A Modular Edge Device Network for Surgery Digitalization
Journal:
arXiv
Published Date:
Mar 18, 2025
Abstract
Future surgical care demands real-time, integrated data to drive informed
decision-making and improve patient outcomes. The pressing need for seamless
and efficient data capture in the OR motivates our development of a modular
solution that bridges the gap between emerging machine learning techniques and
interventional medicine. We introduce a network of edge devices, called Data
Hubs (DHs), that interconnect diverse medical sensors, imaging systems, and
robotic tools via optical fiber and a centralized network switch. Built on the
NVIDIA Jetson Orin NX, each DH supports multiple interfaces (HDMI, USB-C,
Ethernet) and encapsulates device-specific drivers within Docker containers
using the Isaac ROS framework and ROS2. A centralized user interface enables
straightforward configuration and real-time monitoring, while an Nvidia DGX
computer provides state-of-the-art data processing and storage. We validate our
approach through an ultrasound-based 3D anatomical reconstruction experiment
that combines medical imaging, pose tracking, and RGB-D data acquisition.
