Scientists have used big data to develop an algorithm for diagnosing autism based on a blood test.

Scientists at New York’s Rensselaer Polytechnic Institute have developed an algorithm that can accurately predict whether a child has an autism spectrum disorder (ASD), based on a blood sample.

The study, published in the open journal PLOS One, represents the world’s first physiological test for autism, and potentially takes researchers one step closer to earlier autism diagnosis and new therapy developments.

For the work, investigators measured 24 different metabolites in a blood sample, and then used big data techniques to find patterns tied to two connected pathways that have been theorized as being linked to ASD.

By using big data, it was possible to establish patterns that may not otherwise have been discovered.

“Instead of looking at the metabolites one at a time, we were able to look at them altogether,” Professor Juergen Hahn, lead author and head of the Rensselaer Department of Biomedical Engineering, told Digital Trends.

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