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.
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: firstname.lastname@example.org.
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.