Technische Universität Kaiserslautern of Kaiserslautern at MEDICA 2022 in Düsseldorf -- MEDICA - World Forum for Medicine
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07 Nov 2022

New technology simplifies and enhances the analysis and visualization of medical image data

Medical imaging generates a lot of data, for example during computer tomography. This data important when it comes to personalised medicine. Artificial intelligence methods, such as machine learning, use this data to learn and help tailor diagnoses and therapies to individual needs in the future. However, such systems are currently still burdened with uncertainties. A team of researchers from Kaiserslautern and Leipzig is working on a system that automatically analyses and visualises medical data, including their uncertainties. The researchers will be presenting this technology at the medical technology trade fair “Medica” in Düsseldorf held from 14 to 17 November at the Rhineland-Palatinate research stand (stand E80, hall 3).

In the event of a stroke, speed is of essence. Using computer tomography (CT) scans, doctors can quickly determine the position of the blood clot in the brain and what treatment is appropriate. Such imaging procedures play an important role in medicine. They are also used in other areas, for example prior to operations. Magnetic resonance imaging (MRI) scans help surgeons plan an operation before it is performed.

What all these techniques have in common is that they generate a lot of data. “Analysing and visualising this data automatically is an important step toward personalised medicine,” says Dr. Christina Gillmann, a computer scientist at the University of Leipzig. “This area has gained a lot of importance in recent years.” AI processes such as machine learning and neural networks make this possible. These networks learn on the basis of data with which they are trained or “fed”. They learn, for example, from CT image data a doctor has previously processed. In this way, technical information, but also medical experience, is incorporated. The rule is that the more data these methods can evaluate, the better the results will be.

In a few years, such technologies have the potential to be used in everyday clinical practice, for example, to enable personalised diagnoses and therapies. However, they are still in the early stages of development. “Each medical case has to be trained individually. The data must be prepared individually in advance, which is very time-consuming,” explains Robin Maack from the Computer Graphics and Human Computer Interaction working group at Technische Universität Kaiserslautern as a problem. For example, doctors have to “label” the data for each medical case individually. “This means, for example, that if a network is to train to automatically recognise a tumour, hundreds of images with known tumours have to be hand-drawn in so that the neural network has a basis with which to learn,” Gillmann explains.

Maack continues: “In addition, there are no standard interfaces with which trained networks can be handled, loaded and used. But also when there are uncertainties in the data layers; be it training data sets or models used; there are no standardised guidelines for how medical professionals should deal with that.”

Such uncertainties occur, for example, with lesions. During a stroke, certain areas of the brain are no longer supplied with sufficient oxygen, or not at all, due to the blockage of vessels in the brain. They are no longer able to work efficiently. The core of the lesion is often easy to recognise, but at the edge there is usually no clear demarcation and regions where even doctors cannot agree whether they should be classified as a lesion or not. Ultimately, what is needed here is medical experience on how to deal with these issues.

This is where the focus of Gillmann and Maack's work starts. Their team is currently developing a uniform system for processing and evaluating medical image data and visualising their uncertainties. The system is called GUARDIAN. The researchers have designed their technology in such a way that it is easy to use. “Clinics can load their trained neural networks and combine them with the processed data provided, for example, in the case of a stroke”.

The system evaluates the data and visualises the results. “This happens automatically, without the need for IT knowledge,” Maack continues. “In addition, our technology also shows the uncertainties.” This means that the doctors can look at them again and, if necessary, make a joint decision on what is the best treatment in an individual case, for example. 

The system is freely available as an open-source application. The two computer scientists will be presenting this system at the fair.

The “Computer Graphics and Human Computer Interaction” working group of Professor Dr. Hans Hagen at TU Kaiserslautern has been researching for a long time to prepare data from imaging procedures for medicine in such a way that it can be used simply and reliably in everyday clinical practice.

Klaus Dosch, Department of Technology and Innovation, is organizing the presentation of the researchers of the TU Kaiserslautern at the Medica. He is the contact partner for companies and, among other things, establishes contacts to science.
Contact: Klaus Dosch, E-mail: dosch@rti.uni-kl.de, Phone: +49 631 205-3001

Questions can be directed to:
Robin Maack
Computer Graphics and Human Computer Interaction
TU Kaiserslautern
Phone: +49 631 205-3268
E-mail: maack(at)rhrk.uni-kl.de

Christina Gillmann
Image and Signal Processing Group
Universality of Leipzig
Tel: +49 341 97 32281
E-mail: gillmann(at)informatik.uni-leipzig.de

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07 Nov 2022

Artificial intelligence method allows personalised diagnosis of back problems in future

Back problems are generally regarded as a widespread disease Finding the exact cause of these problems is often difficult. A team of researchers from TU Kaiserslautern (TUK), the University Medical Centre in Mainz and several companies is working on a method that will enable more efficient monitoring of malpositions and strains on the back. Artificial intelligence (AI) methods are also being used to help analyse the spine individually. The team will be presenting its project at the Rhineland-Palatinate research stand (stand E80, hall 3) at the Medizintechnikmesse (Medical Technology Trade Fair) in Düsseldorf from 14 to 17 November.

Too little exercise or exercise that is too one-sided, too much sitting at a desk and in front of a computer, not only at work but also in private life. The consequence that follows: Many people have back problems. Yet there are many proven preventive measures, such as courses in back exercises or relaxation methods, which are usually also offered and reimbursed by health insurance companies.

“But all this is of little use if the cause of the pain is not clearly defined,” says Carlo Dindorf, a scientist in the Department of Sports Science at TU Kaiserslautern. This is precisely what the TUK team is working on together with Jürgen Konradi and the research team of the Interprofessional Study Centre of Motion Research at the University Medical Center of the Johannes Gutenberg University in Mainz, the medical technology company DIERS International GmbH and other project partners.

The interdisciplinary team is relying on a diagnostic technique that is already well-tested and widespread in practice. “We scan the back with a projector and a camera unit,” says Dindorf. This involves projecting a grid of light onto the back. Using so-called raster stereography, an individual model of the spine can thus be generated. A new aspect of the method is the use of AI and machine learning methods. “Our system learns with the help of the data obtained,” explains Dindorf. “The more spines are analysed, the better the system and thus our understanding of the spine improves.”

To further improve the learning of these systems in the future, the team is working on enabling multi-centre, data collection for the common good. “For this purpose, we want to combine data from different measurement centres,” explains Dindorf. “All those involved in the therapy, such as doctors and physiotherapists, can upload their movement data to the resulting platform. This allows an objective, data-based assessment of the clinical picture to be generated afterwards using the AI pipelines that have been developed.”

This knowledge can help medicine in the future, for example, to better detect malpositions and to provide personalised diagnoses that enable individualised therapy. But the technology is also of interest for professional and amateur sports as well as for basic research in general. The result is a much more differentiated picture and better insight into the function of the spine.

Offene Digitalisierungsallianz Pfalz (Open Digitalisation Alliance Palatinate) is also involved in the project. They support the research team in translating the findings into practice in collaboration with other researchers, with stakeholders from the health sector and with entrepreneurs in the region.

Offene Digitalisierungsallianz Pfalz
Offene Digitalisierungsallianz Pfalz is a joint project of Kaiserslautern University of Applied Sciences, Technische Universität Kaiserslautern and the Fraunhofer Institute for Industrial Mathematics (ITWM). The project enhances the transfer of ideas, knowledge and technology with industry and society and is based on a cooperation strategy of the two universities. Offene Digitalisierungsallianz Pfalz is funded by the Federal Ministry of Education and Research within the framework of the federal-state initiative “Innovative Hochschule”.

Klaus Dosch, Department of Technology and Innovation, is organizing the presentation of the researchers of the TU Kaiserslautern at the Medica. He is the contact partner for companies and, among other things, establishes contacts to science.
Contact: Klaus Dosch, E-mail: dosch@rti.uni-kl.de, Phone: +49 631 205-3001

 Questions can be directed to:
Dr Carlo Dindorf and Prof. Dr Michael Fröhlich
Research area; Sport Science - Department of Exercise and Training Science
TU Kaiserslautern
E-mail: carlo.dindorf@sowi.uni-kl.de
Phone: +49 631 205-5172

Dr Jürgen Konradi
Interprofessionelles Studienzentrum für Bewegungsforschung [Interprofessional Study Centre for Research on Movement]
Johannes Gutenberg-Universitaet Mainz
E-mail: juergen.konradi@unimedizin-mainz.de
Phone: +49 6131-17-2362

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