A ground-breaking study has introduced a novel, image-based blood test that could significantly enhance the way doctors manage and personalise treatment for coronary artery disease (CAD), a leading global cause of death.
Researchers have developed a deep learning-based method to profile circulating platelets, tiny blood cells involved in clotting, to assess the real-time effectiveness of antiplatelet therapy in CAD patients. The study analysed blood samples from 207 patients, offering a direct window into how well common antiplatelet medications are working.
Traditionally, assessing these therapies has relied on indirect and often imprecise methods. However, this new technique allows clinicians to directly observe the concentration of platelet aggregates, clusters of activated platelets known to contribute to dangerous blood clots.
The results revealed that patients with acute coronary syndrome had significantly higher levels of platelet aggregates than those with chronic conditions. Importantly, the aggregate concentration fell in response to treatment, confirming drug efficacy. The study also found similar aggregate levels in venous and arterial blood, suggesting routine venous samples could reliably guide treatment decisions, despite CAD’s arterial focus.
The findings pave the way for safer, more effective CAD management, promising better outcomes for millions worldwide.
Reference
Hirose K et al. Direct evaluation of antiplatelet therapy in coronary artery disease by comprehensive image-based profiling of circulating platelets. Nat Commun. 2025;16(1):4386.
