virtuelles Feedbackgesteuert durch neuronale Signale
Term: 1. May 2023 - 4. April 2026
Funding source: BMBF / Verbundprojekt
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Dimensionality reduction for molecular data based on explanatory power of differential regulatory networks
(Third Party Funds Group – Overall project)
Term: 1. March 2023 - 28. February 2026
Funding source: Bundesministerium für Bildung und Forschung (BMBF)
URL: https://www.netmap.ai/
Rapid advances in single-cell RNA sequencing (scRNA-seq) technology are leading to ever-increasing dimensions of the generated molecular data, which complicates data analyses. In NetMap, new scalable and robust dimensionality reduction approaches for scRNA-seq data will be developed. To this end, dimensionality reduction will be integrated into a central task of the systems medicine analysis of scRNA-seq data: inference of gene regulatory networks (GRNs) and driver transcription factors based on cell expression profiles. Each resulting dimension will correspond to a driver GRN, and the coordinate of a cell in this low-dimensional representation will quantify the extent to which the particular driver GRN explains the cell's gene expression profile. These new methods will be implemented as a user-friendly software platform for exploratory expert-in-the-loop analysis and in silico prediction of drug repurposing candidates.
As a case study, we will investigate CD4 helper T cell exhaustion, a potential limiting factor in immunotherapy. NetMap's strategy consists of (1) analyzing phenotypic heterogeneity of depleted CD4 T cells, (2) identifying transcriptional mechanisms that control this heterogeneity, (3) amplifying/eliminating specific subsets and testing their functional impact. This will allow the development of an atlas of the gene regulatory landscape of depleted CD4 T cells, while the in vivo testing of key regulatory transcription factors will help demonstrate the power of the developed methods and allow evaluation and improvement of predictions.
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Testing and Experimentation Facility for Health AI and Robotics
(Third Party Funds Group – Sub project)
Overall project: Testing and Experimentation Facility for Health AI and Robotics
Term: 1. January 2023 - 31. December 2027
Funding source: Europäische Union (EU)
URL: https://www.tefhealth.eu/
The EU project TEF-Health aims to test and validate innovative artificial intelligence (AI) and robotics solutions for the healthcare sector and accelerate their path to market. It is led by Prof. Petra Ritter, who heads the Brain Simulation Section at the Berlin Institute of Health at Charité (BIH) and at the Department of Neurology and Experimental Neurology of Charité – Universitätsmedizin Berlin. The MaD Lab of the FAU is one of the 51 participating project partners from nine European countries.
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Maschinelles Lernen und Datenanalyse für heterogene, artübergreifende Daten (X02)
(Third Party Funds Group – Sub project)
Overall project: SFB 1540: Erforschung der Mechanik des Gehirns (EBM): Verständnis, Engineering und Nutzung mechanischer Eigenschaften und Signale in der Entwicklung, Physiologie und Pathologie des zentralen Nervensystems
Term: 1. January 2023 - 31. December 2026
Funding source: DFG / Sonderforschungsbereich (SFB)
X02 nutzt die in EBM erzeugten Bilddaten und mechanischen Messungen, um Deep Learning-Methoden zu entwickeln, die Wissen über Spezies hinweg transferieren. In silico und in vitro Analysen werden deutlich spezifischere Daten liefern als in vivo Experimente, insbesondere für menschliches Gewebe. Um hier Erkenntnisse aus datenreichen Experimenten zu nutzen, werden wir Transfer Learning-Algorithmen für heterogene Daten entwickeln. So kann maschinelles Lernen auch unter stark datenlimitierten Bedingungen nutzbar gemacht werden. Ziel ist es, ein holistisches Verständnis von Bilddaten und mechanischen Messungen über Artgrenzen hinweg zu ermöglichen.
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End-to-End Deep Learning Image Reconstruction and Pathology Detection
(Third Party Funds Single)
Term: 1. January 2023 - 31. December 2025
Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
The majority of diagnostic medicalimaging pipelines follow the same principles: raw measurement data is acquiredby scanner hardware, processed by image reconstruction algorithms, and thenevaluated for pathology by human radiology experts. Under this paradigm, every stephas traditionally been optimized to generate images that are visually pleasingand easy to interpret for human experts. However, raw sensor information thatcould maximize patient-specific diagnostic information may get lost in thisprocess. This problem is amplified by recent developments in machine
learning for medical imaging. Machine learning has been used successfully inall steps of the diagnostic imaging pipeline: from the design of dataacquisition to image reconstruction, to computer-aided diagnosis. So far, thesedevelopments have been disjointed from each other. In this project, we willfuse machine learning for image reconstruction and for image-based diseaselocalization, thus providing an end-to-end learnable image reconstruction andjoint pathology detection approach that operates directly on raw measurementdata. Our hypothesis is that this combination can maximize diagnostic accuracywhile providing optimal images for both human experts and diagnostic machinelearning models.
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Digital health application for the therapy of incontinence patients
(Third Party Funds Single)
Term: since 1. January 2023
Funding source: Bundesministerium für Wirtschaft und Klimaschutz (BMWK)
The goal of this project is the development of an application for supporting the physical rehabilitation therapy of prostatectomy and incontinence patients in planning and execution. An AI-driven algorithm for automatic planning will be developed and extended by a machine learning approach for live exercise execution feedback. The developed application will be clinically evaluated regarding effectiveness and therapy benefit.
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Erarbeitung der Studienkonzeption, Medizinisch wissenschaftlich beratende Funktion
(Third Party Funds Group – Sub project)
Overall project: Digitale Gesundheitsanwendung zur Therapie von Inkontinenzpatienten
Term: 1. January 2023 - 31. December 2024
Funding source: Bundesministerium für Wirtschaft und Klimaschutz (BMWK)
The goal of this project is the development of an application for supporting physical rehabilitation therapy in planning and execution. An AI-driven algorithm for automatic planning will be developed and extended by a machine learning approach for live exercise execution feedback. The developed application will be clinically evaluated regarding effectiveness and therapy benefit.
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DIAMond - diabetes type 1 management with personalized recommendation using data science
(Third Party Funds Single)
Term: 1. September 2022 - 20. January 2025
Funding source: Deutscher Akademischer Austauschdienst (DAAD)
Diabetes is an overwhelming disease, directly influencing more than 422 million people worldwide who are living with this disease. Type 1 diabetes is the most severe form of the disease. The management of type 1 diabetes is especially difficult for young children and adolescents. Additionally, the most feared complication of type 1 diabetes – hypoglycemia – might occur after several hours, for example, during the night.
The DIAmond project will address the personalized and better management of type 1 diabetes using data science and machine learning to gain insights into the problem of hypoglycemia. Data from the DIAcamp study is used to advance personalized treatment recommendations. In the DIAcamp study, children participated for one week. They were equipped with a continuous glucose sensor and a wearable device for monitoring heart rate, accelerometry, and further physiological parameters during their participation. Physicians and carers from the DIAcamp study documented insulin doses, carbohydrate intake, and time and type of activity. Within the DIAmond project, novel machine learning algorithms will determine the probability of hypoglycemia. Exploratory analysis of the physiological time series will result in the most predictive features, building the base for personalized treatment recommendations.
This project is a joint project with the Department of Computer Science, ETH Zurich, Switzerland.
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Applied Data Science in Digital Psychology
(Third Party Funds Single)
Term: 1. September 2022 - 31. August 2026
Funding source: Bayerisches Staatsministerium für Wissenschaft und Kunst (StMWK) (seit 2018)
University education in psychology, medical technology and computer science currently focuses on teaching basic methods and knowledge with little involvement of other disciplines. Increasing digitalization and the ever more rapid spread of digital technologies, such as wearable sensors, smartphone apps, and artificial intelligence, also in the health sector, offer a wide range of opportunities to address psychological issues from new and interdisciplinary perspectives. However, this requires close cooperation between the disciplines of psychology and technical disciplines such as medical technology and computer science to enable the necessary knowledge transfer. Especially in these disciplines, there is a considerable need for innovative and interdisciplinary teaching concepts and research projects that teach the adequate use of digital technologies and explore the application of these technologies to relevant issues in order to enable better care in the treatment of people with mental disorders.
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AI-Powered Manipulation System for Advanced Robotic Service, Manufacturing and Prosthetics
(Third Party Funds Group – Sub project)
Overall project: AI-Powered Manipulation System for Advanced Robotic Service, Manufacturing and Prosthetics
Term: 1. September 2022 - 28. February 2026
Funding source: Europäische Union (EU)
URL: https://intelliman-project.eu/
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Multimodal Machine Learning for Decision Support Systems
(Third Party Funds Single)
Term: since 1. June 2022
Funding source: Siemens AG
The project aims to identify areas where advanced data analysis and processing methods can be applied to aspects of computer tomography (CT) technology. Furthermore included is the implementation and validation of said methods.
In this project, we analyze machine and customer data sent by thousands of high-end medical devices every day.
Since potentially relevant Information is often presented in different modalities, the optimal application of fusion techniques is a key factor when extracting insights.
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Biomechanical Assessment of Big Wave Surfing
(Third Party Funds Single)
Term: 1. June 2022 - 31. May 2025
Funding source: Siemens AG
The goal of this project is to develop experimental approaches and simulation methods for biomechanical assessment of big wave surfing. This goal will be addressed in collaboration with Sebastian Steudtner and Siemens Healthineers. The methods include, but are not limited to, biomechanical movement analysis, musculoskeletal simulation, and sensor fusion.
The focus of the research activities will be centered on:
- Development of a measurement approach for biomechanical assessment of big wave surfing
- Development of efficient and accurate data processing combining inputs from several sensor systems
- Design of a biomechanical simulation model that reflects the situation during surfing
- Analysis of biomechanical measurements and simulation outcomes to provice advice for big wave surfer to improve performance.
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Biomechanical Assessment of Big Wave Surfing
(Third Party Funds Single)
Term: since 1. June 2022
Funding source: Siemens AG
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Machine Learning for CT-Detector Production
(Third Party Funds Single)
Term: since 1. April 2022
Funding source: Industrie
The main goal of this project is to improve the detector manufacturing for computer tomography (CT). Therefore, data is gathered during the production of a CT-detector. This data is analysed and used to develop and train a machine learning system which should find the best composition of a CT-detector. In the future, the system will be integrated into the process of CT-detector manufacturing which, in result, should further improve the image quality and the production process of CT-devices. Especially, the warehouse utilization and the first-pass-yield should be enhaced. The project is realized in cooperation with Siemens Healthineers Frochheim.
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Individual Performance Prediction Using Musculoskeletal Modeling
(Third Party Funds Single)
Term: 1. February 2022 - 31. January 2025
Funding source: Industrie
Biomechanical modeling and simulation are performed to analyze and understand human motion and performance. One objective is to reconstruct human motion from measurement data e.g. to assess the individual performance of athletes and customers. Another objective is to synthesize realistic human motion to study human-production interaction. The reconstruction (a) and synthesis of human motion (b) will be addressed in this research position. New algorithms using biomechanical simulation of musculoskeletal models will be developed to enable innovative applications and services for Adidas. Moreover, predictive biomechanical simulation will be combined with wearable sensor technology to build a product recommendation application.
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Unsupervised Network Medicine for Longitudinal Omics Data
(FAU Funds)
Term: since 15. January 2022
Over the last years, large amounts of molecular profiling data (also called “omics data”) have become available. This has raised hopes to identify so-called disease modules, i.e., sets of functionally related molecules constituting candidate disease mechanisms. However, omics data tend to be overdetermined and noisy; and modules identified via purely statistical means are hence often unstable and functionally uninformative. Hence, network-based disease module mining methods (DMMMs) project omics data onto biological networks such as protein-protein interaction (PPI) networks, gene regulatory networks (GRNs), or microbial interaction networks (MINs). Subsequently, network algorithms are used to identify disease modules consisting of small subnetworks. This dramatically decreases the size of the search space and prioritizes disease modules consisting of functionally related molecules, positively affecting both stability and functional relevance of the discovered modules.
However, to the best of our knowledge, all existing DMMMs are subject to at least one of the following two limitations: Firstly, existing DMMMs are typically supervised, in the sense that they try to find subnetworks explaining differences in the omics data between predefined case and control patients or pre-defined disease subtypes. This is potentially problematic, because it implies that existing DMMMs are biased by our current disease ontologies, which are mostly symptom- or organ-based and therefore often too coarse-grained. For instance, around 95 % of all patients with hypertension are diagnosed with so-called “essential hypertension” (code BA00.Z in the ICD-11 disease ontology), meaning that the cause of the hypertension is unknown. In fact, there are probably several disjoint molecular mechanisms causing “essential hypertension”, and the same holds true for many other complex diseases such as Alzheimer’s disease, multiple sclerosis, and Crohn’s disease. Supervised DMMMs which take existing disease definitions for granted hence risk overlooking the molecular mechanisms causing mechanistically distinct subtypes.
Secondly, most existing DMMMs are designed for static omics data and do not support longitudinal data where the patients’ molecular profiles are observed over time. Existing analysis frameworks for longitudinal omics data largely use purely statistical means. Consequently, network medicine approaches for time series data are needed.
To the best of our knowledge, there are only three DMMMs which, in part, overcome these limitations: BiCoN and GrandForest allow unsupervised disease module mining but do not support longitudinal omics data. TiCoNE supports longitudinal data but requires predefined case vs. control or subtype annotations as input. There is hence an unmet need for unsupervised DMMMs for longitudinal omics data. Developing such methods is the main objective of the proposed project.
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Trusted Ecosystem of Applied Medical Data eXchange; Teilvorhaben: FAU@TEAM-X
(Third Party Funds Group – Sub project)
Overall project: Trusted Ecosystem of Applied Medical Data eXchange (TEAM-X)
Term: 1. January 2022 - 31. March 2025
Funding source: Bundesministerium für Wirtschaft und Technologie (BMWi)
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dhip campus-bavarian aim
(Third Party Funds Group – Overall project)
Term: 1. October 2021 - 30. September 2027
Funding source: Industrie
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Empatho-Kinaesthetic Sensor Technology
(Third Party Funds Group – Overall project)
Term: 1. July 2021 - 30. June 2025
Funding source: DFG / Sonderforschungsbereich / Transregio (SFB / TRR)
URL: https://empkins.de/
The proposed CRC “Empathokinaesthetic Sensor Technology” (EmpkinS) will investigate novel radar, wireless, depth camera, and photonics based sensor technologies as well as body function models and algorithms. The primary objective of EmpkinS is to capture human motion parameters remotely with wave-based sensors to enable the identification and analysis of physiological and behavioural states and body functions. To this end, EmpkinS aims to develop sensor technologies and facilitate the collection of motion data for the human body. Based on this data of hitherto unknown quantity and quality, EmpkinS will lead to unprecedented new insights regarding biomechanical, medical, and psychophysiological body function models and mechanisms of action as well as their interdependencies.The main focus of EmpkinS is on capturing human motion parameters at the macroscopic level (the human body or segments thereof and the cardiopulmonary function) and at the microscopic level (facial expressions and fasciculations). The acquired data are captured remotely in a minimally disturbing and non-invasive manner and with very high resolution. The physiological and behavioural states underlying the motion pattern are then reconstructed algorithmically from this data, using biomechanical, neuromotor, and psychomotor body function models. The sensors, body function models, and the inversion of mechanisms of action establish a link between the internal biomedical body layers and the outer biomedical technology layers. Research into this link is highly innovative, extraordinarily complex, and many of its facets have not been investigated so far.To address the numerous and multifaceted research challenges, the EmpkinS CRC is designed as an interdisciplinary research programme. The research programme is coherently aligned along the sensor chain from the primary sensor technology (Research Area A) over signal and data processing (Research Areas B and C) and the associated modelling of the internal body functions and processes (Research Areas C and D) to the psychological and medical interpretation of the sensor data (Research Area D). Ethics research (Research Area E) is an integral part of the research programme to ensure responsible research and ethical use of EmpkinS technology.The proposed twelve-year EmpkinS research programme will develop novel methodologies and technologies that will generate cutting-edge knowledge to link biomedical processes inside the human body with the information captured outside the body by wireless and microwave sensor technology. With this quantum leap in medical technology, EmpkinS will pave the way for completely new "digital", patient-centred diagnosis and therapeutic options in medicine and psychology.Medical technology is a research focus with flagship character in the greater Erlangen-Nürnberg area. This outstanding background along with the extensive preparatory work of the involved researchers form the basis and backbone of EmpkinS.
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Maschinelle Lernverfahren zur Personalisierung muskuloskelettaler Menschmodelle, Bewegungsanalyse
(Third Party Funds Group – Sub project)
Overall project: Empathokinästhetische Sensorik - Sensortechniken und Datenanalyseverfahren zur empathokinästhetischen
Modellbildung und Zustandsbestimmung
Term: 1. July 2021 - 30. June 2025
Funding source: DFG / Sonderforschungsbereich (SFB)
URL: https://www.empkins.de/
The extent to which a neural network can be used to effectively personalise gait simulations using motion data is explored. We first investigate the influence of body parameters on gait simulation. An initial version of the personalisation is trained with simulated motion data, since ground truth data is known for this purpose. We then explore gradient-free methods to fit the network for experimental motion data. The resulting network is validated with magnetic resonance imaging, electromyography and intra-body variables.
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EmpkinS iRTG - EmpkinS integrated Research Training Group
(Third Party Funds Group – Sub project)
Overall project: Empathokinästhetische Sensorik
Term: 1. July 2021 - 30. June 2025
Funding source: DFG / Sonderforschungsbereich / Integriertes Graduiertenkolleg (SFB / GRK)
URL: https://www.empkins.de/
The integrated Research Training Group (iRTG) offers all young researchers a structured framework programme and supports them in their scientific profile and competence development. The diverse measures provided enable the young researchers to work on their respective academic qualifications in a structured and targeted manner. Particular attention is paid to their networking and their ability to communicate intensively and to take responsibility for their own scientific work. The doctoral researchers are supervised by two project leaders.
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Sensorbasierte Bewegungs- und Schlafanalyse beim Parkinson-Syndrom
(Third Party Funds Group – Sub project)
Overall project: Empathokinästhetische Sensorik
Term: 1. July 2021 - 30. June 2025
Funding source: DFG / Sonderforschungsbereich (SFB)
URL: https://www.empkins.de/
In D04, innovative, non-contact EmpkinS sensor technology using machine learning algorithms and multimodal reference diagnostics is evaluated using the example of Parkinson’s-associated sleep disorder patterns. For this purpose, body function parameters of sleep are technically validated with wearable sensor technology and non-contact EmpkinS sensor technology in comparison to classical poly-somnography and correlated to clinical scales. In an algorithmic approach, multiparametric sleep parameters and sleep patterns are then evalulated in correlation to movement, cardiovascular and sleep phase regulation disorders.
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Empathokinästhetische Sensorik für Biofeedback bei depressiven Patienten
(Third Party Funds Group – Sub project)
Overall project: Empathokinästhetische Sensorik
Term: 1. July 2021 - 30. June 2025
Funding source: DFG / Sonderforschungsbereich (SFB)
URL: https://www.empkins.de/
The aim of the D02 project is the establishment of empathokinesthetic sensor technology and methods of machine learning as a means for the automatic detection and modification of depression-associated facial expressions, posture, and movement. The aim is to clarify to what extent, with the help of kinesthetic-related modifications influence depressogenic information processing and/or depressive symptoms. First, we will record facial expressions, body posture, and movement relevant to depression with the help of currently available technologies (e.g., RGB and depth cameras, wired EMG, established emotion recognition software) and use them as input parameters for new machine learning models to automatically detect depression-associated affect expressions. Secondly, a fully automated biofeedback paradigm is to be implemented and validated using the project results available up to that point. More ways of real-time feedback of depression-relevant kinaesthesia are investigated. Thirdly, we will research possibilities of mobile use of the biofeedback approach developed up to then.
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Erforschung der posturalen Kontrolle basierend auf sensomotorisch erweiterten muskuloskelettalen Menschmodellen
(Third Party Funds Group – Sub project)
Overall project: Empathokinästhetische Sensorik
Term: 1. July 2021 - 30. June 2025
Funding source: DFG / Sonderforschungsbereich (SFB)
URL: https://www.empkins.de/
A novel postural control model of walking is explored to characterise the components of dynamic balance control. For this purpose, clinically annotated gait movements are used as input data and muscle actuated multi-body models are extended by a sensorimotor level. Neuromotor and control model parameters of (patho-)physiological movement are identified with the help of machine learning methods. Technical and clinical validation of the models will be performed. New EmpkinS measurement techniques are to be transferred to the developed models as soon as possible.
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A comprehensive deep learning framework for MRI reconstruction
(Third Party Funds Single)
My research is about intelligent algorithms in healthcare, especially Medical Imaging. I am working on self-driving medical image acquisition that can guide human operators in real-time during diagnostics. Artificial Intelligence is currently used as a blanket term to describe research in these areas.
Thus, we try to democratize rare healthcare expertise through Machine Learning, providing guidance in real-time applications and second reader expertise in retrospective analysis. We develop normative learning algorithms for large populations, integrating imaging, patient records and omics, leading to data analysis that mimics human decision making.
Research projects
Federated network medicine for laboratory data in paediatric oncology
(Third Party Funds Group – Overall project)
Funding source: BMBF / Verbundprojekt
In FLabNet, we will harness the potential of algorithmic network biology and distributed machine learning to address two exemplary unmet needs in paediatric oncology: prediction ofchemotherapy side effects like neutropenic fever and early-stage detection of rare malignantdiseases such as myeloproliferative neoplasms. Based on >54 million laboratory test resultsfrom >500,000 patients from the Core Dataset of the German Medical Informatics Initiative (MII),we will create personalised networks, where nodes represent individual laboratory measurementsand edges encode patient-specific relationships. We hypothesise the emerging personal graph representations to capture the unique spectra and dependencies of the individual patients’ health anddisease characteristics. The networks will be used as signatures for label-efficient graph-based pre-dictors such as graph kernels; and we will provide privacy-preserving federated implementationsof our predictors that are fully interoperable with MII standards. To achieve its objectives, ourconsortium combines expertise in algorithmic systems biology (FAU), paediatric oncology (UKER),quantitative analysis of laboratory data (UKER), federated learning for biomedicine (Bitspark GmbH& FAU), and professional software development (Bitspark GmbH). These synergistic skill sets willenable us to combine laboratory diagnostics, computational systems medicine, and privacy-preserving machine learning, advancing the state of the art in quantitative analysis of laboratory data for precision medicine in paediatric oncology and beyond.
High-resolution protein-protein interaction networks for biomedical research
(Third Party Funds Group – Overall project)
Funding source: andere Förderorganisation
URL: https://www.cobinet.ai/
Center for AI in Medicine
(Own Funds)
Embedded AI for neuromuscular orthoses
(Third Party Funds Single)
Funding source: Bundesministerium für Bildung und Forschung (BMBF)
Bridging the gap in ACL injury prevention with FAME: Field-based Athlete Motion Evaluation and simulation
(Third Party Funds Single)
Funding source: Deutsche Forschungsgemeinschaft (DFG)
Untersuchung von In-situ-Gewebeschemata mittels volumetrischer Bildgebung und maschinellem Lernen für die Bildanalyse
(Third Party Funds Group – Sub project)
Term: 1. January 2024 - 31. December 2027
Funding source: DFG / Klinische Forschungsgruppe (KFO)
Entzündung beginnt vor allem in Geweben, und ihre Ausprägung und ihr Ausmaß werden weitgehend von diesen kontrolliert. Infiltrierende Immunzellen sind hochgradig plastisch und integrieren sowohl molekulare als auch biophysikalische Informationen aus ihrer Umgebung, die allesamt ihre Effektor-Funktionen sowie ihr weiteres Schicksal bestimmen. Mit dem Fortschritt hochdimensionaler OMICs-Ansätze haben wir begonnen, die zelluläre Heterogenität komplexer entzündlicher Infiltrate besser zu verstehen. Während jüngste analytische Ansätze zelluläre Interaktionen auf der Grundlage von Transkriptionsprofilen vorhersagen können, bleiben ihre tatsächlichen funktionellen Interaktionen und die Einwirkung ihrer mikroanatomischen Umgebung jedoch weiterhin spekulativ. Um die Beziehung zwischen Gewebestruktur, Architektur der Mikroumgebung und Zellfunktionen verstehen zu können, müssen multidimensionale Ex vivo-Zellanalysen durch quantitative In-situ-Bildgebung ergänzt werden. Neueste 2D-Hyperplex-Mikroskopieverfahren erlauben mittlerweile mehrere Parameter mit räumlichem Kontext zu erfassen. Mit diesen Techniken lässt sich jedoch die komplexe 3D-Architektur von strukturell vielschichtigen und kompartimentierten Geweben wie dem Darm oder dem Gehirn nicht adäquat abbilden. Gleichzeitig erfordert die Fülle von Daten, die durch Bildgebungs- und OMICs-Techniken erzeugt werden, nachfolgende integrative Analyseschritte, die bei einer manuellen Auswertung in der Regel nicht zu bewältigen sind. Um diese Lücken zu schließen, werden wir neue Mikroskopieanwendungen und quantitative, durch maschinelles Lernen unterstützte Analysemethoden für Gewebe entlang der Darm-Hirn-Achse mit hoher Granularität sowohl in 2D als auch in 3D entwickeln. Dieses explorative Vorhaben dient der Etablierung innovativer Methoden und wird dazu beitragen, räumliche Daten mit zellulären Profilen zu integrieren, um Organisationsschemata zu entschlüsseln, die Gesundheit und Krankheit zugrunde liegen.
Federated virtual twins for privacy-preserving personalised outcome prediction of type 2 diabetes treatment
(Third Party Funds Single)
Funding source: EU - 9. Rahmenprogramm - Horizon Europe
URL: https://daibetes.eu/
Virtual twins have the potential to be used as prognostic tools in precision medicine to support individualized disease management. However, training these models requires large volumes of data from various sources, which is challenging due to privacy regulations like the General Data Protection Regulation (GDPR). Recently, privacy-preserving computational methods, such as federated learning, have emerged, offering a way to utilize extensive data effectively while protecting sensitive patient information.
In dAIbetes, our main medical objective is to provide personalized predictions of treatment outcomes for type 2 diabetes, a condition affecting 1 in 10 adults globally and leading to annual costs of approximately 893 billion EUR. Although healthcare professionals are improving at addressing diabetes risk factors like diet and exercise, there are currently no guidelines for predicting treatment outcomes tailored to individual patients.
dAIbetes brings together advaned expertise in federated learning, artificial intelligence, cybersecurity, diabetes data standardization, clinical validation, as well as in legal and ethical evaluation of applying advanced federated machine learning to personalized medicine. 13 Partners from 13 European countries and the US will jointly implement the project which is structured into 9 Work Packages (WP1-WP9). At FAU, we are working on WP3, i.e., the development of virtual twin apps for training of virtual twin models that will use data from type 2 diabetes patients.
Automatisierte Kuratierung von in-vivo erfassten Zeitreihen
(Third Party Funds Single)
Funding source: Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie (StMWi) (seit 2018)
Restoring hand function with neuromuscular restrictions using an intelligent neuroorthosis
(Third Party Funds Single)
Funding source: Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie (StMWi) (seit 2018)
A Platform for Dynamic Exploration of the Cooperative Health Research in South Tyrol Study Data via Multi-Level Network Medicine
(Third Party Funds Single)
Funding source: Deutsche Forschungsgemeinschaft (DFG)
URL: https://www.dyhealthnet.ai/
The Cooperative Health Research in South Tyrol (CHRIS) study offers a comprehensive overview of the health state of >13,000 adults in the middle and upper Val Venosta. It is the largest population-based molecular study in Italy with a longitudinal lookout to investigate the genetic and molecular basis of age-related common chronic conditions and their interaction with lifestyle and environment in the general population. In CHRIS, the combination of molecular profiling data, such as genomics and metabolomics, together with important baseline clinical and lifestyle data offers vast opportunities for understanding physiological changes that could lead to clinical complications or indicate the prevalence or early onset of diseases together with their molecular underpinnings.
Where disease-focused studies often have a clear hypothesis that dictates the necessary statistical analyses, population-based cohorts such as CHRIS are more versatile and allow both testing existing hypotheses as well as generating new hypotheses that arise from statistically significant associations of the available data. Ideally, this type of explorative analysis is open to biomedical researchers that do not necessarily have experience with data analysis or machine learning. Network-based approaches are ideally suited for studying heterogeneous biomedical data, giving rise to the field of network medicine. However, network medicine techniques have so far mainly been used in the context of studies focusing on individual diseases. Network-based platforms for the explorative analysis of population-based cohort data do not exist.
In DyHealthNet, we will close this gap and develop a network-based data analysis platform, which will allow to integrate heterogeneous data and support explorative data analytics over dynamically generated subsets of the CHRIS study data. To fully leverage the potential of the available multi-level data, the DyHealthNet platform combines (1) data integration using standardized medical information models (HL7 FHIR), (2) innovative index structures for scalable dynamic analysis, (3) machine learning, and (4) visual analytics. DyHealthNet will render the CHRIS population cohort data accessible for state-of-the-art privacy-preserving, network-based data analysis. DyHealthNet will hence enable mining of context-specific pathomechanisms for precision medicine, and will serve as a blueprint for dynamic explorative analysis of multi-level cohort data worldwide. To achieve these objectives, the DyHeathNet consortium combines expertise in population-based cohort studies (Fuchsberger) and in the development of complex algorithms for the analysis of molecular networks (Blumenthal), applied biomedical AI and software systems (List), and customized index structures for scalable data management (Gamper).
Development of an innovative neurobandage with an integrated brain-computer interface for testing hand function
(Third Party Funds Single)
Funding source: Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie (StMWi) (seit 2018)
Smart Wound Dressing incorporating Dye-based Sensors Monitoring of O2, pH and CO2 under the wound dressing and smart algorithms to assess the wound healing process
(Third Party Funds Single)
Funding source: Bayerische Forschungsstiftung
In Germany alone, the number of patients with chronic wound healing disorders is estimated at around 2.7 million. According to projections, the treatment of chronic wounds accounts for € 23 - 36 billion per year. Of the treatment costs for chronic wounds, 4.6 to 7.2 billion € alone are accounted for by the associated cost-intensive dressing materials. The aim of the SWODDYS project is to research the fundamentals for a new type of intelligent wound dressing for the treatment of acute and chronic wounds, which can monitor the energy-metabolic tissue and wound healing status individually for each patient and online by integrating fluorescent dye-based oxygen, pH and CO2 sensors.
Teilprojekt A2
(Third Party Funds Group – Sub project)
Term: 1. September 2023 - 31. August 2027
Funding source: DFG / Forschungsgruppe (FOR)
Quantitative diffusionsgewichtete MRT und Suszeptibilitätskartierung zur Charakterisierung der Gewebemikrostruktur
(Third Party Funds Group – Sub project)
Term: 1. September 2023 - 31. August 2027
Funding source: DFG / Forschungsgruppe (FOR)
Dieses Projekt ist Teil der Forschungsgruppe (FOR) "Schnelle Kartierung von quantitativen MR bio-Signaturen bei ultrahohen Magnetfeldstärken". Es konzentriert sich auf die Erweiterung, Beschleunigung und Verbesserung der Diffusions- und quantitativen Suszeptibilitäts-Magnetresonanztomographie. Das Arbeitsprogramm ist in zwei Teile gegliedert. Im ersten Teil wird ein beschleunigtes Protokoll für die klinischen Projekte der FOR vorbereitet. Im zweiten Teil sollen eine weitere Beschleunigung sowie Qualitätsverbesserungen erreicht werden. Konkret werden wir eine lokal niedrigrangig regularisierte echoplanare Bildgebungssequenz für die diffusionsgewichtete Bildgebung implementieren. Sie nutzt Datenredundanzen bei Akquisitionen mit mehreren Diffusionskodierungen, um das Signal-Rausch-Verhältnis effektiv zu erhöhen und damit den Akquisitionsprozess zu beschleunigen. Die Sequenz wird im Wesentlichen beliebige Diffusionskodierungsmöglichkeiten ermöglichen (z.B. b-Tensor-Kodierung). In einem zweiten Schritt werden wir eine verschachtelte Mehrschuss-Version dieser Sequenz entwickeln, um Bildverzerrungen zu reduzieren, die bei der 7-Tesla echoplanaren Bildgebung störend sind. Für die quantitative Suszeptibilitätskartierung (QSM) werden wir eine Sequenz mit einer Stack-of-Stars-Aufnahmetrajektorie implementieren. Da die Magnitudenbilder von Gradientenechosequenzen, die zu unterschiedlichen Echozeiten akquiriert werden, Datenredundanzen aufweisen, die mit denen von diffusionskodierten Bildern vergleichbar sind, werden wir bei der Bildrekonstruktion ebenfalls eine lokale Regularisierung niedrigen Ranges verwenden. Die radialen Trajektorien dieser Sequenz sollten für eine unterabgetastete und damit beschleunigte Bildrekonstruktion gut geeignet sein. In einem zweiten Schritt werden wir die Fähigkeiten unserer Sequenz durch eine quasi-kontinuierliche Echozeit-Abtastung erweitern, bei dem jede Speiche ihre eigene optimierte Echozeit hat. Dies wird eine verbesserte Qualität der QSM ermöglichen, wenn Fett im Bild vorhanden ist, wie es häufig bei Muskeluntersuchungen und in der Brustbildgebung der Fall ist. Bezüglich der QSM-Rekonstruktion werden wir Verfahren des tiefen Lernens entwickeln, um eine qualitativ hochwertige Rekonstruktion mit einer geringeren Menge an Bilddaten als bei herkömmlichen Rekonstruktionsansätzen zu ermöglichen. Wir werden bestehende neuronale Netzwerke von niedrigeren Feldstärken auf 7 T anpassen und deren Fähigkeiten so erweitern, dass wir auch atemzyklusabhängige Feldkarten. Dieses Projekt wird parallele Sendemethoden (pTx) vom pTx-Projekt der FOR erhalten. Wir werden die entwickelten Sequenzen nach dem ersten Jahr an die klinischen Projekte der FOR liefern. Darüber hinaus werden wir wesentliche Auswerte- und Bildrekonstruktionsmethoden an die anderen Projekte der FOR transferieren.und quasi-kontinuierliche Echozeiten in die Rekonstruktion integrieren können.
Medical Image Analysis with Normative Machine Learning
(Third Party Funds Single)
Funding source: Europäische Union (EU)
As one of the most important aspects of diagnosis, treatment planning, treatment delivery, and follow-up, medical imaging provides an unmatched ability to identify disease with high accuracy. As a result of its success, referrals for imaging examinations have increased significantly. However, medical imaging depends on interpretation by highly specialised clinical experts and is thus rarely available at the front-line-of-care, for patient triage, or for frequent follow-ups. Very often, excluding certain conditions or confirming physiological normality would be essential at many stages of the patient journey, to streamline referrals and relieve pressure on human experts who have limited capacity. Hence, there is a strong need for increased imaging with automated diagnostic support for clinicians, healthcare professionals, and caregivers.
Machine learning is expected to be an algorithmic panacea for diagnostic automation. However, despite significant advances such as Deep Learning with notable impact on real-world applications, robust confirmation of normality is still an unsolved problem, which cannot be addressed with established approaches.
Like clinical experts, machines should also be able to verify the absence of pathology by contrasting new images with their knowledge about healthy anatomy and expected physiological variability. Thus, the aim of this proposal is to develop normative representation learning as a new machine learning paradigm for medical imaging, providing patient-specific computational tools for robust confirmation of normality, image quality control, health screening, and prevention of disease before onset. We will do this by developing novel Deep Learning approaches that can learn without manual labels from healthy patient data only, applicable to cross-sectional, sequential, and multi-modal data. Resulting models will be able to extract clinically useful and actionable information as early and frequent as possible during patient journeys.
Human Impedance control for Tailored Rehabilitation
(Third Party Funds Single)
Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
Privacy-preserving analysis of distributed medical data
(Own Funds)
Recent legislative development, such as the European Health Data Space, expand access to anonymizied health data for various entities. While these advances offer opportunities for medical research and innovation, they also increase the risk of compromising individuals' privacy.
This project addresses the critical tension between the growing utility of health data and the need to protect individual privacy through organizational, infrastructural, and technical approaches. A key component of the technical solutions is privacy-enhancing technologies (PETs), such as secure multi-party computation and (local) differential privacy, which safeguard individuals' privacy while enabling the statistical analysis of aggregate data.
AI4MDD: AI-Powered Prognosis of Treatment Response in Major Depression Disorder
(Third Party Funds Single)
Funding source: Industrie
Teilvorhaben: Friedrich-Alexander-Universität Erlangen-Nürnberg
(Third Party Funds Group – Sub project)
Term: 1. May 2023 - 4. April 2026
Funding source: BMBF / Verbundprojekt
Dimensionality reduction for molecular data based on explanatory power of differential regulatory networks
(Third Party Funds Group – Overall project)
Funding source: Bundesministerium für Bildung und Forschung (BMBF)
URL: https://www.netmap.ai/
Rapid advances in single-cell RNA sequencing (scRNA-seq) technology are leading to ever-increasing dimensions of the generated molecular data, which complicates data analyses. In NetMap, new scalable and robust dimensionality reduction approaches for scRNA-seq data will be developed. To this end, dimensionality reduction will be integrated into a central task of the systems medicine analysis of scRNA-seq data: inference of gene regulatory networks (GRNs) and driver transcription factors based on cell expression profiles. Each resulting dimension will correspond to a driver GRN, and the coordinate of a cell in this low-dimensional representation will quantify the extent to which the particular driver GRN explains the cell's gene expression profile. These new methods will be implemented as a user-friendly software platform for exploratory expert-in-the-loop analysis and in silico prediction of drug repurposing candidates.
As a case study, we will investigate CD4 helper T cell exhaustion, a potential limiting factor in immunotherapy. NetMap's strategy consists of (1) analyzing phenotypic heterogeneity of depleted CD4 T cells, (2) identifying transcriptional mechanisms that control this heterogeneity, (3) amplifying/eliminating specific subsets and testing their functional impact. This will allow the development of an atlas of the gene regulatory landscape of depleted CD4 T cells, while the in vivo testing of key regulatory transcription factors will help demonstrate the power of the developed methods and allow evaluation and improvement of predictions.
Testing and Experimentation Facility for Health AI and Robotics
(Third Party Funds Group – Sub project)
Term: 1. January 2023 - 31. December 2027
Funding source: Europäische Union (EU)
URL: https://www.tefhealth.eu/
Maschinelles Lernen und Datenanalyse für heterogene, artübergreifende Daten (X02)
(Third Party Funds Group – Sub project)
Term: 1. January 2023 - 31. December 2026
Funding source: DFG / Sonderforschungsbereich (SFB)
X02 nutzt die in EBM erzeugten Bilddaten und mechanischen Messungen, um Deep Learning-Methoden zu entwickeln, die Wissen über Spezies hinweg transferieren. In silico und in vitro Analysen werden deutlich spezifischere Daten liefern als in vivo Experimente, insbesondere für menschliches Gewebe. Um hier Erkenntnisse aus datenreichen Experimenten zu nutzen, werden wir Transfer Learning-Algorithmen für heterogene Daten entwickeln. So kann maschinelles Lernen auch unter stark datenlimitierten Bedingungen nutzbar gemacht werden. Ziel ist es, ein holistisches Verständnis von Bilddaten und mechanischen Messungen über Artgrenzen hinweg zu ermöglichen.
End-to-End Deep Learning Image Reconstruction and Pathology Detection
(Third Party Funds Single)
Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
The majority of diagnostic medicalimaging pipelines follow the same principles: raw measurement data is acquiredby scanner hardware, processed by image reconstruction algorithms, and thenevaluated for pathology by human radiology experts. Under this paradigm, every stephas traditionally been optimized to generate images that are visually pleasingand easy to interpret for human experts. However, raw sensor information thatcould maximize patient-specific diagnostic information may get lost in thisprocess. This problem is amplified by recent developments in machine
learning for medical imaging. Machine learning has been used successfully inall steps of the diagnostic imaging pipeline: from the design of dataacquisition to image reconstruction, to computer-aided diagnosis. So far, thesedevelopments have been disjointed from each other. In this project, we willfuse machine learning for image reconstruction and for image-based diseaselocalization, thus providing an end-to-end learnable image reconstruction andjoint pathology detection approach that operates directly on raw measurementdata. Our hypothesis is that this combination can maximize diagnostic accuracywhile providing optimal images for both human experts and diagnostic machinelearning models.
Digital health application for the therapy of incontinence patients
(Third Party Funds Single)
Funding source: Bundesministerium für Wirtschaft und Klimaschutz (BMWK)
The goal of this project is the development of an application for supporting the physical rehabilitation therapy of prostatectomy and incontinence patients in planning and execution. An AI-driven algorithm for automatic planning will be developed and extended by a machine learning approach for live exercise execution feedback. The developed application will be clinically evaluated regarding effectiveness and therapy benefit.
Erarbeitung der Studienkonzeption, Medizinisch wissenschaftlich beratende Funktion
(Third Party Funds Group – Sub project)
Term: 1. January 2023 - 31. December 2024
Funding source: Bundesministerium für Wirtschaft und Klimaschutz (BMWK)
The goal of this project is the development of an application for supporting physical rehabilitation therapy in planning and execution. An AI-driven algorithm for automatic planning will be developed and extended by a machine learning approach for live exercise execution feedback. The developed application will be clinically evaluated regarding effectiveness and therapy benefit.
DIAMond - diabetes type 1 management with personalized recommendation using data science
(Third Party Funds Single)
Funding source: Deutscher Akademischer Austauschdienst (DAAD)
Diabetes is an overwhelming disease, directly influencing more than 422 million people worldwide who are living with this disease. Type 1 diabetes is the most severe form of the disease. The management of type 1 diabetes is especially difficult for young children and adolescents. Additionally, the most feared complication of type 1 diabetes – hypoglycemia – might occur after several hours, for example, during the night.
The DIAmond project will address the personalized and better management of type 1 diabetes using data science and machine learning to gain insights into the problem of hypoglycemia. Data from the DIAcamp study is used to advance personalized treatment recommendations. In the DIAcamp study, children participated for one week. They were equipped with a continuous glucose sensor and a wearable device for monitoring heart rate, accelerometry, and further physiological parameters during their participation. Physicians and carers from the DIAcamp study documented insulin doses, carbohydrate intake, and time and type of activity. Within the DIAmond project, novel machine learning algorithms will determine the probability of hypoglycemia. Exploratory analysis of the physiological time series will result in the most predictive features, building the base for personalized treatment recommendations.
This project is a joint project with the Department of Computer Science, ETH Zurich, Switzerland.
Applied Data Science in Digital Psychology
(Third Party Funds Single)
Funding source: Bayerisches Staatsministerium für Wissenschaft und Kunst (StMWK) (seit 2018)
University education in psychology, medical technology and computer science currently focuses on teaching basic methods and knowledge with little involvement of other disciplines. Increasing digitalization and the ever more rapid spread of digital technologies, such as wearable sensors, smartphone apps, and artificial intelligence, also in the health sector, offer a wide range of opportunities to address psychological issues from new and interdisciplinary perspectives. However, this requires close cooperation between the disciplines of psychology and technical disciplines such as medical technology and computer science to enable the necessary knowledge transfer. Especially in these disciplines, there is a considerable need for innovative and interdisciplinary teaching concepts and research projects that teach the adequate use of digital technologies and explore the application of these technologies to relevant issues in order to enable better care in the treatment of people with mental disorders.
AI-Powered Manipulation System for Advanced Robotic Service, Manufacturing and Prosthetics
(Third Party Funds Group – Sub project)
Term: 1. September 2022 - 28. February 2026
Funding source: Europäische Union (EU)
URL: https://intelliman-project.eu/
Multimodal Machine Learning for Decision Support Systems
(Third Party Funds Single)
Funding source: Siemens AG
The project aims to identify areas where advanced data analysis and processing methods can be applied to aspects of computer tomography (CT) technology. Furthermore included is the implementation and validation of said methods.
In this project, we analyze machine and customer data sent by thousands of high-end medical devices every day.
Since potentially relevant Information is often presented in different modalities, the optimal application of fusion techniques is a key factor when extracting insights.
Biomechanical Assessment of Big Wave Surfing
(Third Party Funds Single)
Funding source: Siemens AG
The goal of this project is to develop experimental approaches and simulation methods for biomechanical assessment of big wave surfing. This goal will be addressed in collaboration with Sebastian Steudtner and Siemens Healthineers. The methods include, but are not limited to, biomechanical movement analysis, musculoskeletal simulation, and sensor fusion.
The focus of the research activities will be centered on:
Biomechanical Assessment of Big Wave Surfing
(Third Party Funds Single)
Funding source: Siemens AG
Machine Learning for CT-Detector Production
(Third Party Funds Single)
Funding source: Industrie
The main goal of this project is to improve the detector manufacturing for computer tomography (CT). Therefore, data is gathered during the production of a CT-detector. This data is analysed and used to develop and train a machine learning system which should find the best composition of a CT-detector. In the future, the system will be integrated into the process of CT-detector manufacturing which, in result, should further improve the image quality and the production process of CT-devices. Especially, the warehouse utilization and the first-pass-yield should be enhaced. The project is realized in cooperation with Siemens Healthineers Frochheim.
Individual Performance Prediction Using Musculoskeletal Modeling
(Third Party Funds Single)
Funding source: Industrie
Biomechanical modeling and simulation are performed to analyze and understand human motion and performance. One objective is to reconstruct human motion from measurement data e.g. to assess the individual performance of athletes and customers. Another objective is to synthesize realistic human motion to study human-production interaction. The reconstruction (a) and synthesis of human motion (b) will be addressed in this research position. New algorithms using biomechanical simulation of musculoskeletal models will be developed to enable innovative applications and services for Adidas. Moreover, predictive biomechanical simulation will be combined with wearable sensor technology to build a product recommendation application.
Unsupervised Network Medicine for Longitudinal Omics Data
(FAU Funds)
Over the last years, large amounts of molecular profiling data (also called “omics data”) have become available. This has raised hopes to identify so-called disease modules, i.e., sets of functionally related molecules constituting candidate disease mechanisms. However, omics data tend to be overdetermined and noisy; and modules identified via purely statistical means are hence often unstable and functionally uninformative. Hence, network-based disease module mining methods (DMMMs) project omics data onto biological networks such as protein-protein interaction (PPI) networks, gene regulatory networks (GRNs), or microbial interaction networks (MINs). Subsequently, network algorithms are used to identify disease modules consisting of small subnetworks. This dramatically decreases the size of the search space and prioritizes disease modules consisting of functionally related molecules, positively affecting both stability and functional relevance of the discovered modules.
However, to the best of our knowledge, all existing DMMMs are subject to at least one of the following two limitations: Firstly, existing DMMMs are typically supervised, in the sense that they try to find subnetworks explaining differences in the omics data between predefined case and control patients or pre-defined disease subtypes. This is potentially problematic, because it implies that existing DMMMs are biased by our current disease ontologies, which are mostly symptom- or organ-based and therefore often too coarse-grained. For instance, around 95 % of all patients with hypertension are diagnosed with so-called “essential hypertension” (code BA00.Z in the ICD-11 disease ontology), meaning that the cause of the hypertension is unknown. In fact, there are probably several disjoint molecular mechanisms causing “essential hypertension”, and the same holds true for many other complex diseases such as Alzheimer’s disease, multiple sclerosis, and Crohn’s disease. Supervised DMMMs which take existing disease definitions for granted hence risk overlooking the molecular mechanisms causing mechanistically distinct subtypes.
Secondly, most existing DMMMs are designed for static omics data and do not support longitudinal data where the patients’ molecular profiles are observed over time. Existing analysis frameworks for longitudinal omics data largely use purely statistical means. Consequently, network medicine approaches for time series data are needed.
To the best of our knowledge, there are only three DMMMs which, in part, overcome these limitations: BiCoN and GrandForest allow unsupervised disease module mining but do not support longitudinal omics data. TiCoNE supports longitudinal data but requires predefined case vs. control or subtype annotations as input. There is hence an unmet need for unsupervised DMMMs for longitudinal omics data. Developing such methods is the main objective of the proposed project.
Trusted Ecosystem of Applied Medical Data eXchange; Teilvorhaben: FAU@TEAM-X
(Third Party Funds Group – Sub project)
Term: 1. January 2022 - 31. March 2025
Funding source: Bundesministerium für Wirtschaft und Technologie (BMWi)
dhip campus-bavarian aim
(Third Party Funds Group – Overall project)
Funding source: Industrie
Empatho-Kinaesthetic Sensor Technology
(Third Party Funds Group – Overall project)
Funding source: DFG / Sonderforschungsbereich / Transregio (SFB / TRR)
URL: https://empkins.de/
Maschinelle Lernverfahren zur Personalisierung muskuloskelettaler Menschmodelle, Bewegungsanalyse
(Third Party Funds Group – Sub project)
Term: 1. July 2021 - 30. June 2025
Funding source: DFG / Sonderforschungsbereich (SFB)
URL: https://www.empkins.de/
EmpkinS iRTG - EmpkinS integrated Research Training Group
(Third Party Funds Group – Sub project)
Term: 1. July 2021 - 30. June 2025
Funding source: DFG / Sonderforschungsbereich / Integriertes Graduiertenkolleg (SFB / GRK)
URL: https://www.empkins.de/
The integrated Research Training Group (iRTG) offers all young researchers a structured framework programme and supports them in their scientific profile and competence development. The diverse measures provided enable the young researchers to work on their respective academic qualifications in a structured and targeted manner. Particular attention is paid to their networking and their ability to communicate intensively and to take responsibility for their own scientific work. The doctoral researchers are supervised by two project leaders.
Sensorbasierte Bewegungs- und Schlafanalyse beim Parkinson-Syndrom
(Third Party Funds Group – Sub project)
Term: 1. July 2021 - 30. June 2025
Funding source: DFG / Sonderforschungsbereich (SFB)
URL: https://www.empkins.de/
In D04, innovative, non-contact EmpkinS sensor technology using machine learning algorithms and multimodal reference diagnostics is evaluated using the example of Parkinson’s-associated sleep disorder patterns. For this purpose, body function parameters of sleep are technically validated with wearable sensor technology and non-contact EmpkinS sensor technology in comparison to classical poly-somnography and correlated to clinical scales. In an algorithmic approach, multiparametric sleep parameters and sleep patterns are then evalulated in correlation to movement, cardiovascular and sleep phase regulation disorders.
Empathokinästhetische Sensorik für Biofeedback bei depressiven Patienten
(Third Party Funds Group – Sub project)
Term: 1. July 2021 - 30. June 2025
Funding source: DFG / Sonderforschungsbereich (SFB)
URL: https://www.empkins.de/
The aim of the D02 project is the establishment of empathokinesthetic sensor technology and methods of machine learning as a means for the automatic detection and modification of depression-associated facial expressions, posture, and movement. The aim is to clarify to what extent, with the help of kinesthetic-related modifications influence depressogenic information processing and/or depressive symptoms. First, we will record facial expressions, body posture, and movement relevant to depression with the help of currently available technologies (e.g., RGB and depth cameras, wired EMG, established emotion recognition software) and use them as input parameters for new machine learning models to automatically detect depression-associated affect expressions. Secondly, a fully automated biofeedback paradigm is to be implemented and validated using the project results available up to that point. More ways of real-time feedback of depression-relevant kinaesthesia are investigated. Thirdly, we will research possibilities of mobile use of the biofeedback approach developed up to then.
Erforschung der posturalen Kontrolle basierend auf sensomotorisch erweiterten muskuloskelettalen Menschmodellen
(Third Party Funds Group – Sub project)
Term: 1. July 2021 - 30. June 2025
Funding source: DFG / Sonderforschungsbereich (SFB)
URL: https://www.empkins.de/
A novel postural control model of walking is explored to characterise the components of dynamic balance control. For this purpose, clinically annotated gait movements are used as input data and muscle actuated multi-body models are extended by a sensorimotor level. Neuromotor and control model parameters of (patho-)physiological movement are identified with the help of machine learning methods. Technical and clinical validation of the models will be performed. New EmpkinS measurement techniques are to be transferred to the developed models as soon as possible.
A comprehensive deep learning framework for MRI reconstruction
(Third Party Funds Single)
Funding source: National Institutes of Health (NIH)
URL: https://govtribe.com/award/federal-grant-award/project-grant-r01eb029957
2025
2024
2023
2022
2021
2020
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