# MAGES 4.0: Accelerating the world's transition to VR training and democratizing the authoring of the medical metaverse

arXiv: https://arxiv.org/abs/2209.08819 · HTML: https://mlsanity.com/paper/2209.08819/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2022.

## Built on by (most cited first, 4 of 4)

- [MultiPlaneNeRF: Neural Radiance Field with Non-Trainable Representation](https://mlsanity.com/paper/2305.10579/) (2023)
- [Real-Time Onboard Object Detection for Augmented Reality: Enhancing Head-Mounted Display with YOLOv8](https://mlsanity.com/paper/2306.03537/) (2023)
- [A Survey on Federated Learning for the Healthcare Metaverse: Concepts, Applications, Challenges, and Future Directions](https://mlsanity.com/paper/2304.00524/) (2023)
- [UniSG^GA: A 3D scenegraph powered by Geometric Algebra unifying geometry, behavior and GNNs towards generative AI](https://mlsanity.com/paper/2306.10621/) (2023)

## Abstract (arXiv)

In this work, we propose MAGES 4.0, a novel Software Development Kit (SDK) to accelerate the creation of collaborative medical training applications in VR/AR. Our solution is essentially a low-code metaverse authoring platform for developers to rapidly prototype high-fidelity and high-complexity medical simulations. MAGES breaks the authoring boundaries across extended reality, since networked participants can also collaborate using different virtual/augmented reality as well as mobile and desktop devices, in the same metaverse world. With MAGES we propose an upgrade to the outdated 150-year-old master-apprentice medical training model. Our platform incorporates, in a nutsell, the following novelties: a) 5G edge-cloud remote rendering and physics dissection layer, b) realistic real-time simulation of organic tissues as soft-bodies under 10ms, c) a highly realistic cutting and tearing algorithm, d) neural network assessment for user profiling and, e) a VR recorder to record and replay or debrief the training simulation from any perspective.