Brain-NET, a deep learning methodology, accurately predicts surgeon certification scores based on neuroimaging data

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.”