New AI Model Unveils Genetic Stroke Risks in Australia
Australian researchers have created an AI tool that analyzes DNA to pinpoint genetic factors contributing to stroke risk.
The AI model identifies 18 genetic variants associated with ischemic stroke.
Individuals in the highest genetic risk category face a 22% increased likelihood of stroke.
Future developments will integrate genetic data with health and lifestyle information for enhanced risk assessments.
On October 18, researchers from the University of Technology Sydney (UTS) announced the development of a groundbreaking artificial intelligence (AI) model designed to assess genetic risks for stroke. This innovative tool analyzes DNA to identify genetic variants that may contribute to the likelihood of experiencing a stroke, particularly ischemic stroke, which is the most prevalent form.
The research revealed 18 specific genetic variants that are linked to an increased risk of ischemic stroke. Notably, individuals classified in the highest genetic risk group exhibited a 22% greater chance of suffering a stroke compared to those in lower-risk categories. This significant finding underscores the potential of personalized medicine in stroke prevention and risk assessment.
Utilizing a transformer-based AI approach, the research team trained the model on extensive genetic data, including information from the UK Biobank and a substantial U.S. database. The AI demonstrated superior accuracy in differentiating between individuals who had experienced a stroke and those who had not, outperforming existing genetic risk assessment tools.
Additionally, the study pinpointed the EDEM2 gene within brain cortex tissue as a critical factor influencing stroke susceptibility. This discovery opens avenues for further investigation into the mechanisms behind stroke risk and potential therapeutic targets.
Looking ahead, researchers are working on a multimodal AI tool that will integrate genetic evaluations with various data sources, such as health surveys, lifestyle assessments, and imaging scans. This comprehensive approach aims to enhance the accuracy of stroke risk assessments and could be applied to other diseases, marking a significant advancement in the field of personalized healthcare.
