AI sheds light on why stroke recovery differs

The health of a person's brain before a stroke may help explain why people with similar strokes can experience very different recoveries, according to ongoing research at Waipapa Taumata Rau, University of Auckland.

By combining artificial intelligence with MRI brain scans, researchers hope to develop tools that help doctors predict recovery earlier and tailor rehabilitation plans for stroke patients.

Leading the project is Simi Sasidharan, a PhD student with an extensive background in IT. Using advanced image-processing techniques, the research investigates how brain conditions before and after a stroke influence recovery.

Stroke damage alone may not tell the full story, the study finds. Patients with similar-sized lesions in the same brain region can experience very different outcomes.

Senior Lecturer and Postgraduate Director of Medical Imaging Dr Sibusiso Mdletshe says the findings are helping researchers better understand the factors that influence recovery after a stroke.

"The patients could have a similar lesion in the brain, but the health of that brain is different," he says.

The project brings together expertise in medical imaging, artificial intelligence and neurology. Working with MRI scans from hundreds of stroke patients, researchers are creating detailed three-dimensional maps of affected brain pathways and training AI models to identify patterns that may predict recovery.

Researchers are using the technology to build a clearer picture of how stroke injuries affect different patients.

"In simple terms, AI helps us better understand what is happening within the stroke lesion in a patient's brain," he says. "It allows us to map how that lesion is behaving and how it may affect the patient's recovery in the future."

The study analyses data from the Stroke Motor Rehabilitation and Recovery Study at Massachusetts General Hospital in Boston, United States. Researchers collaborated with Dr David Lin, a critical care neurologist and neurorehabilitation specialist, and Julie DiCarlo, programme manager for the hospital's Laboratory for Translational Neurorecovery.

Mdletshe says the team's focus is now on translating the research into a clinically useful tool.

"If the tool we're developing is able to do what we intend, it will help physicians predict how a stroke will behave and support the recovery plan for that patient," he says.

The project forms part of a wider stroke AI imaging research programme led by Associate Professor Alan Wang from the Auckland Bioengineering Institute. Mdletshe is the principal supervisor of the PhD research, with Associate Professor Wang serving as mentor and co-supervisor.

The research is already drawing attention beyond New Zealand. The PhD project is more than halfway through and has resulted in conference presentations in New Zealand and overseas, as well as published research.

The team hopes future studies will include New Zealand imaging data, helping researchers understand whether the patterns they are seeing internationally are reflected here.

Further validation is needed before any AI tool can be used in clinical practice and researchers continue to test the approach with larger datasets.

"The view is that this will impact the care of stroke patients in a significant way in the future," Mdletshe says.

The project reflects his wider interest in using medical imaging to improve patient care.

He has also co-led, with Associate Professor Peter Jones, the development of the University's new Postgraduate Certificate in Health Sciences specialisation in Point of Care Ultrasound (POCUS), which will welcome its first students in 2027. Developed in response to growing demand for clinician-performed ultrasound training in New Zealand, the programme will help healthcare professionals build bedside imaging skills that support faster clinical decision-making.