A team of scientists from VIB and KU Leuven has developed a machine learning algorithm that identifies children with juvenile arthritis with almost 90% accuracy from a simple blood test.

The work was led by Professor Adrian Liston, from VIB and KU Leuven in Leuven, Belgium and the Babraham Institute in Cambridge, UK.

Juvenile idiopathic arthritis is the most common rheumatic disease in children, but it presents in many different forms, severities and outcomes.

A team of researchers at Belgian research organisations VIB, KU Leuven and UZ Leuven undertook a detailed biological characterisation of the immune system of hundreds of children with and without juvenile arthritis to help the diagnosis or treatment decisions for this disease.

"We analysed their immune system at a greater level of detail than was ever done before for this disease, and simply using this data we then used machine learning to see if we could tell which children had arthritis."

The results were quite remarkable: the algorithm was about 90% accurate at identifying the children with the disease.

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