Improving surgical motor skill assessment and acquisition via neuromodulation, neuroimaging, and machine learning

Authors
Gao, Yuanyuan
ORCID
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Other Contributors
De, Suvranu
Intes, Xavier
Yan, Pingkun
Zhang, Lucy T.
Liu, Li (Emily)
Issue Date
2020-08
Keywords
Mechanical engineering
Degree
PhD
Terms of Use
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
Full Citation
Abstract
Secondly, we explore the possibility to emulate the current standardized surgical skill metric employed in the field, namely the FLS score, by combining neuroimaging data acquired during the task execution and machine learning methodologies for potentially fast and bedside implementation. In this context, we have validated a deep neural network, Brain-NET, that accurately predicts performance scores from hemodynamic data from the brain obtained using functional near-infrared spectroscopy (fNIRS). Furthermore, we are also currently implementing deep learning approaches to improve and speed up the fNIRS data preprocessing workflow towards enabling real-time implementation.
Description
August 2020
School of Engineering
Department
Dept. of Mechanical, Aerospace, and Nuclear Engineering
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
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