Image: An AI algorithm detects subtle brain abnormalities which cause epileptic seizures (Photo courtesy of University College London)
Around 1% of the world’s population suffers from the serious neurological condition epilepsy that is characterized by frequent seizures. Drugs treatments are available for the majority of people with epilepsy, although 20-30% do not respond to medications. In children who have had surgery to control their epilepsy, focal cortical dysplasia (FCD) is the most common cause, and in adults it is the third most common cause. FCD are areas of the brain that have developed abnormally and often cause drug-resistant epilepsy. It is typically treated with surgery, although identifying the lesions from an MRI is an ongoing challenge for clinicians, as MRI scans in FCDs can look normal. Now, an artificial intelligence (AI) algorithm can detect subtle brain abnormalities which cause epileptic seizures.
In the Multicentre Epilepsy Lesion Detection project (MELD), a team of international researchers led by the University College London (London, UK) used over 1,000 patient MRI scans from 22 global epilepsy centers to develop the algorithm, which provides reports of where abnormalities are in cases of drug-resistant FCD – a leading cause of epilepsy. To develop the algorithm, the team quantified cortical features from the MRI scans, such as how thick or folded the cortex/brain surface was, and used around 300,000 locations across the brain. Researchers then trained the algorithm on examples labeled by expert radiologists as either being a healthy brain or having FCD – dependant on their patterns and features.
The researchers found that overall the algorithm was able to detect the FCD in 67% of cases in the cohort (538 participants). Previously, 178 of the participants had been considered MRI negative, which means that radiologists had been unable to find the abnormality – yet the MELD algorithm was able to identify the FCD in 63% of these cases. This is particularly important, as if doctors can find the abnormality in the brain scan, then surgery to remove it can provide a cure.
“This algorithm could help to find more of these hidden lesions in children and adults with epilepsy, and enable more patients with epilepsy to be considered for brain surgery that could cure the epilepsy and improve their cognitive development,” said study co-senior author, Dr. Konrad Wagstyl from the UCL Queen Square Institute of Neurology.
“Our algorithm automatically learns to detect lesions from thousands of MRI scans of patients. It can reliably detect lesions of different types, shapes and sizes, and even many of those lesions that were previously missed by radiologists,” added study co-first author, Dr. Hannah Spitzer (Helmholtz Munich).
“We hope that this technology will help to identify epilepsy-causing abnormalities that are currently being missed. Ultimately it could enable more people with epilepsy to have potentially curative brain surgery,” said study co-senior author, Dr. Sophie Adler from the UCL Great Ormond Street Institute of Child Health.
University College London