Improving surgical motor skill assessment and acquisition via neuromodulation, neuroimaging, and machine learning
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Authors
Gao, Yuanyuan
Issue Date
2020-08
Type
Electronic thesis
Thesis
Thesis
Language
ENG
Keywords
Mechanical engineering
Alternative Title
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
School of Engineering
Full Citation
Publisher
Rensselaer Polytechnic Institute, Troy, NY