Medical Systems – Systems-based Medicine Platform
Advances in healthcare lead to more and more complex interconnections between agents and result in larger quantities of data available for analysis. There are risks associated with these developments. The technical possibility of data processing and its promised benefits may shadow the effective cost of data acquisition. However, there are also very significant opportunities. That is why we engage in interdisciplinary research that is focused on the systems-related aspects of new technological developments, and their integration in the clinical environment particularly and the fields of personal - and public health in general.
The Medical Systems – Systems-based Medicine Platform is primarily concerned with the various aspects arising from the integration of technical systems and, more generally, with the analysis of systems from a structural and method-oriented point of view. Systems-based medicine aims to recognise and understand the interdependence of interactions taking place on various different levels (the levels may refer to length and time scales as well as more abstract levels of organization). In a narrow sense, this is motivated by a systems-biology view focusing on disease and therapy in patients. In a broader sense, it encompasses the interactions between patients, therapists, technology, the hospital and the healthcare system. To conduct this research, we develop and deploy technical, mathematical and physical concepts and methods. The platform’s mission is to foster the combination of these approaches in medical systems. The key focus, however, is on the transfer of knowledge and techniques between biomedical engineering and systems-based medicine. Current research results are incorporated into our continuing education courses and the teaching we provide to our students.
Diagnosis and therapy increasingly make use of complexprocedures which combine technologies and methods from experimental sciences, simulation – supported data sciences as well as pure mathematics.. For example, advances in imaging and sensor technology provide access to large quantities of medical data whose meaningful interpretation requires model-based analysis. That analysis requires complex, dynamical systems to be observed at different levels. Specifically, these comprise:
- the patient (and his or her organs, tissue and cells) as a complex biological system
- the technical systems interacting with the patient and the clinicians (imaging systems, medical findings systems and documentation systems used by the physician; electronic implants; life-support systems in acute medicine; human-machine interaction in personalised forms of treatment used in physiotherapy and occupational therapy)
- interaction with data, such as database-assisted systems for initial diagnosis by GPs (with particular focus on dynamic data)
- the clinic and its interacting processes (such as the effect ofcomplex treatment modalities impacting patient workflow) or the healthcare system as a whole
Technical innovations and the new possibilities they bring in their wake are increasingly evaluated from the standpoint of how they can be implemented in the clinical setting. The implementation of new diagnostic or therapeutic processes and technologies often requires a quality-assurance programme comprising technical support tools, changes to workflows and data-transfer procedures as well as a clinical and medical evaluation. The projects currently under way at the ZHAW School of Engineering are already firmly headed in this direction. The ZHAW’s comprehensive expertise inbiomedical engineering, sensor technology, model-based data analysis, modelling, computer simulation and medical imaging provide a sound basis for interdisciplinary research.
- Orthopaedic biomechanics, ergonomics, rehabilitation: Daniel Baumgartner, IMES
- Patient assistive systems, numerical methods in biomechanics: Bernd Heinlein, IMES
- Biosignal processing, wireless data communication: Olaf Hoenecke, ZSN
- Medical robotics and instruments: Hans Werner van de Venn, IMS
- Biofluid dynamics, magnetic resonance imaging: Dirk Wilhelm, MPS
- e-health, m-health, information visualisation: Philipp Ackermann, InIT
- Software engineering, data and text analysis, machine learning: Mark Cieliebak, InIT
- Big data, data warehousing: Kurt Stockinger, InIT
- Data analysis, statistical and deep learning, machine perception: Oliver Dürr, IDP
- Formal methods of knowledge representation: Dandolo Flumini, IAMP
- Numerical and physical radiation dosimetry: Patrik Eschle, IAMP
- Medical biophysics,systems science in medicine: Stephan Scheidegger, IEM
- Model-based data analysis: Mathias Weyland, IAMP
- Complex systems, evolutionary optimisation methods: Ruedi Füchslin, IAMP
- Industrial and organisational psychology, human factors: Céline Mühlethaler, ZAV
The intricacies of the human shoulder are not yet fully understood. In order to gain new insights into its various functional mechanisms, the <st1:placename w:st="on"><st1:place w:st="on"><st1:placetype w:st="on">Institute</st1:placetype></st1:place> of <st1:placename w:st="on">Mechanical Systems</st1:placename></st1:placename> at the ZHAW School of Engineering developed a physiological shoulder simulator. The simulator can also be used as a dummy for conducting stability tests on implants and prosthetic devices.
Patients suffering from conditions affecting the central nervous system are often unable to walk independently. Often, repeated and intensive training is needed for them to learn to walk again. Such training can be provided by automated gait trainers. However, because these automated devices are comparatively expensive, institutions such as clinics and nursing homes are often unable to buy them. Is it possible to develop an automated gait trainer which is cost-efficient, safe and effective?
Researchers and students at the ZHAW School of Engineering are developing an innovative new method for diagnosing skin cancer. Instead of resorting to preventive surgical intervention, an infra-red camera is used to examine skin lesions without touching the patient. Two ZHAW graduates have helped to develop clinically viable diagnostic apparatus designed to identify malignant changes in images of the skin.
Effects of different DNA damaging agents have been well characterized in hu-man cells in vitro, but little is known about the kinetics of DDR in in vivo. This is due to the fact that repeated sampling of tissue is difficult. Herein, fine needle aspirate (FNA) technique was adapted as a minimally invasive sampling method to address cellular response to DNA damaging agents in vivo (dogs). Investigated end-points are quantification of induced DNA damage, time course (kinetics) of damage formation and repair, residual damage, and functionality of specific DNA repair pathways.
- Institute of Applied Information Technology (InIT)
- Institute of Applied Mathematics and Physics (IAMP)
- Institute of Data Analysis and Process Design (IDP)
- Institute of Mechanical Systems (IMES)
- Institute of Mechatronic Systems (IMS)
- Institute of Embedded Systems (InES)
- Institute of Computational Physics (ICP)
- Institute for Signal Processing and Wireless Communications (ISC)
- Centre for Artificial Intelligence (CAI)
- Centre for Aviation (ZAV)