Our researchers develop AI tools both for diagnosis and for use in the rehabilitation process of patients with cerebral stroke.
Associate Professor Mahdieh Khanmohammadi has been working on a project involving image analysis of acute stroke. The goal has been to develop an AI tool that can help doctors make better and faster decisions when a stroke is suspected. PhD students Luca Tomasetti and Liv Jorunn Høllesli worked on the so-called twin project until spring 2024.
Luca Tomasetti's part of the twin project mainly involved developing new automatic methods for image diagnostics using machine learning. He used images from CTP scans (computed tomography perfusion) as input for an artificial intelligence network that can segment areas in the brain with reduced blood supply. In other words, to identify the areas in the brain that should be treated for stroke.
Liv Jorunn Høllesli analyzed the relationship between damaged tissue at patient admission and after treatment. The project continues with a new PhD student at UiS, Sazidur Rahman. He will continue working on the machine learning part of the project.
Detects cerebral stroke using artificial intelligence
Khanmohammadi has recently been contacted by a group of researchers from Aberystwyth university in Wales, UK and research partners in Istanbul, Turkey who are interested in collaboration on image analysis of stroke. They collect patient data after the stroke has occurred, for the rehabilitation of the disease itself. So far, Khanmohammadi's research has focused on what can be done to simplify the diagnosis of a stroke. Now they are ready for phase two – namely artificial intelligence to assist in the treatment of stroke. She recently visited Istanbul to meet with the collaborators and visit the clinic where they collect data.
Another ongoing research project concerns Parkinson's disease. Here, researchers use artificial intelligence to study the progression of the disease. The goal is to develop good tools for neurologists. However, there are challenges in implementing AI tools in healthcare due to costs. Mahdieh Khanmohammadi emphasizes that they are still in the development phase and that the long-term goal is clinical implementation.