Health
New AI Tool Could Accelerate Heart Disease Treatments, Study Finds
Scientists at Imperial College London have developed an artificial intelligence tool that could speed up the discovery of treatments for heart disease and eventually support more personalised care. The technology, known as CardioKG, combines detailed heart scans with large medical databases to identify genes linked to cardiovascular conditions and predict which drugs may be most effective.
Cardiovascular diseases remain the leading cause of death and disability in the European Union, causing around 1.7 million deaths each year and affecting an estimated 62 million people, according to the Organisation for Economic Co-operation and Development (OECD). Researchers hope the AI tool can help address this significant health burden by accelerating drug discovery and improving treatment outcomes.
CardioKG was built using heart imaging data from thousands of participants in the UK Biobank, including patients with atrial fibrillation, heart failure, and heart attacks, as well as healthy volunteers. By integrating genetic information, disease data, and drug profiles into a single knowledge graph, the researchers say the system can make more precise predictions about which medications could benefit patients with specific heart conditions.
“One of the advantages of knowledge graphs is that they integrate information about genes, drugs, and diseases,” said Declan O’Regan, group leader of the Computational Cardiac Imaging Group at Imperial College London. He added that including heart imaging in the model significantly improved the identification of new genes and potential drug therapies.
The study highlighted several drugs for potential repurposing. Methotrexate, commonly used to treat rheumatoid arthritis, was suggested as a possible therapy for heart failure, while gliptins, a class of diabetes medications, could benefit patients with atrial fibrillation. The analysis also indicated a potential protective effect of caffeine for some atrial fibrillation patients, although researchers stressed this does not justify changing caffeine consumption without medical advice.
The team aims to expand CardioKG into a dynamic, patient-focused framework that can capture disease progression over time. Khaled Rjoob, the study’s first author, said the approach could enable more personalised treatment strategies and help predict when diseases are likely to develop. “This will open new possibilities for personalised treatment and predicting disease trajectories,” he said.
Researchers also believe the underlying technology could be applied beyond heart disease, including for conditions such as brain disorders and obesity, offering a broader tool for accelerating medical research and drug development.
By combining AI, medical imaging, and genetic data, CardioKG represents a promising step toward more targeted therapies and improved outcomes for patients with cardiovascular disease, potentially transforming how clinicians understand and treat heart conditions in the future.
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