Prediction of Changes in Blood Parameters Induced by Low-Frequency Ultrasound

In this study, we reveal the influence of low-frequency ultrasound on erythrocyte and platelet aggregation. Furthermore, we show that the consequences of sonication of blood samples can be predicted using machine learning techniques based on a set of explicit parameters. A total of 300 blood samples were exposed to low-frequency ultrasound of varying intensities for different durations. The blood samples were sonicated with low-frequency ultrasound in a water bath, which operated at a frequency of 46 ± 2 kHz. Statistical analyses, an ANOVA, and the non-parametric Kruskal–Wallis method were used to evaluate the effect of ultrasound on various blood parameters. The obtained results suggest that there are statistically significant variations in blood parameters attributed to ultrasound exposure, particularly when exposed to a high-intensity signal lasting 180 or 90 s. Furthermore, among the five machine learning algorithms employed to predict ultrasound’s impact on platelet counts, support vector regression (SVR) exhibited the highest prediction accuracy, yielding an average MAPE of 10.34%. Notably, it was found that the effect of ultrasound on the hemoglobin (with a p-value of < 0.001 for MCH and MCHC and 0.584 for HGB parameters) in red blood cells was higher than its impact on platelet aggregation (with a p-value of 0.885), highlighting the significance of hemoglobin in facilitating the transfer of oxygen from the lungs to bodily tissues.