Detection Algorithm of Thrombolytic Solution Concentration with an Optimized Conical Thrombolytic Actuator for Interventional Therapy

Fragmented thrombolytic actuators address the limited time window of thrombolysis agents and the risk of intimal injury from mechanical thrombectomy, emerging as a crucial method for rapid vascular recanalization. However, occluded vessels are often tortuous and narrow, imposing strict size constraints on the actuator. Moreover, the inability to assess thrombolysis efficacy in real-time during procedures impedes timely adjustments to control strategies for the actuator. To address these challenges, this study designs a conical piezoelectric actuator that employs high-frequency vibration in conjunction with a small dose of thrombolytics to fragment and accelerate thrombus dissolution. Firstly, structural parameters of the actuator are optimized using grey relational analysis combined with an improved entropy-weighting method, and the optimal design is prototyped and tested. Next, a real-time thrombolytic solution concentration detection algorithm based on an Improved Grey Wolf Optimizer–Support Vector Regression (IGWO-SVR) model is proposed. Finally, an experimental platform is constructed for validation and analysis. The results show that compared to the initial design, the optimized actuator has significantly improved kinematic and force performance, with the tip amplitude increasing by 42% and the output energy density reaching 3.3726 × 10−2 W/mm3. The IGWO-SVR model yields highly accurate, stable concentration estimates, with a coefficient of determination (R2) of 0.9987 and a root-mean-square error (RMSE) of 0.8118. This work provides a pathway toward actuator miniaturization and real-time thrombolysis monitoring, with positive implications for future clinical applications.