Multi-dimensional Medical Environment Representation for Immersive VR Training
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Solution Overview
Problem
Current VR/AR medical education tools lack the capability to provide dense, immersive, and realistic representations of medical procedures, failing to replicate actual environments and procedures effectively.
Innovation Solution
The system generates a multi-dimensional representation of a medical environment using images from sensing devices, employing machine-learning models to determine semantic information, edit identifying features, and create synthetic views, allowing for a stereoscopic VR experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If simulations or partial recordings are used for VR/AR medical education, then the system complexity is reduced, but the realism and immersion of the medical environment representation deteriorates
Solution Approach 1:
The patent creates a digital twin or virtual replica of the actual medical environment by capturing images from multiple viewpoints and generating a multi-dimensional representation. This copying approach allows the VR/AR system to present a realistic reproduction of the surgical environment without requiring the entire physical system to be replicated, thus maintaining realism while managing complexity.
Solution Approach 2:
The patent transitions from traditional 2D video recordings to multi-dimensional representations that incorporate spatial depth, multiple viewpoints, and semantic information layers. By adding these dimensional aspects, the system achieves higher realism and immersion while using computational methods to manage the increased data complexity.
2Loss of information
If multiple viewpoints and semantic information are incorporated, then the immersion and educational value are improved, but the data processing complexity and time increase
Solution Approach 1:
The patent performs preliminary processing of images from multiple viewpoints before final assembly, including extracting semantic information, identifying medical instruments and anatomical structures, and organizing data by spatial location. This preliminary organization reduces the computational burden during final rendering and playback, decreasing processing time while maintaining information completeness.
Solution Approach 2:
The patent introduces an intermediary processing layer that captures images from multiple cameras, extracts semantic information through machine learning models, and generates a structured multi-dimensional representation. This intermediary layer acts as a mediator between raw image data and the final VR/AR display, efficiently managing information flow and reducing processing bottlenecks.
3Object-affected harmful factors
If identifying features of persons are edited to protect privacy, then ethical compliance is improved, but the amount of usable information for training decreases
Solution Approach 1:
The patent extracts and removes identifying features such as facial characteristics and personal identifiers from the captured images while preserving the underlying medical procedure information. This extraction process separates privacy-sensitive data from educational content, allowing privacy protection without significantly compromising training value.
Solution Approach 2:
The patent applies selective editing only to specific regions containing identifying features (such as faces and name tags) while leaving the rest of the medical environment and procedure details unchanged. This localized approach maintains privacy protection while preserving maximum training information in non-identifying areas.
Data Source
AI summary
Described herein are systems, methods, and instrumentalities associated with generating a multi-dimensional representation of a medical environment based on images of the medical environments. Various pre-processing and/or post-processing operations may be performed to supplement and/or improve the multi-dimensional representation. These operations may include determining semantic information associated with the medical environment based on the images and adding the semantic information to the multi-dimensional representation in addition to space and time information. The operations may also include anonymizing a person presented in the multi-dimensional representation, adding synthetic views to the multi-dimensional representation, improving the quality of the multi-dimensional representation, etc. The multi-dimensional representation of the medical environment generated using these techniques may allow a user to experience and explore the medical environment, for example, via a virtual reality device.