Scientific advancements in nanotechnology and advanced materials are paving the way toward nanoscale devices for in-body precision medicine; comprising integrated sensing, computing, communication, data and energy storage capabilities. In the human cardiovascular system, such devices are envisioned to be passively flowing and continuously sensing for detecting events of diagnostic interest. The diagnostic value of detecting such events can be enhanced by assigning to them their physical locations (e.g., body region), which is the main proposition of flow-guided localization. Current flow-guided localization approaches suffer from low localization accuracy and they are by-design unable to localize events within the entire cardiovascular system. Toward addressing this issue, we propose the utilization of Graph Neural Networks (GNNs) for this purpose, and demonstrate localization accuracy and coverage enhancements of our proposal over the existing State of the Art (SotA) approaches. Based on our evaluation, we provide several design guidelines for GNN-enabled flow-guided localization.

翻译：纳米技术与先进材料的科学进步正推动面向体内精准医疗的纳米级器件发展，这些器件集成了传感、计算、通信、数据与能量存储等功能。在人体心血管系统中，此类器件被设想为可被动流动并持续感知，以检测具有诊断意义的事件。通过赋予这些事件物理位置（如身体区域）可增强检测的诊断价值，这正是流引导定位的核心主张。现有流引导定位方法存在定位精度低且固有设计缺陷导致无法定位整个心血管系统内事件的问题。针对此问题，我们提出利用图神经网络（GNNs）实现该目标，并展示我们的方案相较于现有最先进（SotA）方法在定位精度与覆盖范围上的提升。基于评估结果，我们为基于GNN的流引导定位提供了若干设计指导原则。