Ultrasonic exposure parameters screening in permeability of mycobacterium smegmatis cytoderm induced by cavitation based on artificial neural network identification

The low intensity ultrasound has been adopted by researchers to enhance the bactericidal effect against bacteria in vitro and in vivo. Although the mechanism is not completely understood, one dominant opinion is that the permeability increases because of acoustic cavitation. However, the relationship between ultrasonic exposure parameters and cavitation effects is not definitely addressed. In this paper, by establishing a modified artificial neural network (ANN) model between ultrasonic parameters and cavitation effects, the cavitation effects can be predicted and inversely the direction for choosing parameters can be given despite of different ultrasonic systems. Compared with the generic model, the computational results obtained by modified model are more close to experimental results with low calculation cost. It means that as an efficient solution, the validity of the new model has been proved. Although the research is of preliminary stage, the new method may have great value and significance because of reducing the experimental expense. The next step of this research is to explore an optimization method to obtain the most suitable parameters based on this identification model. We hope it can give a guideline for future applications in ultrasonic therapy.