Tailoring Graph Neural Network-based Flow-guided Localization to Individual Bloodstreams and Activities
This work addresses the need for continuous and personalized monitoring in medical applications, but it is incremental as it adapts an existing SotA method.
The paper tackled the problem of flow-guided localization for in-body nanodevices, which is limited by existing methods that cannot handle diverse patient bloodstreams and activities, and proposed a GNN adaptation pipeline using physiological indicators to address these issues, showing beneficial results in reconciling individual differences.
Flow-guided localization using in-body nanodevices in the bloodstream is expected to be beneficial for early disease detection, continuous monitoring of biological conditions, and targeted treatment. The nanodevices face size and power constraints that produce erroneous raw data for localization purposes. On-body anchors receive this data, and use it to derive the locations of diagnostic events of interest. Different Machine Learning (ML) approaches have been recently proposed for this task, yet they are currently restricted to a reference bloodstream of a resting patient. As such, they are unable to deal with the physical diversity of patients' bloodstreams and cannot provide continuous monitoring due to changes in individual patient's activities. Toward addressing these issues for the current State-of-the-Art (SotA) flow-guided localization approach based on Graph Neural Networks (GNNs), we propose a pipeline for GNN adaptation based on individual physiological indicators including height, weight, and heart rate. Our results indicate that the proposed adaptions are beneficial in reconciling the individual differences between bloodstreams and activities.