Deep Reinforcement Learning and Spatiotemporal Transformers for Intelligent Drug Delivery & Flow Control in Thrombosis Prevention

We introduce a unified framework that fuses a data‑driven spatiotemporal CNN‑Transformer surrogate with a deep‑reinforcement‑learning (DRL) controller to optimize anticoagulant delivery and flow in microfluidic thrombosis prevention. Traditional CFD approaches require days of simulation for coupled platelet aggregation, biochemical reactions, and hemodynamics, limiting real-time clinical applications. Our approach introduces two key innovations: (1) a spatiotemporal CNN-transformer surrogate model achieving >1000× speedup while maintaining high accuracy in predicting hemodynamic evolution across pressure drop and tissue factor parameter spaces; (2) a Soft Actor‑Critic DRL agent with virtual drug effects that learns optimal spatiotemporal control policies for anticoagulant injection at multiple sites—without rerunning expensive CFD. The DRL agent controls heparin, aspirin, and tissue plasminogen activator (tPA) delivery across 8 strategic injection points in H-shaped microfluidic channels, optimizing multi-objective rewards balancing clot reduction, flow preservation, drug efficiency, and safety constraints. Results demonstrate 67% clot volume reduction, 82% flow preservation, and 45% drug usage efficiency compared to baseline scenarios. This represents the first application of transformer architectures and reinforcement learning to thrombosis control, enabling real-time optimization of drug-eluting stents, precision anticoagulation protocols, and emergency thrombosis interventions with direct clinical translation potential.