Article published by Neurology Today
Remarkable gains in the diagnosis, treatment, and prognosis of epilepsy are heading from the lab to the bedside with the aid of artificial intelligence (AI), according to neurologists and computer scientists who spoke at the first international devoted to the subject.
Among the findings from studies presented at the conference in Breckenridge, CO, researchers reported that an AI program can diagnose genetic forms of epilepsy in children 3.6 years sooner than clinicians can, and that a machine learning program can predict with 85 to 90 percent accuracy who will be seizure-free following ablative surgery.
But clinicians and researchers also emphasized that scientific and ethical concerns must be addressed before the new technologies are rolled out, since few AI programs are ready for the clinic.
“We need to come together as a community and begin to discuss what’s out there, set standards, and lay down some guidelines,” said Samden Lhatoo, MD, FRCP, the John P. and Kathrine G. McGovern Distinguished University Professor of Neurology and director of the Texas Comprehensive Epilepsy Program at UTHealth Houston’s McGovern Medical School.
The learning curve for neurologists seeking to understand the advanced mathematical techniques used to construct AI programs for epilepsy can be steep. One of the prize-winning papers presented at the conference, for instance, described what it called a dynamic brain network model to predict seizures.
“Specifically, we use the source-sink (SS) metric to quantify each node by its connectivity properties to other nodes in the network,” stated the paper, whose first author was Amir Hossein Daraie, a PhD student in the biomedical engineering department at Johns Hopkins University School of Medicine.