A Distributed Augmented Reality System for Medical Training and Simulation

TL;DR

This paper introduces a distributed augmented reality system for medical training that enhances remote practice of procedures like intubation, using real-time 3D models and a novel synchronization algorithm to improve interactivity and realism.

Contribution

The work presents a novel distributed AR prototype with an adaptive synchronization algorithm for medical training, enabling remote, realistic, and interactive practice of medical procedures.

Findings

Successful demonstration of remote endotracheal intubation training.

The adaptive synchronization algorithm improves interactivity despite network delays.

Potential for expanding to deformable medical models in simulations.

Abstract

Augmented Reality (AR) systems describe the class of systems that use computers to overlay virtual information on the real world. AR environments allow the development of promising tools in several application domains. In medical training and simulation the learning potential of AR is significantly amplified by the capability of the system to present 3D medical models in real-time at remote locations. Furthermore the simulation applicability is broadened by the use of real-time deformable medical models. This work presents a distributed medical training prototype designed to train medical practitioners' hand-eye coordination when performing endotracheal intubations. The system we present accomplishes this task with the help of AR paradigms. An extension of this prototype to medical simulations by employing deformable medical models is possible. The shared state maintenance of the collaborative AR environment is assured through a novel adaptive synchronization algorithm (ASA) that increases the sense of presence among participants and facilitates their interactivity in spite of infrastructure delays. The system will allow paramedics, pre-hospital personnel, and students to practice their skills without touching a real patient and will provide them with the visual feedback they could not otherwise obtain. Such a distributed AR training tool has the potential to: allow an instructor to simultaneously train local and remotely located students and, allow students to actually "see" the internal anatomy and therefore better understand their actions on a human patient simulator (HPS).