A Modular Edge Device Network for Surgery Digitalization
Fuente:
arXiv
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| Autori principali: | , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866908312989073408 |
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| author | Schorp, Vincent Giraud, Frédéric Pargätzi, Gianluca Wäspe, Michael von Ritter-Zahony, Lorenzo Wegmann, Marcel Cavalcanti, Nicola A. Henao, John Garcia Bünger, Nicholas Cachin, Dominique Caprara, Sebastiano Fürnstahl, Philipp Carrillo, Fabio |
| author_facet | Schorp, Vincent Giraud, Frédéric Pargätzi, Gianluca Wäspe, Michael von Ritter-Zahony, Lorenzo Wegmann, Marcel Cavalcanti, Nicola A. Henao, John Garcia Bünger, Nicholas Cachin, Dominique Caprara, Sebastiano Fürnstahl, Philipp Carrillo, Fabio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_14049 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Modular Edge Device Network for Surgery Digitalization Schorp, Vincent Giraud, Frédéric Pargätzi, Gianluca Wäspe, Michael von Ritter-Zahony, Lorenzo Wegmann, Marcel Cavalcanti, Nicola A. Henao, John Garcia Bünger, Nicholas Cachin, Dominique Caprara, Sebastiano Fürnstahl, Philipp Carrillo, Fabio Systems and Control Hardware Architecture Human-Computer Interaction Networking and Internet Architecture 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. |
| title | A Modular Edge Device Network for Surgery Digitalization |
| topic | Systems and Control Hardware Architecture Human-Computer Interaction Networking and Internet Architecture |
| url | https://arxiv.org/abs/2503.14049 |