The AI algorithm detects brain abnormalities that cause epileptic seizures.
International researchers working under the direction of University College London have created an artificial intelligence (AI) algorithm that can identify subtle brain abnormalities that cause epileptic seizures.
In order to create the algorithm that reveals where abnormalities occur in instances with drug-resistant focal cortical dysplasia (FCD), a major cause of epilepsy, the Multicentre Epilepsy Lesion Detection project (MELD) analyzed more than 1,000 patient MRI images from 22 international epilepsy centers.
FCDs are brain regions that have developed abnormally and often cause drug-resistant epilepsy. Surgery is typically used to treat it, however, finding the lesions on an MRI is an ongoing problem for physicians since MRI scans for FCDs can appear normal.
The scientists utilized about 300,000 locations throughout the brain to develop the algorithm, which measured cortical features using MRI scans, such as how thick or folded the cortex/brain surface was. After that, based on patterns and characteristics, professional radiologists classified examples as either having FCD or having a healthy brain, which served as the algorithm’s training data.
According to the results, which were published in the journal Brain, the algorithm was successful in identifying the FCD in 67% of cases in the cohort (538 participants).
Previously, 178 of the individuals were declared MRI negative, which signifies that radiologists were unable to detect the abnormality; however, the MELD algorithm was able to detect the FCD in 63% of these instances.
This is particularly crucial because, if medical professionals can identify the abnormality in the brain scan, surgery to remove it may provide a cure.
Co-first author, Mathilde Ripart (UCL Great Ormond Street Institute of Child Health) said: “We put an emphasis on creating an AI algorithm that was interpretable and could help doctors make decisions. Showing doctors how the MELD algorithm made its predictions was an essential part of that process.”
Co-senior author, Dr. Konrad Wagstyl (UCL Queen Square Institute of Neurology) added: “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 epilepsy and improve their cognitive development. Roughly 440 children per year could benefit from epilepsy surgery in England.”
Around 1% of the world’s population has the serious neurological condition epilepsy, which is characterized by frequent seizures.
In the UK some 600,000 people are affected. While drug treatments are available for the majority of people with epilepsy, 20-30% do not respond to medications.
In children who have had surgery to control their epilepsy, FCD is the most common cause, and in adults, it is the third most common cause.
Additionally, of patients who have epilepsy that have an abnormality in the brain that cannot be found on MRI scans, FCD is the most common cause.
Co-first author, Dr. Hannah Spitzer (Helmholtz Munich) said: “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.”
Co-senior author, Dr. Sophie Adler (UCL Great Ormond Street Institute of Child Health) added: “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.”
This study on FCD detection uses the largest MRI cohort of FCDs to date, meaning it is able to detect all types of FCD.
The MELD FCD classifier tool can be run on any patient with a suspicion of having an FCD who is over the age of 3 years and has an MRI scan.
Different MRI scanners were used at the 22 hospitals involved in the study around the globe, which enables the algorithm to be more robust but might also affect algorithm sensitivity and specificity.
Reference: “Interpretable surface-based detection of focal cortical dysplasias: a Multi-centre Epilepsy Lesion Detection study ” by Hannah Spitzer, Mathilde Ripart, Kirstie Whitaker, Felice D’Arco, Kshitij Mankad, Andrew A Chen, Antonio Napolitano, Luca De Palma, Alessandro De Benedictis, Stephen Foldes, Zachary Humphreys, Kai Zhang, Wenhan Hu, Jiajie Mo, Marcus Likeman, Shirin Davies, Christopher Güttler, Matteo Lenge, Nathan T Cohen, Yingying Tang, Shan Wang, Aswin Chari, Martin Tisdall, Nuria Bargallo, Estefanía Conde-Blanco, Jose Carlos Pariente, Saül Pascual-Diaz, Ignacio Delgado-Martínez, Carmen Pérez-Enríquez, Ilaria Lagorio, Eugenio Abela, Nandini Mullatti, Jonathan O’Muircheartaigh, Katy Vecchiato, Yawu Liu, Maria Eugenia Caligiuri, Ben Sinclair, Lucy Vivash, Anna Willard, Jothy Kandasamy, Ailsa McLellan, Drahoslav Sokol, Mira Semmelroch, Ane G Kloster, Giske Opheim, Letícia Ribeiro, Clarissa Yasuda, Camilla Rossi-Espagnet, Khalid Hamandi, Anna Tietze, Carmen Barba, Renzo Guerrini, William Davis Gaillard, Xiaozhen You, Irene Wang, Sofía González-Ortiz, Mariasavina Severino, Pasquale Striano, Domenico Tortora, Reetta Kälviäinen, Antonio Gambardella, Angelo Labate, Patricia Desmond, Elaine Lui, Terence O’Brien, Jay Shetty, Graeme Jackson, John S Duncan, Gavin P Winston, Lars H Pinborg, Fernando Cendes, Fabian J Theis, Russell T Shinohara, J Helen Cross, Torsten Baldeweg, Sophie Adler and Konrad Wagstyl, 12 August 2022, Brain.
The MELD project was funded by the Rosetrees Trust.