access intranet after hours circle-arrow apply blog caret circle arrow close closer look community outreach community outreach contact contact us down arrow facebook lock solid find a provider find a clinical trial find a provider find a researcher find faculty find-a-service how to apply join leadership left arrow locations logo make a gift map location maximize minimize my chart my chart notification hp notification lp next chevron right nxt prev pay your bill play previous quality and safety refer a patient request a speaker request appointment request an appointment residents corner rss search search jobs Asset 65 submit a story idea symptom checker Arrow Circle Up twitter youtube Dino Logo External Link University Logo Color University Logo Solid Health Logo Solid Arrow Right Circle Book Calendar Date Calendar Search Date Diploma Certificate Dollar Circle Donate Envelope Graduation Cap Map Pin Map Search Phone Pills Podcast

Predicting Epilepsy Surgery Outcomes With Deep Learning

DTI Sample

by Caroline Wallace

Using deep learning, a subset of artificial intelligence involving statistical computation, MUSC Health neurologists have developed a new method that may one day help both patients with medication-refractory epilepsy and their physicians weigh the pros and cons of brain surgery. In addition to the potential clinical implications, these findings, published in the September 2018 issue of Epilepsia, highlight how artificial intelligence is driving change in the medical field.

Although brain surgery is often recommended to patients who do not respond to medication, many hesitate, in part due to the operative risks and in part due to limited success. To overcome this, Leonardo Bonilha, M.D., Ph.D., and his team searched for a better way to predict which patients are likely to be seizure free after surgery.

The team turned to deep learning due to the massive amount of data analysis required. “In this study, we incorporated advanced neuroimaging and computational techniques to anticipate surgical outcomes with the goal of enhancing quality of life,” explains Neurology Department Chief Resident Ezequiel Gleichgerrcht, M.D.

The whole-brain connectome, the key component of this study, is a map of all physical connections in a person’s brain. The map is created by in-depth analysis of diffusion magnetic resonance imaging (dMRI), which patients receive as standard of care prior to surgery. The neurologists used deep learning to examine the connectome, allowing for patterns to be automatically learned.

Today, post-surgery outcomes are predicted using clinical variables that are only 50 percent accurate, while deep learning predictions were 79-88 percent accurate.

“We are using artificial intelligence as an extra tool to make better informed decisions regarding a surgical intervention that may hold the hope for a cure of epilepsy in many patients,” summarizes Gleichgerrcht.