Im BMWK geförderten Projekt KliNet5G wird die Umsetzbarkeit einer rein 5G-basierten Netzinfrastruktur auf Basis von OpenRAN in Kliniken evaluiert. Das Projekt verbindet Enduser-Equipment-Hersteller, Klinikbetreiber und medizinische Anwender. Es werden unter anderem Konzepte für die zukünftige Ausgestaltung der Infrastruktur und die damit einhergehenden Veränderungen von Arbeitsabläufe entwickelt. Außerdem werden praxisnahe klinische Anwendungen der Logistik und Patientenversorgung kombiniert und umgesetzt, um damit Prozesse in der Klinik zu flexibilisieren und kontinuierlich zu optimieren. So kann z.B. mobiles Patientenmonitoring sowie Tracking von Geräten und Equipment praktisch realisiert werden. Das Projekt zielt darauf ab, vorhandenes 5G-Knowhow und 5G-Technologie in vorhandene Produkte und Anwendungsgebiete der Medizin zu integrieren um die Anwendung dieser Zukunftstechnologie zu unterstützen.

Efficient health care requires data originating from various sources of the clinical environment that are intuitively usable and semantically linked. In reality, however, clinical data is often loosely structured and stored in continuous text or raw data. The research and development of a digital patient model (DPM) to tackle said problems is part of the MPM project (Models for Personalized Medicine) at ICCAS. MPM focuses on semantic data integration and multimodal data analysis. The GAIA-X digital patient model project serves as complementary research for MPM to extend possible applications of the DPM. The aim is the development of concepts to integrate the technology of a DPM into the GAIA-X ecosystem and, thereby, share pseudonymized population-based data, trained models and analysis modules between institutions and countries inside the EU.

The research project SDC – Control Station Med (SDC-CSM) aims at integrating the new communication standard IEEE 11073 SDC – which offers an open, safe and multivendor-capability interconnectivity between medical devices – into a novel control station. The control station then allows the personal of a medical-technical department of a clinic to access aggregated data on the current state of all attached SDC systems. Additionally, SDC-CSM shall provide the documentation of error messages, the handling of errors with automatically specified reactions and the survey of performance numbers. The project will review the possibility and integration of predictive maintenance algorithms. The research centers on the expansion and advancement of SDC standards, data model and data aggregation, and machine learning algorithms.

Tissue perfusion and moisture are important physiological parameters that reflect the healthy state of patients and are therefore measured for patient monitoring. Problems, such as incorrect drug concentration, pulmonary complications and inefficient oxygen therapy, can be early detected based on the parameter values. Currently, the standard methods, such as pulse oximetry and transcutaneous electrodes, have limitations especially for an application to premature babies. The devices are in contact with the body and measure the local perfusion.
The goal of the MultiGuard project is the development of a contactless and non-invasive multispectral system to support the diagnosis of patient complications. Multispectral imaging combines the principles of photometry with digital imaging and does not require any contrast agent. The system includes a multispectral measurement unit and image processing tools to compute continuously perfusion and pulsatile parameters, fat and water content from the measured absorption values. The light source unit will be made of switchable LEDs, not to disturb the patient with continuous visual light. The physiological parameters have to be delivered at video rate and quality. The visualization has to be optimal to warn the medical staff in case of complications.
At the end of the project, a prototype of the developed system will be evaluated at the intensive care unit and neonatology department.

Minimally-invasive endoscopic surgery is a well-established surgical practice. However, decoupled hand-eye-coordination, limited field-of-view and operating space as well as decreased depth perception, are demanding for both surgeon and equipment. Faced with this complex intraoperative environment, surgeons are required to train their spatial awareness and instrumentation skill from training and live operations. Since training effects on spatial cognition and orientation capabilities vary individually, the quality of laparoscopic training with physical and virtual simulators is dependent on the predisposition of trainees. The training effectiveness and a potential skill transfer to the operating room is generally not predictable.

As a consequence, the purpose of this project is the development of a novel training assistance systems that acquires a continuous multimodal representation of a trainees’ individual laparoscopic exercises to predict the current and overall training progression and, in response, provide aural and visual feedback cues. A physical simulator extended with multiple sensor components will be used to generate a knowledge base of basic bimanual laparoscopic skills. Training progression and quality, currently assessed through subjective skill questionnaires, will be extended through the introduction of objective, machine-readable metrics as a form of unbiased description of laparoscopic expertise.

The European medical response system is comprised of first responder units that are operating quickly and lightly. On occurring disasters (e.g. earthquakes, tsunamis, floods, etc.), these Emergency Medical Teams (EMT) are deployed on disaster relief missions to support the local medical system and avert humanitarian crises.
The “EMT Operating System” (EOS) is a field hospital information system, which is tailored to the requirements of EMT on disaster relief missions. Its idea was created and designed during the EUMFH-Project. The system supports the whole patient treatment process from triage to discharge and is highly configurable to adapt to the needs of the EMT. Despite EOS being primarily designed as an electronic patient record, it also includes essential functions for EMT mission and field hospital management. Besides patient management and treatment documentation, EOS enables quick department configuration, visualization of important hospital key performance indicators (patient admissions, triage category count, department workload, etc.) and reporting functionalities (e.g. to local government or WHO). Thus, EOS plays an essential role in monitoring and assessing the current situation and performance on a strategic and tactical level.
EOS provides highly customizable functionalities. They can be adjusted to the specific frameworks of different EMT entities or other requirements by specialized teams, e.g. Burn Assessment Teams. Generally speaking, EOS includes digital documentation and management of the usual processes within an EMT. However, detailed characteristics can differ.
EOS relies heavily on structured data entry and storage (in contrast to free texts). This ensures high information quality and supports fast and easy data input as well as automatic information aggregation in databases. The latter benefits the reporting obligation and allows for comparison between different missions or EMT installations.
The system is under continuous development in close collaboration with different first responder organizations. It will be free of charge for civil first responder organizations. Designed as a web application, EOS can be used with modern browsers (e.g. Chrome, Firefox, etc.) and can be utilized easily on PCs, laptops or touch devices like tablet pcs or smartphones. You are interesting in using EOS or wanting to try it out? Then contact us: eos@iccas.de.

See the EPR and Technology report

The medical field of hematology is characterized by highly heterogeneous diseases and disease courses. Nevertheless, clinical trial design, drug development and subsequent therapy are mostly based on the administration of identical therapeutic regimens. As treatment strategies become more precisely tailored to patients, this process becomes more effective, but at the same time causes an enormous amount of complexity in the information that must be considered.  Thus, clinical decision-making also depends on whether the treating physician has the appropriate therapeutic experience and access to novel therapies. The goal of KAIT, an artificial intelligence-based platform for therapeutic decision support for patients with myelodysplastic syndrome, acute myeloid leukemia and multiple myeloma, is to support the clinical decision-making process by providing and evaluating relevant information to enable patient-specific and personalized treatment for all patients.

Link to project website (German)

The goal of the project is the development of a mobile EIT-ECG combination system. With its help, a non-invasive monitoring of a patient’s respiratory activity and heartbeat shall be realized, while employing only one wearable, performing both measurements. Users, regardless of their technical background knowledge, should be able to perform non-invasive examinations of heart and lung functions in a very short time. The relevant vital parameters, such as lung activity, regionally resolved ventilation visualization, relative tidal volume, respiratory rate or heart rate, should be presented in an understandable way without invasive methods. One of the main components of the system will be an electrode attachment that can be used on patients of different sizes.

Annually more than 270,000 people suffer from acute strokes in Germany, out of which roughly 200,000 experience it for the first time. Further consequences follow after the acute event for the majority of the afflicted patients, including physical handicaps and emotional distress. This often leads to the stroke evolving into a chronic illness.

In order to support patients in the often difficult time after the stroke, the PostStroke Manager represents a patient-oriented, digital system that uses common everyday communication channels in order to enable a coordinated preventive long-term care of stroke patients. The system integrates stroke pilots (Schlaganfalllotsen), patients and general practitioners as well as mobile sensors (so-called wearables) and, thus, creates the basis for innovative digital services, new forms of care and a structured disease management program for strokes. It serves as a supplement to stroke pilot care from the first year on after the acute event, but can also be used in areas without an established program of that kind directly after the incident. In addition, the PostStroke-Manager can help with secondary prophylaxis matters by i.e. reminding the patient to take their medications and – provided that the patient enabled the function – recording important parameters like heart rate or systolic and diastolic blood pressure via Bluetooth blood pressure monitors.

Link to project website

The project aims to develop a non-invasive, multimodal monitoring system that will enable first responders to obtain informative objective feedback on the quality of executed cardipulmonary resuscitation. Regardless of their technical or medical background knowledge, users should be able to quickly bring the system into an operational state, attach it to the patient’s body and receive an evaluation of the resuscitation activity performed. The user should be shown the qualitative changes in the arterial blood flow in the neck that occurs as a result of thoracic compressions. In addition, several preclinical measurements on the phantom are to be used to establish ranges for measured values in the laboratory environment, which will support the user in assessing the displayed blood flow values.