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DOI: 10.1055/a-2358-8928
Development of a Machine Learning-Enabled Virtual Reality Tool for Preoperative Planning of Functional Endoscopic Sinus Surgery
Supported by: I01 BX004356Supported by: K99 HL148493
Supported by: NIH HL083015,HL111437,HL118650,HL129727
Objectives: Virtual reality (VR) is an increasingly valuable teaching tool, but current simulators are not typically clinically scalable due to their reliance on inefficient manual segmentation. The objective of this project was to leverage a high-throughput and accurate machine learning method to automate data preparation for a patient-specific VR simulator used to explore preoperative sinus anatomy Methods: An endoscopic VR simulator was designed in Unity to enable interactive exploration of sinus anatomy. The Saak transform, a data-efficient machine learning method, was adapted to accurately segment sinus CT scans using minimal training data, and the resulting data was reconstructed into 3D patient-specific models that could be explored in the simulator. Results: Using minimal training data, the Saak transform-based machine learning method offers accurate soft-tissue segmentation. When explored with an endoscope in the VR simulator, the anatomical models generated by the algorithm accurately capture key sinus structures and showcase patient-specific variability in anatomy. Conclusions: By offering an automatic means of preparing VR models from a patient’s raw CT scans, this pipeline takes a key step towards clinical scalability. In addition to preoperative planning, this system also enables virtual endoscopy—a tool that is particularly useful in the COVID-19 era. As VR technology inevitably continues to develop, such a foundation will help ensure that future innovations remain clinically accessible.
Publication History
Received: 05 March 2024
Accepted after revision: 16 May 2024
Accepted Manuscript online:
02 July 2024
© . The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).
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