AI-driven actuation-compatible tracking for closed-loop navigation of miniature robots in vivo.

Abstract

Miniature wireless magnetic robots (MWMRs) hold immense potential for minimally invasive interventions. Real-time tracking of MWMRs is essential for precise in vivo navigation but remains challenging. Tracking MWMRs via their emitted magnetic signals offers a viable solution, yet it has been limited to centimeter-scale robots because isolating weak MWMR signals from overwhelming actuation-induced interference becomes increasingly difficult with miniaturization. Here, we present an artificial intelligence (AI)-driven tracking system to overcome these challenges, enabling precise localization of millimeter-scale MWMRs in the presence of actuation fields. Our approach integrates a sensor array design with a transformer to accurately model and filter the interference, followed by a spatiotemporal transformer that interprets the extracted MWMR signals over a short time window to corresponding three-dimensional coordinates. Our system has been validated in a range of in vitro and in vivo experiments, demonstrating its applicability to closed-loop navigation of MWMRs in unshielded clinical settings.