New AI Algorithm Could Lead to an Epilepsy Cure



An artificial intelligence (AI) program has been developed by international academics working under the guidance of University College London that can detect minute brain irregularities that lead to epileptic seizures.

The Multicentre Epilepsy Lesion Detection project (MELD) examined more than 1,000 patient MRI images from 22 international epilepsy centers in order to develop the algorithm that identifies the locations of abnormalities in cases of drug-resistant focal cortical dysplasia (FCD), a major cause of epilepsy.

Brain areas known as FCDs have evolved improperly and frequently lead to drug-resistant epilepsy. Surgery is usually used to treat it, but doctors frequently struggle to spot the lesions on an MRI since FCDs can cause scans to look normal.                                                                                                               
The method that the researchers created assessed cortical properties using MRI scans, such as how thick or folded the cortex/brain surface was. They did this by employing roughly 300,000 places throughout the brain. Professional radiologists then classified cases as either having FCD or having a healthy brain based on patterns and traits, which provided as the algorithm's training data.

The algorithm correctly identified the FCD in 67% of instances in the group, according to the findings, which were published in the journal Brain (538 participants).

Previously, 178 of the patients were classified as MRI negative, meaning that radiologists were unable to find the abnormality; however, in 63% of these cases, the MELD algorithm was successful in finding the FCD.

This is especially important because, if doctors can spot the brain scan anomaly, surgery to remove it might offer a cure.

Mathilde Ripart, a co-first author from the UCL Great Ormond Street Institute of Child Health, stated: "We focused on developing an AI system that was interpretable and could assist physicians in making decisions. A crucial step in that process was demonstrating to the doctors how the MELD algorithm generated its forecasts.

Dr. Konrad Wagstyl, a senior co-author from the University College London Queen Square Institute of Neurology, added that the 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 epilepsy and improve their cognitive development. In England, epilepsy surgery could help about 440 youngsters annually.

Epilepsy is a severe neurological illness that affects 1% of people worldwide and is marked by recurrent seizures.

About 600,000 people in the UK are impacted. The majority of persons with epilepsy can be treated with drugs, although 20–30% of them do not respond to them.

FCD is the most frequent cause in children who have had surgery to treat their epilepsy, and it is the third most frequent cause in adults.

Furthermore, FCD is the most frequent reason for epilepsy in people who have a brain anomaly that cannot be seen on an MRI scan.

Dr. Hannah Spitzer, a co-first author from Helmholtz Munich, stated: "Our system automatically learns to detect lesions from thousands of patient MRI scans. It is capable of accurately identifying lesions of various sorts, forms, and sizes, including several that radiologists had previously overlooked.

Dr. Sophie Adler, a co-senior author from the UCL Great Ormond Street Institute of Child Health, continued, "We believe that this technique will help to discover abnormalities that are now being missed that cause epilepsy. In the long run, it might make it possible for more epilepsy patients to undergo possibly curative brain surgery.

This FCD detection study makes use of the biggest MRI cohort of FCDs to date, making it capable of identifying all FCD subtypes.

Any patient with an MRI who has a suspicion of having an FCD and is over 3 years old can use the MELD FCD classifier tool.

The 22 hospitals involved in the study used various MRI scanners from around the world, making the algorithm more robust but also perhaps affecting its sensitivity and specificity                                                                                                                                                                                                                   UNIVERSITY COLLEGE LONDON                                      

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