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A Modular Edge Device Network for Surgery Digitalization

Vincent Schorp, Frédéric Giraud, Gianluca Pargätzi, Michael Wäspe, Lorenzo von Ritter-Zahony, Marcel Wegmann, Nicola A. Cavalcanti, John Garcia Henao, Nicholas Bünger, Dominique Cachin, Sebastiano Caprara, Philipp Fürnstahl, Fabio Carrillo

TL;DR

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 and validate the approach through an ultrasound-based 3D anatomical reconstruction experiment.

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.

A Modular Edge Device Network for Surgery Digitalization

TL;DR

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 and validate the approach through an ultrasound-based 3D anatomical reconstruction experiment.

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.

Paper Structure

This paper contains 7 sections, 1 figure, 1 table.

Table of Contents

  1. INTRODUCTION
  2. MATERIALS AND METHODS
  3. RESULTS
  4. DISCUSSION

Figures (1)

  • Figure 1: Data Hub Network.Left: Data Hub (DH) with all ports. Right: Experimental setup comprised of a patient-side DH connected to a US scanner, a pose tracking device, and an RGB-D camera; a supervisor DH to configure and monitor the experiment; and a high-performance computer for data storage and processing. Further DH can be added to the OR-X Network. The real-time transfer of the synchronized data is achieved using a ROS2 network.