Digitization and interoperability for location and device-independent access to clinical data will be more firmly established in medicine. The greatest challenges at present are the lack of standardized, interoperable interfaces, the complexity of the clinical IT ecosystem, and the requirements posted by inevitable data protection and security measures.
In the MediNet project, the necessary interconnections between clinics, research and companies are being established. To this end, ICCAS runs a Transfer center to assist regional health care providers and companies with organizational and technical challenges when introducing novel products into the medical environment. Beyond that, ICCAS scientists are developing a technical platform that facilitates clinical studies and innovative treatment strategies with networked medical products. Based on established international standards and new technologies, the entire spectrum, ranging from mobile point of care devices to clinical information systems, can be addressed.
In cooperation with regional companies, the next generation of medical products will be integrated into clinical processes and digital infrastructures in the university hospital. This will also lay the foundation for new, more flexible solutions for location-independent, integrated medicine beyond the project period.
The 6G-Health project brings together the domains of communications engineering, medical engineering with medical and technical end users to enable precisely tailored technology development in the field of sixth-generation mobile communications (6G). This involves not only developing specific 6G technology components, but also identifying market entry barriers at an early stage and developing possible countermeasures. These include, above all, the aspects of licensing, operation and standardization. At the technical core are developments in the area of sensor integration in 6G, the development of technologies for enhanced network intelligence, and concepts and technologies for intelligent distribution of computing resources and efficient pre-processing of data at various levels of the infrastructure. From a medical perspective, representative applications from three areas will be addressed: The acquisition of biosignals directly at the patient and their transmission, the use and processing of data and information to improve a collaborative work environment, and applications of 6G in the field of Smart Hospital with the aim of recording and optimizing intraclinical processes.
Subproject – Leipzig University Medical Center:
Leipzig University Medical Center (UML) is developing ideas and concepts for the field of 6G research in medical technology applications. Technically, the main focus is on the investigation of application scenarios for the use of 6G technologies in clinical processes and their optimization. 6G can make a major contribution by providing a flexible, energy-efficient and high-performance infrastructure. The use of advanced sensor technology in clinics including the possibility of Joint Communication and Sensing (JCAS) as well as distributed processing intelligence opens up various advantages while significantly reducing power consumption. The medical applications addressed in the project are representative of different classes of medical applications. They are intended to highlight a wide range of future challenges and opportunities, which can be discussed with communication engineering experts via the 6G platform. In the project, the UML assumes the role of a mediator between the domains of medicine, communications engineering and medical engineering.Here, in the sense of a holistic understanding of the project, not only the technical applications are considered, but also the requirements for the future technologies through standardization and operation. To this end, the work is closely interlinked with players from the various domains and with experts from the areas of approval, operation, standardization and standardization.
The aim of the CortexMap project is the development of a novel navigated transcranial magnetic stimulation (nTMS) system for non-invasive pre- and post-surgical mapping of the motor cortex of the brain of patients with brain tumors. The new system is expected to offer an efficient use in neurosurgery and to be optimally integrated into the surgical workflow.
For this purpose, necessary hardware components as well as new software functionalities will be developed. An electromyography device with 8 or 16 electrodes for the measurement of motor evoked potentials (MEP) will enable faster and more precise examinations. Functionalities to automatically adjust the intensity of the stimulation and post-process the MEP will lead to accurate mapping of the motor cortex. New visualization and data analysis features will support the surgeons for the interpretation of the measurements too.
Therefore, the monitoring of patients before and after surgical treatment with this new non-invasive and simple measurement system, will become more efficient for the benefit of the patient.
The SDC-VAS research project aims to develop a new ‘distributed alarm system’ for use in intensive care units based on the new IEEE 11073 SDC standard family. The primary goal is to reduce alarm fatigue and noise pollution in intensive care units.
The SDC family of standards is a new communication protocol that allows for communication between medical devices from different manufacturers. In doing so, these devices can provide data, status and services in an electronic network. This information can also be used by so-called value-added systems.
The distributed alarm system should receive the information from the various SDC-enabled devices and aggregate and evaluate it together with data from other sources such as the clinical information system (CIS) and IoT sensors and then forward said information to the appropriate nursing staff. In addition, we propose to explore alarm prediction possibilities using pattern recognition algorithms.
The project faces three core challenges: First of all, an integrator able to meaningfully link the data from the SDC interface, from the CIS and from various IoT sensors needs to be developed. Secondly, a meaningful methodology to select and inform a suitable employee needs to be established. Finally, the question to what extent the legal and normative regulations have to change in order to be able to use an SDC-based distributed alarm system safely needs to be answered.
The project, which is a cooperation between the company tetronik, the University of Leipzig represented by ICCAS and the HTWK Leipzig, has started in August 2022 and is funded by the ZIM program of the Federal Ministry of Economics and Climate Protection.
Funding measure „Recognizing and treating mental and neurological diseases Using the potential of medical technology for a better quality of life“
|Project title:||3MP-FUS: Multimodality Multi purpose Multi plattform Focused Ultrasound – “Neuromodulation in rare neuropsychiatric disorders with focused ultrasound. ”
Prof. Andreas Melzer
Funding amount/donation for the MF/UL: 5.2 Mio € (6.2 Mio € including fixed rate)
|Project duration:||01.04.2022 bis 31.03.2025|
Clinically available MRI-guided FUS systems (MRgFUS/MRHiFU) are dedicated and approved only for specific indications (not to the diseases mentioned) and are approved only for specific MRI systems. In addition, they are permanently installed in one single MRI scanner. Flexible and cost-effective use of these FUS systems on other MRI scanners is currently not possible, in contrast to the planned 3MP-FUS system. One System to be used both under Ultrasound guidance and MRI guidance is not yet available.
The objective of this project is to demonstrate feasibility of such a multi use FUS System which we have developed for neuromodulation.
Neurological diseases are often accompanied by focal changes in the brain. Therapy options mainly aim to normalize the altered brain function, e.g. by neuromodulation. Deep brain stimulation (DBS) is in use for this purpose. DBS requires neurosurgical invasive intervention with the possibility for complications. An alternative would be non-invasive electromagnetic stimulation techniques, such as transcranial magnetic stimulation (TMS). However, these have a low spatial focus without reaching the relevant deep structures of the brain.
The BMBF funded project 3MP-FUS aims to optimize the neuromodulation adressing the two orphan diseases Dystonie and young onset Parkinson’s syndrome. Both benefit from invasive DBS but face the disadvantages of invasiveness. Dystonia (incidence of 20:100,000 (ORPHA:68363)) is characterized by spontaneous involuntary muscle movements. This disorder is usually treated with medication; in advanced stages, therapy with DBS is indicated. The target region is the globus pallidus internus. DBS is also used in the rare early adult Parkinson’s syndrome with an incidence of 1.5:100,000 (ORPHA:2828), where the target region is usually the Nc. Subthalamicus.
The further goal of the project is the ongoing development and testing of a multi-modal, multi-parameter, platform-independent focused ultrasound system (3MP-FUS) for neuromodulation in dystonia and rare forms of Parkinson’s disease. 3MP-FUS will be integrated into different MRI and PET/MRI platforms for precise targeting of circumscribed brain regions and altering their function.
The approach proposed here has the potential to significantly improve neuromodulation. For brain research and novel therapy, the 3MP-FUS device will open up applications similar to and beyond TMS.
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.