Using AI and Electrocorticography To Detect Brain Activity Beyond MRI

Bill Tatum (00:00):

Hello, my name is Bill Tatum. I work at the Mayo Clinic. I'm a Professor of Neurology. My research here at the AES meeting has been to outline our work with a unique novel 360-degree circular grid. We've measured signals that translate to electrocorticography in the operating room for brain tumor. I've learned a lot from other people's experience at multiple different institutions throughout the country. We have a workshop that we're doing on EEG and misdiagnosis referred to as “EEG misadventures”. This is an opportunity for us to get together with novices and experts in the field of epilepsy and EEG to outline some of the pitfalls that can occur and some of the traps that lead people into misdiagnosis, which for patients translates to mistreatment. And so we feel it's very important that these negative impacts that occur with EEG interpretation be brought to light so that they can be corrected and ultimately be used for the betterment of patient care.

(01:16):

One of the key things that we're interested in is using artificial intelligence to identify different signals that emanate from brain directly. So we're using the tools that we have in the operating room to use artificial intelligence to try to define some of the different waveforms and predict tumor recurrence that might occur using electrophysiologic findings that might be able to identify growth of brain tumors even before MRI scanning is complete.