Researchers develop new AI tool to better predict stroke risk
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Researchers from the University of Technology Sydney (UTS) have developed an AI model that analyzes a person's DNA to better identify genetic risk factors for stroke, paving the way for more personalized risk assessments.
Stroke is the second leading cause of death worldwide. Around 46,000 Australians have a stroke each year, with one in three survivors experiencing moderate to severe disability, which can include long-term impairment.
First author Dr. Yue (Eric) Yang, who recently completed a Ph.D. in artificial intelligence at UTS, said the AI tool is unique because it not only identifies genetic variants linked with stroke but also considers their location in the genome and how they might interact.
"A stroke happens when blood flow to the brain is cut off or severely reduced, usually by a blood clot, which causes brain cells to die. Genetic susceptibility plays an important role in stroke risk, along with lifestyle factors such as smoking and alcohol consumption.
"We tested our AI model using genetic and health data from the UK Biobank, and also against health records from a large U.S.-based database (All-of-us). Our model was more accurate than previous genetic risk tools at distinguishing people who had suffered a stroke from those who had not."
The recently published study in Computer Methods and Programs in Biomedicine, "Predicting risk of ischemic stroke: A transformer model using genomic data," identified 18 genetic variants associated with ischemic stroke, the most common type of stroke.
The researchers found that people with the highest genetic risk score had a 22% higher chance of stroke compared with the lower-risk group.
The study pointed to the gene EDEM2 in brain cortex tissue as a key contributor to stroke susceptibility, which could provide a specific target for further biological research into the causes of stroke.
The team used a type of AI known as a transformer, which is the technology behind large language models such as ChatGPT, to train the model on thousands of genetic variations and identify links to ischemic stroke.
Distinguished professor Jie Lu, director of the Australian Artificial Intelligence Institute and Australian laureate fellow in the UTS Faculty of Engineering and IT, said the research highlights how AI can be used to develop personalized health care.
"AI can be used to find associations between human genes and disease and build personalized risk assessments to help prevent a potentially life-changing or fatal event," said Lu, who is also the Australian industry laureate fellow in AI-for-Women's Health.
"Genetics is only one part of stroke risk. The ability to combine a genetic score with clinical and lifestyle information, including established risk factors such as blood pressure, age, atrial fibrillation, type 2 diabetes and cholesterol, could enhance monitoring and prevention.
"We are now working on a multimodal AI tool that can combine a genetic risk assessment with other modes of data, including health and lifestyle surveys, scans and imaging, to better assess risk in stroke and other diseases such as endometriosis."
Corresponding author Dr. Kairui Guo has been working with the stroke unit at Liverpool Hospital in Sydney on genomic data analysis, as well as AI applications for acute stroke management, with support from neurologist and co-author Dennis Cordato, an associate professor and senior staff specialist who has provided clinical insights.
"When someone has a stroke, particularly at a young age, they often want to know whether other family members are also at risk. These new AI tools will provide the ability to better help families understand that risk," said Guo.
"We currently have a proposal for a preventive clinical trial. The aim is to recruit the first cohort of patients, sequence their genomes and analyze stroke risk. We are also interested in sex differences in stroke risk between men and women.
"Traditionally, in thinking about stroke risk, genetics hasn't been a main consideration; however, this study highlights the significant impact of genetic factors. Without AI, it would be impossible to process that much data at an individual level to provide a personalized assessment."
More information
Yue Yang et al., Predicting risk of ischemic stroke: A transformer model using genomic data, Computer Methods and Programs in Biomedicine (2026). DOI: 10.1016/j.cmpb.2026.109625
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NeurologyCommon illnesses & PreventionClinical genetics Provided by University of Technology, Sydney Who's behind this story?
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Swati Mestri holds a bachelor's degree in Electronics Engineering and has worked as a content editor since 2019. She has experience editing research documents across technology, health care, and materials science, and has a particular interest in technology and space. Full profile →
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