Working to reach new diagnostics

​Research to develop new techniques for diagnostics is found all over Chalmers. Read about some examples here!​
​These examples are linked to a main article published here.

Combating antibiotic resistance

Erik Kristiansson at the Department of Mathematical Sciences has developed algorithms to analyse patterns in bacterial DNA. This can pinpoint changes that lead to resistance to antibiotics, thus increasing the chances of effective treatment. 
In partnership with Kristina Lagerstedt and Susanne Staaf, Kristiansson founded 1928 Diagnostics, whose cloud-based software analyses the genetic code of bacteria and provides information about its spread and treatment options.

Fredrik Westerlund at Biology and Biological Engineering studies the DNA molecules, called plasmids, that primarily cause the rapid spread of antibiotic resistance. To identify plasmids, the scientists attach “bar codes” to them. In combination with the CRISPR gene-editing tool, they can also identify the genes that make bacteria antibiotic resistant. Now the method has been further developed to identify the actual bacterium, which is important as different types of bacteria cause infections of differing severity.

Caption to picture above: Fredrik Westerlund studies the DNA molecules that primarily cause the rapid spread of antibiotic resistance. Here with colleagues Gaurav Goyal and Vinoth Sundar Rajan.

Diagnostics using microwaves

Microwaves make it possible to detect patterns that can be used for diagnostics, by passing weak microwave signals through the body and processing them. The pattern created is analysed using algorithms for image reconstruction or AI-based classification.
Researchers in the Department of Electrical Engineering, along with Sahlgrenska University Hospital and other partners, are applying these methods to stroke diagnostics and mammography. The technology makes it possible to build small, mobile units, which make it easier to make a fast, early diagnosis – which is particularly critical when diagnosing a stroke. 
The so called “stroke helmet” developed by the research team can be used in an ambulance to determine, even before the patient arrives in hospital, whether a stroke was caused by a blood clot or a haemorrhage. This reduces the time to treatment, allowing more stroke patients to recover with fewer aftereffects. 
“Many factors indicate that microwave technology has the potential to be a highly efficient diagnostic tool,” says Andreas Fhager.

Caption to picture above: Andreas Fhager and the “stroke helmet”, which can determine whether a stroke was caused by a blood clot or a haemorrhage.

AI and diagnostics

Artificial intelligence can provide significant help in making healthcare decisions, and several AI projects are under way at Chalmers.
Robert Feldt, Professor of computer science, and Marina Axelson-Fisk, Professor of mathematics, are working with the Clinic for Infectious Diseases at Sahlgrenska University Hospital in a project about sepsis – blood poisoning. Rapid diagnosis and treatment are critical for survival, but modern screening tools have low precision. The aim of the project is to help doctors to make the right diagnosis faster through the use of AI. The method they are developing can also be tested on other diagnoses, and this spring the researchers have particularly looked at whether it can be used on Covid-19.

Another field where AI support has potential is in the analysis of medical imaging, in which computers learn to interpret radiological images of human organs. Fredrik Kahl’s research team at Electrical Engineering has partnered with Sahlgrenska University Hospital to develop an AI-based method of assessing tomographic images of the coronary arteries. Cardiovascular diseases are still the most common cause of death in Sweden and worldwide. An AI assessment not only has the potential to be just as accurate as a human, but also goes much faster and is more consistent once the computer has been fully trained. 
In the next step, AI can help to discover hitherto unnoticed connections and patterns, and thus contribute to creating new medical knowledge.

Caption to picture above: Fredrik Kahl is a professor in the Department of Electrical Engineering. His research team is developing AI to diagnose medical imaging.

Identifies disease before symptoms arise

Rikard Landberg at the Department of Biology and Biological Engineering works in the field of metabolomics, an extensive analysis of molecules in biological samples such as blood plasma. Factors that affect health – genetics, lifestyle, environmental pollutants, medicines – make their mark on the metabolome, the pattern of tiny molecules in the sample. By measuring these indicators and relating them to health parameters and diseases, scientists can study the impact of various factors, as well as learning about underlying mechanisms. Research is also under way to find biomarkers that can identify diseases such as cardiovascular disease, type 2 diabetes or cancer.

Caption to picture above: Biomarkers in blood samples can give information on the risks of developing common illnesses.

Fast and accurate influenza test

At the Department of Microtechnology and Nanoscience, Dag Winkler and his colleagues are building a small portable device that will be able to diagnose influenza in less than an hour, eliminating the need to send the sample to a lab for analysis. Getting the test results within an hour means that patients with contagious diseases can be isolated in time. The research project is being carried out in collaboration with several partners, including Karolinska Institutet.
The project is focused on influenza diagnostics, but the team say the equipment can also be used to diagnose other diseases, such as malaria, SARS or Covid-19. In the past year, the research team has improved the sensitivity of the device to such a degree that they have applied for a patent and are looking into commercialisation.

Texts: Mia Malmstedt and Malin Ulfvarson

These texts are republished from Chalmers Magasin no.1, 2020 (in Swedish).


Published: Thu 25 Jun 2020.