Brain-NET, a deep learning methodology, accurately predicts surgeon certification scores based on neuroimaging data
New technology could transform training and certification process for
surgeons

Date:
August 11, 2020
Source:
Rensselaer Polytechnic Institute
Summary:
Researchers demonstrated how a deep learning framework they call
'Brain- NET' can accurately predict a person's level of expertise in
terms of their surgical motor skills, based solely on neuroimaging
data.



FULL STORY ==========================================================================
In order to earn certification in general surgery, residents in the United States need to demonstrate proficiency in the Fundamentals of Laparoscopic program (FLS), a test that requires manipulation of laparoscopic
tools within a physical training unit. Central to that assessment is a quantitative score, known as the FLS score, which is manually calculated
using a formula that is time-consuming and labor-intensive.


==========================================================================
By combining brain optical imaging, and a deep learning framework they
call "Brain-NET," a multidisciplinary team of engineers at Rensselaer Polytechnic Institute, in close collaboration with the Department
of Surgery at the Jacobs School of Medicine & Biomedical Sciences at
the University at Buffalo, has developed a new methodology that has
the potential to transform training and the certification process for
surgeons.

In a new article in IEEE Transactions on Biomedical Engineering,
the researchers demonstrated how Brain-NET can accurately predict a
person's level of expertise in terms of their surgical motor skills,
based solely on neuroimaging data. These results support the future
adoption of a new, more efficient method of surgeon certification that
the team has developed.

"This is an area of expertise that is really unique to RPI," said
Xavier Intes, a professor of biomedical engineering at Rensselaer,
who led this research.

According to Intes, Brain-NET not only performed more quickly than the traditional prediction model, but also more accurately, especially as
it analyzed larger datasets.

Brain-NET builds upon the research team's earlier work in this area.

Researchers led by Suvranu De, the head of the Rensselaer Department
of Mechanical, Aerospace, and Nuclear Engineering, previously showed
that they could accurately assess a doctor's surgical motor skills by
analyzing brain activation signals using optical imaging.

In addition to its potential to streamline the surgeon certification
process, the development of Brain-NET, combined with that optical
imaging analysis, also enables real-time score feedback for surgeons
who are training.

"If you can get the measurement of the predicted score, you can give
feedback right away," Intes said. "What this opens the door to is to
engage in remediation or training."

========================================================================== Story Source: Materials provided by
Rensselaer_Polytechnic_Institute. Original written by Torie Wells. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Yuanyuan Gao, Pingkun Yan, Uwe Kruger, Lora Cavuoto, Steven
Schwaitzberg,
Suvranu De, Xavier Intes. Functional brain imaging reliably predicts
bimanual motor skill performance in a standardized surgical
task. IEEE Transactions on Biomedical Engineering, 2020; 1 DOI:
10.1109/ TBME.2020.3014299 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/08/200811125049.htm

--- up 3 weeks, 6 days, 1 hour, 55 minutes
* Origin: -=> Castle Rock BBS <=- Now Husky HPT Powered! (1337:3/111)