Skip to main content

A.I. can tell if you’re a good surgeon just by scanning your brain

Could a brain scan be the best way to tell a top-notch surgeon? Well, kind of. Researchers at Rensselaer Polytechnic Institute and the University at Buffalo have developed Brain-NET, a deep learning A.I. tool that can accurately predict a surgeon’s certification scores based on their neuroimaging data.

This certification score, known as the Fundamentals of Laparoscopic Surgery program (FLS), is currently calculated manually using a formula that is extremely time and labor-consuming. The idea behind it is to give an objective assessment of surgical skills, thereby demonstrating effective training.

“The Fundamental of Laparoscopic Surgery program has been adopted nationally for surgical residents, fellows and practicing physicians to learn and practice laparoscopic skills to have the opportunity to definitely measure and document those skills,” Xavier Intes, a professor of biomedical engineering at Rensselaer, told Digital Trends. “One key aspect of such [a] program is a scoring metric that is computed based on the time of the surgical task execution, as well as error estimation.”

The team of researchers on this project wanted to see if they could predict the FLS score of surgeons by using optical brain imaging. Thanks to a concurrent neural network, they demonstrated that they were able to do this with a high level of accuracy. This work is based on previous research in which functional near-infrared spectroscopy (fNIRS) was shown to be effective at classifying different motor task types, thereby providing a potential means of manual skill performance level. In this latest project, the researchers used the same fNIRS data to predict ultimate performance scores used in surgical certification.

“These results are a stepping stone toward leveraging neuroimaging and deep learning for neurofeedback to improve surgical skill acquisition, retention, and the certification process,” Intes continued. “The advantage of these approaches is that they should enable a more personalized training regimen with bedside feedback for optimal skill acquisition. Current approaches are singularly focusing on task repetition without potential for fast and objective feedback.”

This work is part of a continuous effort to enhance the way that surgical skills are taught and assessed. On its own, this latest piece of research is not going to fundamentally change that. However, going forward it could lay the groundwork for new ways of improving surgical task execution — and personalized approaches to training — by using neuroimaging assessment.

“We are currently using the FLS score as the means to assess surgical skills,” Intes said. “We hope that, with further studies, we will be able also to go beyond this metric and discover [a] new set of neurobiomarkers that will provide finer insight on surgical skill learning and execution.”

A paper describing the research is available to read in the journal IEEE Transactions on Biomedical Engineering.

Editors' Recommendations

Luke Dormehl
I'm a UK-based tech writer covering Cool Tech at Digital Trends. I've also written for Fast Company, Wired, the Guardian…
What’s the carbon footprint of A.I.? This clever tool breaks it down
brain network on veins illustration


Deep-learning A.I. is the machine learning technology that powers everything from cutting-edge natural language processing to machine vision tools. It may also be powering climate change -- as a result of the massive energy consumption and CO2 emissions associated with training these deep-learning models. As the use of deep learning has exploded, so has the compute power associated with them, although this effect is rarely studied.

Read more
This basic human skill is the next major milestone for A.I.
Profile of head on computer chip artificial intelligence.

Remember the amazing, revelatory feeling when you first discovered the existence of cause and effect? That’s a trick question. Kids start learning the principle of causality from as early as eight months old, helping them to make rudimentary inferences about the world around them. But most of us don’t remember much before the age of around three or four, so the important lesson of “why” is something we simply take for granted.

It’s not only a crucial lesson for humans to learn, but also one that today’s artificial intelligence systems are pretty darn bad at. While modern A.I. is capable of beating human players at Go and driving cars on busy streets, this is not necessarily comparable with the kind of intelligence humans might use to master these abilities. That’s because humans -- even small infants -- possess the ability to generalize by applying knowledge from one domain to another. For A.I. to live up to its potential, this is something it also needs to be able to do.

Read more
New ‘A.I. lawyer’ analyzes your emails to find moneysaving loopholes
Joshua Browder parking ticket legal robot

Email systems have gotten smarter. Whether it’s filtering out spam, prioritizing the messages we need to respond to, reminding us when we’ve forgotten to include a mentioned attachment, or suggesting appropriate responses, 2020 email has come a long way from the basic inboxes of yesteryear. But there’s still further they can go -- and Joshua Browder, the creator of the robot lawyer service DoNotPay, believes he’s come up with a way to make email even more user-friendly. (Hint: It involves saving people money.)

Browder, for those unfamiliar with him, is the legal tech genius who has been creating automated legal bots for the past several years. Whether it’s helping appeal parking fines (where the original DoNotPay name came from) or aiding people in gaining unemployment benefits, he’s focused on one consumer rights area after the other to disrupt through automation.

Read more