Summary: UCLA researchers in the Department of Bioengineering have developed a neural network-based machine learning system that can use the accurate segmentation of CT scans of bone to generate patient specific VR models for preoperative planning.
Background: Virtual reality (VR) is a computer-generated simulation in which a person can interact within an artificial three-dimensional environment using electronic devices. Due to its interactive characteristics, VR offers tremendous potential for surgical education. However, image processing hurdles have prevented its practical adoption as a clinically relevant tool since current VR simulators use pre-crafted models that do not allow users to view patient-specific anatomy for preoperative planning. Therefore, there is a need for a method that can adapt case-to-case variabilities within patients that cannot be comprehensively captured by a preconceived set of standardized models.
Innovation: UCLA researchers in the Department of Bioengineering have developed a method of adapting a neural network-based machine learning method to automate the accurate segmentation of CT scans of bone to generate models for use in VR simulators for patient-specific preoperative planning. The method utilized a machine learning algorithm and high throughput processing to reconstruct into a VR model with clearly identifiable bony elements including fracture fragments. This allows for VR to be accessible for preoperative planning and medical training as models can be easily generated as necessary.
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Status of Development: First successful demonstration by reconstructed into a VR model with clearly identifiable bony elements and fracture lines