Elliptical Magnetic Nanoparticle Swarm Formation by an Artificial Neural Network-Based Rotating Magnetic Field

Biomedical applications of magnetic nanoparticle (MNP) swarm control rely on precise manipulation of the swarm using magnetic fields. This approach improves the accessibility and maneuverability of the swarm and enhances targeted therapies and drug delivery. In this study, we applied artificial intelligence to control the shape of elliptical MNP swarms by adjusting the parameters of a rotating magnetic field (RMF). The resulting RMF controlled the major axis and slant angle of the ellipse-shaped swarm. We constructed a dataset of diverse swarm shapes and corresponding RMF parameters, and applied it to train an artificial neural network (ANN). The trained ANN was used to predict the experimental RMF parameters required to achieve the desired shapes of elliptical MNP swarms. After ANN training, differences between the predicted and actual root mean square error (RMSE) values were evaluated to verify the model. The RMSEs of the major and minor axis lengths and slant angle were 0.21 mm, 0.06 mm, and 6.01°, respectively. This simple, inference-based approach to swarm formation yielded higher swarm formation accuracy and allowed fine control of the swarm.