By Dana Yamashita
Over the centuries, surgical training has changed from when surgery was considered a trade and one would go from apprentice to journeyman, to present day, when training involves years of schooling, simulators, skills training stations, and animated models. Today’s surgeons are performing increasingly delicate surgeries with increasingly sophisticated equipment, requiring manual dexterity and an understanding of their procedures.
With a new teaching and training method currently being developed, surgeons would complete technical tasks while images of their brain activity reveal how well they have mastered critical skills. A team of researchers from Rensselaer Polytechnic Institute and the University of Buffalo are working to combine neuroimaging, neuromodulation, and artificial intelligence to better understand and measure surgical skill acquisition — and then determine if that mastery can be accelerated.
“Being able to improve technical skills and certify surgeons based on quantitative metrics is an absolute necessity for a safer surgical environment,” said Suvranu De, the principal investigator on the research grant and co-director of the Center for Modeling, Simulation, and Imaging in Medicine (CeMSIM) at Rensselaer. “We need to move toward more objective metrics of skill assessment and certification.”
Currently, surgeons in the United States are certified through training and testing programs that measure skill based on how quickly a surgeon can complete simulated surgical tasks against how many errors are made. De believes this process could be improved if certifiers had a more objective, quantitative measurement of whether a surgeon’s performance reflected a deep level of mastery.
Researchers will use neuroimaging to map where activity is happening within the brain while surgeons complete technical tasks. That data will be analyzed using a collection of deep learning algorithms to assess and quantify each surgeon’s level of learning and skill.
In the second stage of the research, the team will examine whether or not neuromodulation can affect neural activity to facilitate learning. In order to do this, researchers will observe the brain activity of medical students who are performing technical tasks while neural stimulation is applied through external electrodes.
“One of the aspects of the study is to better understand how the brain works and how the brain acquires knowledge,” said Xavier Intes, a professor of biomedical engineering and co-director of CeMSIM. “This will be momentous not only for training, but if you have, for instance, a new robotic surgery tool, we can see how the surgeon responds to the new ergonomics or new information feedback, and it can be refined.”
“This is a one-of-a-kind project,” De said. “It’s really at the bleeding edge of research at the interface of neuroimaging, deep learning, and surgical skill assessment.”
Joining the research team are Professors Pingkun Yan and Uwe Kruger from the Department of Biomedical Engineering, and Rahul, a senior research scientist in CeMSIM. The Rensselaer team of engineers is complemented by a team of human factors experts, neuroscientists, and surgeons at the University at Buffalo’s Jacobs School of Medicine and Biomedical Sciences, including Dr. Steven Schwaitzberg, chair of the Department of Surgery, Lora Cavuoto, an associate professor of industrial and systems engineering, and Anirban Dutta, a research associate professor of biomedical engineering and surgery.
This research is being conducted with the support of a $2.2 million grant from the U.S. Army Medical Research and Development Command of the U.S. Department of Defense, received through a Medical Technology Enterprise Consortium award.
