Scientists Use Machine Learning and MRI Scans to Predict Learning Difficulties

A team of scientists from the Medical Research Council (MRC) Cognition and Brain Sciences Unit at the University of Cambridge (Cambridge, England, UK) used machine learning - a type of artificial intelligence - with data from hundreds of children who struggle at school to identify clusters of learning difficulties, which did not match their previous diagnosis. According to the researchers, this reinforces the need for children to receive detailed assessments of their cognitive skills to identify the best type of support.

For the study, the researchers recruited 550 children who had been referred to a clinic because they were struggling at school. Previous research on learning difficulties has focused on children who had already been diagnosed with a particular difficulty, such as attention deficit hyperactivity disorder (ADHD), an autism spectrum disorder, or dyslexia. The latest study included children with all difficulties, irrespective of their diagnosis, to better capture the range of difficulties within, and overlap between, the diagnostic categories.

The researchers applied machine learning to a broad spectrum of hundreds of struggling learners by supplying the computer algorithm with lots of cognitive testing data from each child, including measures of listening skills, spatial reasoning, problem solving, vocabulary, and memory. Based on these data, the algorithm suggested that the children best fit into four clusters of difficulties. These clusters aligned closely with other data on the children, such as the parents' reports of their communication difficulties, and educational data on reading and math.

However, there was no correspondence with their previous diagnoses. In order to check if these groupings corresponded to biological differences, the groups were checked against MRI brain scans from 184 children. The groupings mirrored patterns in connectivity within parts of the children's brains, suggesting that that the machine learning was identifying differences that partly reflect underlying biology. Two of the four groupings identified were: difficulties with working memory skills, and difficulties with processing sounds in words. The other two clusters identified were: children with broad cognitive difficulties in many areas, and children with typical cognitive test results for their age. The researchers noted that the children in the grouping that had cognitive test results that were typical for their age might still have had other difficulties that were affecting their schooling, such as behavioral difficulties, which had not been included in the machine learning.

"Our study is the first of its kind to apply machine learning to a broad spectrum of hundreds of struggling learners," said Dr Duncan Astle from the MRC Cognition and Brain Sciences Unit at the University of Cambridge, who led the study.

"These are interesting, early-stage findings which begin to investigate how we can apply new technologies, such as machine learning, to better understand brain function," said Dr Joanna Latimer, Head of Neurosciences and Mental Health at the MRC.

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