Functional brain imaging reliably predicts bimanual motor skill performance in a standardized surgical task
Gao, Yuanyuan Yan, Pingkun Kruger, Uwe Cavuoto, Lora Schwaitzberg, Steven De, Suvranu
Gao, Yuanyuan
Yan, Pingkun
Kruger, Uwe
Cavuoto, Lora
Schwaitzberg, Steven
De, Suvranu
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2020-08-05
Type
Article
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Keywords
Surgery,Task analysis,Feature extraction,Brain modeling,Training,Biological system modeling,Hidden Markov models,Bi-manual skills,Deep learning,Functional imaging,Laparoscopic surgery,Optical imaging
Subjects (LCSH)
Citation
Y. Gao et al., "Functional Brain Imaging Reliably Predicts Bimanual Motor Skill Performance in a Standardized Surgical Task," in IEEE Transactions on Biomedical Engineering, vol. 68, no. 7, pp. 2058-2066, July 2021, doi: 10.1109/TBME.2020.3014299.
Abstract
Currently, there is a dearth of objective metrics for assessing bi-manual motor skills, which are critical for high-stakes professions such as surgery. Recently, functional near-infrared spectroscopy (fNIRS) has been shown to be effective at classifying motor task types, which can be potentially used for assessing motor performance level. In this work, we use fNIRS data for predicting the performance scores in a standardized bi-manual motor task used in surgical certification and propose a deep-learning framework ‘Brain-NET’ to extract features from the fNIRS data. Our results demonstrate that the Brain-NET is able to predict bi-manual surgical motor skills based on neuroimaging data accurately (R2=0.73). Furthermore, the classification ability of the Brain-NET model is demonstrated based on receiver operating characteristic (ROC) curves and area under the curve (AUC) values of 0.91. Hence, these results establish that fNIRS associated with deep learning analysis is a promising method for a bedside, quick and cost-effective assessment of bi-manual skill levels.
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Article published: 2020-08-05. Issue published: 2021-07
Publisher
IEEE
Journal
IEEE Transactions on Biomedical Engineering
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ISSN
0018-9294
1558-2531
1558-2531