Using Virtual Reality and Machine Learning Techniques to Visualize the Human Spine

10 pages•Published: October 4, 2021

Lin Hall, Ping Wang, Grayson Blankenship, Emmanuel Zenil Lopez, Chris Castro, Zhen Zhu and Rui Wu

Abstract

Machine learning technique usage has exploded in recent years, as has the utilization of virtual reality techniques. One area that these tools can be utilized is the practice of medicine. In this research, we propose a framework to visualize the position and rotation of human spines based on machine learning predictions. This framework approach is signifi- cant due to the importance of medical visualizations and organ tracking, with uses ranging from education of medical students, to surgical uses. Subsequently, using machine learning techniques with virtual reality offers real-time medical visualizations which is significant for surgery. According to our experiment results, our proposed framework can accurately predict position and rotation data.

Keyphrases: computer science, extreme event split, gradient boosting regressor, machine learning, virtual reality

In: Frederick Harris, Rui Wu and Alex Redei (editors). Proceedings of ISCA 30th International Conference on Software Engineering and Data Engineering, vol 77, pages 123-132.

Links:	https://easychair.org/publications/paper/Bptz
	https://doi.org/10.29007/xmcf

BibTeX entry

@inproceedings{SEDE2021:Using_Virtual_Reality_Machine,
  author    = {Lin Hall and Ping Wang and Grayson Blankenship and Emmanuel Zenil Lopez and Chris Castro and Zhen Zhu and Rui Wu},
  title     = {Using Virtual Reality and Machine Learning Techniques to Visualize the Human Spine},
  booktitle = {Proceedings of ISCA 30th International Conference on Software Engineering and Data Engineering},
  editor    = {Frederick Harris and Rui Wu and Alex Redei},
  series    = {EPiC Series in Computing},
  volume    = {77},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/Bptz},
  doi       = {10.29007/xmcf},
  pages     = {123-132},
  year      = {2021}}

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