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AI to diagnose invisible brain abnormalities in children with epilepsy

A team of researchers from UCL and King’s College London has developed an AI-powered tool that can detect 64% of brain abnormalities associated with epilepsy, that were missed by human radiologists.

24 February 2025

AI to diagnose invisible brain abnormalities in children with epilepsy

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MELD Graph is an AI tool that could drastically change the care of 30,000 patients in the UK and 4 million patients worldwide who have focal cortical dysplasia (FCD) – a leading cause of epilepsy.

The study, published in JAMA Neurology, shows how the tool significantly improves the detection of FCD. And researchers believe that it will speed up diagnosis times, get patients the surgical treatment they need quicker, and reduce costs to the NHS by up to £55,000 per patient.

FCDs are areas of the brain that have developed abnormally and often cause drug-resistant epilepsy. It is typically treated with surgery, however identifying the structural abnormalities (lesions) from an MRI is an ongoing challenge for clinicians, as MRI scans in FCDs can look normal.

Delays to diagnosis and surgery mean more seizures, more visits to A&E, and more disruption to school, work and home life. 

In the study, the researchers pooled MRI data from 1,185 participants – including 703 people with FCD and 482 controls - from 23 epilepsy centres around the world in the Multicentre Epilepsy Lesion Detection project (MELD). Half of the dataset is from children.

They then trained MELD Graph on the scans to detect subtle brain abnormalities that might otherwise go undetected.

Co-author Professor Helen Cross OBE (Director of UCL Great Ormond Street Institute of Child Health, Prince of Wales's Chair of Childhood Epilepsy, President of the International League Against Epilepsy, and Consultant Epileptologist at Great Ormond Street Hospital), said: “Many of the children I see have experienced years of seizures and investigations before we find a lesion. The epilepsy community is searching for ways to speed up diagnosis and treatment. Initiatives such as MELD have the potential to rapidly identify abnormalities that can be removed and potentially cure the epilepsy.”

Project lead-author, Dr Konrad Wagstyl, from King’s College London, added: “Radiologists are currently inundated with images they have to review. Using an AI-powered tool like MELD Graph can support them with their decisions, making the NHS more efficient, speeding time to treatment for patients and relieving them of unnecessary and costly tests and procedures.”

Around 1% of the world’s population have the serious neurological condition epilepsy, that is characterised 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.

While MELD Graph is not yet clinically available, the research team have released the AI-tool as an open-source software. They are running workshops to train clinicians and researchers around the world, including Great Ormond Street Hospital and the Cleveland Clinic, in how to use it.

First author, Dr Mathilde Ripart (UCL Great Ormond Street Institute of Child Health), said “One of the highlights for me is hearing from doctors around the world, including the UK, Chile, India and France who have been able to use our tools to help their own patients.”

Co-lead Dr Sophie Adler (UCL Great Ormond Street Institute of Child Health), said: “This type of research is only possible with international collaboration. We were privileged to work with 75 researchers and clinicians towards this common goal of ‘no missed epilepsy lesions worldwide’”.

Links

  • Research in JAMA Neurology
  • Professor Helen Cross's academic profile
  • Dr Sophie Adler's academic profile
  • UCL Great Ormond Street Institute of Child Health
  • UCL Population Health Sciences

Image

  • Project leads Dr Konrad Wagstyl, Dr Sophie Adler and Dr Mathilde Ripart, working on the MELD AI algorithm at King’s College London.

Media contact 

Poppy Tombs

E: p.tombs [at] ucl.ac.uk

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UCL Great Ormond Street Institute of Child Health

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