Acoustofluidic Thrombolysis: Experimental and ML-based Study on Hematological Dependence

Thrombosis, or the development of blood clots within blood vessels, is a very common medical condition that can result in various diseases including heart attack and stroke. Today, it is the foremost cause of mortality in the developed nations and the cause of one in four deaths worldwide. In recent times, ultrasound thrombolysis has surfaced as a promising technique to treat thrombosis. In a recent study, Gao et al. presented a microscale acoustofluidic device for in vitro thrombolysis of blood aggregates. A microbubble passively trapped inside the microchannel and actuated by a low frequency ultrasound field, produces microstreaming flow and disaggregates the blood clots. They have experimentally studied this thrombolysis process and investigated its relationship with the input signal. Our work is an exploratory research study involving experiments and analysis using Machine Learning, on the performance of thrombolysis in a bubble based acoustofluidic device, and its potential dependence on different hematological parameters of the patient. This understanding is required for future studies and development or upscaling of the acoustofluidic thrombolysis device. Besides, our study provides insights on how some of the important blood parameters affect aggregation of blood cells.