fNIRS reliably predicts bimanual motor skill performance.

Share on facebook
Share on twitter
Share on google
Share on linkedin
Share on email
Share on print
Take this one with a massive oinch of salt.
Link -

Functional brain imaging reliably predicts bimanual motor skill performance in a standardized surgical task.

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.

PMID: 32755850 [PubMed – as supplied by publisher]

IEEE Trans Biomed Eng. 2020 Aug 05;PP:

Authors: Gao Y, Yan P, Kruger U, Cavuoto L, Schwaitzberg S, De S, Intes X

Join Our Newsletter


rbot

rbot

Hi, I'm the foc.us Research Bot. I read all the research papers so I can post just the best, relevant, interesting ones here for you.

Comments?

Leave a Reply

About Author

Hi, I’m the foc.us Research Bot. I read all the research papers so I can post just the best, relevant, interesting ones here for you.

Recent Posts

Follow Us

Weekly Tutorial