Sensing the Future of Thrombosis Management: Integrating Vessel-on-a-Chip Models, Advanced Biosensors, and AI-Driven Digital Twins

Abstract

Thrombotic events, such as strokes and deep vein thrombosis, remain a significant global health burden, with traditional diagnostic methods often failing to capture the complex, patient-specific nuances of thrombosis risk. This Perspective explores the revolutionary potential of microengineered vessel-on-chip platforms in thrombosis research and personalized medicine. We discuss the evolution from basic microfluidic channels to advanced 3D-printed, patient-specific models that accurately replicate complex vascular geometries, incorporating all elements of Virchow’s triad. Integrating these platforms with cutting-edge sensing technologies, including wearable ultrasonic devices and electrochemical biosensors, enables real-time monitoring of thrombosis-related parameters. Crucially, we highlight the transformative role of artificial intelligence and digital twin technology in leveraging vast patient-specific data collected from these models. This integration allows for the development of predictive algorithms and personalized digital twins, offering unprecedented thrombosis risk assessment, treatment optimization, and drug screening capabilities. The clinical relevance and validation of these models are examined, showcasing their potential to predict thrombotic events and guide personalized treatment strategies. While challenges in scalability, standardization, and regulatory approval persist, the convergence of vessel-on-chip platforms, advanced sensing, and AI-driven digital twins promises to revolutionize thrombosis management. This approach paves the way for a new era of precision cardiovascular care, offering noninvasive, predictive, and personalized strategies for thrombosis prevention and treatment, ultimately improving patient outcomes and reducing the global burden of cardiovascular diseases.