Research

Bio Signal Processing

Research focus: Computer-assisted Image-guided Interventions
Team lead: Michael Unger

Members: Reinhard Fuchs, Sven Martin, Mohamed Awad, Martin Hertel

The team focuses on the use of sensors and technologies to improve medical practice – in diagnostics, treatment and training. One focus is non-invasive neurological mapping: neuronal activity is recorded precisely through the measurement of muscle activity, visualized and processed so that it can be seamlessly integrated into clinical workflows.

Further projects improve surgical training through data-based assistance systems that analyze training progress and provide targeted feedback, and create realistic simulation models with integrated sensors that reproduce anatomical conditions and biomechanical reactions in detail.

Projects

• CortexMap
  CortexMap Development of a novel navigated transcranial magnetic stimulation system for efficient planning and evaluation of neurosurgical procedures, as well as optimal integration into the surgical workflow

  Navigated transcranial magnetic stimulation system for non-invasive mapping of the motor cortex in patients with brain tumors – with multi-channel EMG, automatic adjustment of stimulation intensity and analysis for surgical planning.

• EyeHearU
  EyeHearU Multimodal acquisition, simulation and audiovisual enhancement for the individual training of basic functional laparoscopic skills

  Training assistance system that records multimodal data from laparoscopic exercises and predicts training progress; two studies with students have been evaluated.

• FaszioTrain
  FaszioTrain Development of a sensor-based medical training model for fasciotomies

  Sensor-based training model for fasciotomies of the lower leg that realistically reproduces swelling and pressure on the nerve pathways.

• ReaFBS
  ReaFBS Feedback system for reducing brain damage during cardiopulmonary resuscitation

  Feedback system that measures arterial blood flow during resuscitation using Doppler ultrasound – usable by laypersons without additional training; a mobile device with automated vessel location is in development.

• NeoTapi
  NeoTapi Development of a non-invasive and contactless method for measuring physiological parameters for the detection and classification of apnea of prematurity and neonatal sepsis

  Contactless measurement of respiration, heart rate and body temperature for detecting apnea of prematurity and neonatal sepsis.

Clinical Workflows and Integrated Assistance Systems

Research focus: Model-based Medicine and Intelligent Operating Room
Team lead: Juliane Neumann, Erik Schreiber

The team develops concepts, methods and digital tools to improve the efficiency, safety and quality of healthcare, reduce costs and risks, and reduce the workload of medical staff. A central aspect is the modelling, analysis and optimization of clinical workflows – using business process modeling, data analysis and simulation techniques.

In addition, the team develops integrated assistance systems combining artificial intelligence, machine learning and sensor-based applications: for example tools for identifying shortcomings of digital systems in everyday clinical practice, aggregating study data from digital and paper-based sources, sensor-based gait analysis for predicting falls, and real-time decision support in critical situations. Another focus is the integration of these systems into existing workflows and IT structures.

Projects

• SurgiTrace
  SurgiTrace AI-based system for optimizing surgical planning and enhancing perioperative safety through intelligent tracking of instruments and implants

  AI-based system for surgical planning: a smart implant cabinet dispenses endoprostheses automatically and a tablet application verifies completeness, product type and size – enhancing efficiency and patient safety.

Connected Health: Next-Generation Medical Networks

Research focus: Model-based Medicine and Intelligent Operating Room
Team lead: Tobias Pabst

Members: Alexander Prull, Anna Schatz, Christoph Georgi, Clemens Möllenhoff, Gregor Thürk, Ken Paulitz, Misha Justen

The team addresses the demands that increasingly networked medical technology places on communication technology: it identifies areas of application and selects suitable technologies – always with clinical benefit as the starting point and in collaboration with partners from industry and academia. Key areas of focus include mobile communications (5G/6G/campus networks), interoperability standards (HL7 FHIR, IEEE 11073 SDC), augmented and virtual reality, model-based approaches for the smart hospital, and positioning technologies (BLE, UWB, 5G).

In collaboration with the UKL’s clinical departments, including ENT and Trauma Surgery, as well as other clinical departments in Germany, patient journeys are being developed to better understand clinical needs, and demonstrators are being created to bring new technologies to life. Examples include immersive teleconsultations, mobile ultrasound at the point of care using edge computing, wireless sensors for the early detection of healing complications following surgery, and new spatial concepts enabled by intelligent networking within the hospital.

Projects

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  6G-Health Holistic development of high-performance 6G networking for distributed medical technology systems

  High-performance 6G networking for distributed medical technology: in 2025 the infrastructure for tele-neuromonitoring, a testbed with three indoor localization technologies and a “patient room of the future” at the 6G Conference in Berlin were realized.

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  KliNet5G Clinical networks via 5G

  Secure, standards-based 5G campus networks (Open RAN) for hospitals – with measurement campaigns, SDC alarms in network management and assessment of reliability, IT security and economic viability; part of the BMWK-funded CampusOS framework.

• QoE
  QoE Development of a system for Quality of Experience in telemedical data exchange

  Quality of experience in telemedical data exchange: video, vital signs and ECG data are prioritized in real time based on network quality and the clinical context – for example in ambulances.

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  SUSTAINET-guarDian Sustainable Technologies for Advanced Resilient and Energy-Efficient Networks – guided utilities for automation, resilience, and digital innovation in advanced networks

  Secure, resilient and energy-efficient networks for healthcare with AI-supported network monitoring and fault detection; ICCAS is responsible for the medical demonstrator.

Completed projects (1)

• MIRACLE-5
  MIRACLE-5 until 06/2025 Mixed-Reality Integrated Remote Assistance and Communication in 5G-Enabled Local Environments

  XR-based telemedicine via 5G campus networks – for geriatric assessments after hip fractures, ENT teleconsultations, ENT teaching and remote medical advice. In 2025 an open 5G core was installed, test networks were established, and the scenarios were evaluated with the ENT department of Leipzig University Hospital and compared with Wi-Fi.

Digital Patient Model

Research focus: Model-based Medicine and Intelligent Operating Room
Team lead: Dr. Stefan Franke

Members: Max Schreiber, Jean-Baptiste Tylcz, Adrian Lindenmeyer, Erik Schreiber, Nora Grieb, Johannes Keller, Richard Steinert

The “Digital Twins” research group develops methods for digital images of patients to enable AI-supported healthcare. Digitization and interoperability for location- and device-independent access to clinical data play a central role: many AI-based methods require structured patient data, but the lack of standardized interfaces, the high complexity of clinical IT ecosystems and the requirements for data protection and security are major hurdles.

The Medi.NET project creates a network of clinics, research institutions and companies, with a transfer office and a technical platform for virtual twins. In the EU project CERTAINTY, the group develops infrastructure, interfaces and models for personalized cancer therapy – with a virtual twin to support the treatment of multiple myeloma.

Projects

• Medi.NET
  Medi.NET Intelligent Digital Platform for Location- and Time-Independent Medicine of the Future

  Intelligent digital platform for location- and time-independent medicine: interoperable infrastructure based on IEEE 11073 SDC and HL7 FHIR, demonstrator and test environments, and a partner network of more than 80 actors; coordinated by ICCAS.

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  CERTAINTY A Cellular Immunotherapy Virtual Twin for Personalized Cancer Treatment

  Virtual twin for personalized cancer treatment, focusing on multiple myeloma and CAR-T cell therapy; ICCAS implements the virtual twin backend and frontend.

• NFDI4DS
  NFDI4DS NFDI for Data Science

  National research data infrastructure for data science and AI; ICCAS leads the biomedical use cases within the consortium.

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  DaDriv-StAC Digital Platform for Data-Driven Decision Support in Post-Stroke Care

  Digital platform for data-driven decision support in post-stroke care: a virtual medical twin compares the recommended care pathway with the actual course of treatment across sectors.

Imaging Technologies

Research focus: Computer-assisted Image-guided Interventions
Team lead: Hannes Köhler

Members: Annekatrin Pfahl, Denis Gholami Bajestani, Julia Pretschner

The team researches new imaging technologies and methods for non-invasive use in medicine. The focus is on optical procedures for perfusion imaging and tissue differentiation in the context of surgical interventions – from evaluation in the laboratory to the analysis of study data, in close cooperation with several clinics and industrial partners.

The team has many years of experience in spectral imaging, which can support the intraoperative assessment of tissue perfusion and the identification of tissue structures and tumor tissue. To support the transfer to clinical application, methods for the automatic classification and visualization of tissue are developed – using conventional machine learning as well as neural networks. The team supports clinical studies and supervises student theses in medicine, computer science and engineering.

Projects

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  Klinische Relevanz von MSI und HSI Basic studies on the clinical relevance of multi- and hyperspectral imaging – industry-sponsored research by KARL STORZ SE & Co. KG

  Basic studies on the clinical relevance of multi- and hyperspectral imaging (industry-sponsored research for KARL STORZ SE & Co. KG). In 2025 the first clinical study on nerve identification using laparoscopic HSI was published and an animal study on video-rate perfusion imaging was launched; a patent application was filed and a further patent granted.

Interdisciplinary Robotics

Research focus: Computer-assisted Image-guided Interventions
Team lead: Johann Berger

Members: Albrecht Bloße, Manuel Rosenau, Julia Pretschner

Since September 2024, the team has been working on the further development of medical robotic systems by integrating principles of soft and continuum robotics as well as networking and standardization. Robots made of flexible, soft materials are highly adaptable and safe when handling sensitive tissue; continuum robots do not require rigid joints and adapt precisely to complex geometries.

The aim is to make diagnostic and interventional robotic systems safer, more user-friendly and more efficient in order to facilitate their clinical application. The team develops soft end effectors such as gripping or guidance systems and realistic, patient-specific organ models – such as an actuated heart simulator – for planning, training and simulation. At the same time, it develops standardized descriptions of robotic systems and integrates them with other medical devices via IEEE 11073 SDC.

Projects

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  SoKoRoMed Soft and Continuum Robotics for Medical Applications

  Soft and continuum robotics for medical applications: processes for producing patient- and application-specific soft end effectors. The reference use case is an actuated heart model as a training system for transcatheter aortic valve implantation (TAVI); first 3D-printed cardiac and vascular models and an ultrasound test bench for materials were developed and tested.

MR Technology

Research focus: Computer-assisted Image-guided Interventions

Members: Andreas Eger, Till Handel

The project group deals with the development of MR-supported therapy procedures and the transfer of minimally invasive interventions, which are currently primarily performed using X-ray imaging, to the MR environment. The advantages are improved soft tissue contrast and the absence of iodine-containing contrast agents and ionizing radiation – which benefits patients as well as the hospital staff involved. The development of MRI-compatible instruments and devices is a central objective.

Our main focus is on therapy procedures using focused ultrasound (FUS) in MRI: the sound waves can be bundled in a focus approximately the size of a pine nut and used therapeutically without affecting the surrounding tissue. We are particularly interested in low-energy FUS for neuromodulation: it can activate or inhibit ion channels of neurons and thus non-invasively modulate deep brain areas – for example to treat stroke, addiction or obsessive-compulsive disorder.

Projects

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  3MP-FUS Multimodality Multi-purpose Multi-platform Focused Ultrasound – Neuromodulation in rare neuropsychiatric disorders with focused ultrasound

  Multi-purpose, multi-parameter, multi-platform FUS system for transcranial neuromodulation in rare neuropsychiatric disorders. In 2025 the neuro-navigated system was integrated with Localite into the 1.5 T MRI (automatic slice orientation along the acoustic lobe) and transferred to the 3.0 T MRI at MPI CBS, where a neuromodulation pilot on volunteers is being prepared.

• MRgFUS
  MRgFUS Non-invasive Magnetic Resonance Guided Focused Ultrasound in the treatment of uterine fibroids

  Non-invasive treatment of symptomatic uterine fibroids with MR-guided focused ultrasound – with real-time temperature mapping and a very low complication rate.

• Imaging Robotics
  Imaging Robotics Development of an integrated robotic system for the application in focused ultrasound interventions

  Modular control software for robotic arms (KUKA LBR iiwa/Med) in image-guided interventions, connected via IEEE 11073 SDC; winner of the Best Design Award at the Hamlyn Symposium on Medical Robotics 2023.

Smart Clinical Assistance: Decision Support

Research focus: Model-based Medicine and Intelligent Operating Room
Team lead: Maximilian Ferle

Members: Nora Grieb

Our research team focuses on the analysis and modeling of blood cancers such as multiple myeloma (MM) and related diseases such as myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML). In close cooperation with the Department of Hematology, Cell Therapy, Hemostaseology and Infectiology at Leipzig University Hospital, we develop data science and machine learning methods that make precise predictions about disease progression and therapy success – providing physicians with data-driven decision support for individual therapy decisions.

Projects

• VOLTA
  VOLTA Validation Of Model-Based Therapy Decision Support in Hematology

  Validation of model-based therapy decision support in hematology: KAIT, the platform for multiple myeloma developed with the hematology department at Leipzig University Hospital since 2020, is being expanded, tested and optimized for routine practice.

• GenoMed4All
  GenoMed4All

  EU-wide network for exchanging clinical and multi-omics data via federated learning for hematological diseases; ICCAS contributes data integration and standardization based on HL7 FHIR.

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  LENS (ScaDS.AI-Seedfunding) Learning Engine for Novel Survival-clustering (ScaDS.AI seed funding)

  Learning Engine for Novel Survival-clustering: a model-agnostic method that finds prognostically distinct patient groups – pioneered on multiple myeloma data and CT images of non-small cell lung cancer.

Smart Clinical Assistance: Emergency Management

Research focus: Model-based Medicine and Intelligent Operating Room
Team lead: Jan Gaebel

Members: Hector Condori Alagón, Alexander Holzenleiter, Marcel Zill

The group develops digital tools and process assistance for emergency and intensive care – on an interdisciplinary basis, combining technical expertise with medical know-how and in close collaboration with end users such as aid organizations and authorities.

One example is the EOS software (EMT Operating System) in the EU project RescEU EMT, which strengthens civil protection capacities in Europe with mobile Emergency Medical Teams (EMTs). EOS supports mission coordination, patient management and data analysis in crisis situations and is continuously adapted to the needs of emergency services using agile, user-centered methods.

Projects

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  RescEU EOS Emergency Medical Team Operating System

  Field hospital information system: supports patient treatment from triage to discharge, department configuration, key performance indicators and reporting (e.g. to local government or WHO).

• Tele-KAI
  Tele-KAI Creation of a virtual intensive care treatment center through implementation of a tele-intensive care infrastructure

  Tele-intensive care at the Department of Anaesthesiology and Intensive Care at Leipzig University Hospital: makes intensive care expertise available across distances and enables targeted, resource-adapted patient transfers.