AI successfully identifies children with learning disabilities

Children who struggle at school or who have learning difficulties can be more easily identified in the future thanks to artificial intelligence.

Data from hundreds of children who are struggling at school have been introduced by researchers from the Cognition and Brain Sciences Unit of the Medical Research Council (MRC) at the University of Cambridge.

The scientists say that much of the previous research into learning disabilities focused on children who had already received a specific diagnosis, such as attention deficit hyperactivity disorder (ADHD), an autism spectrum disorder or dyslexia.

By including children with all difficulties, regardless of diagnosis, this study could better determine the range of problems within and the overlap between the diagnostic categories.

Study leader Dr. Duncan Astle from the University of Cambridge said: "Receiving a diagnosis is an important point of recognition for parents and children with learning disabilities who recognize the child's difficulties and help them gain access to support.

"But parents and professionals who work with these children every day see that neat labels do not record their individual problems – for example, the ADHD of a child is often not like the ADHD of another child.

"Our study is the first of its kind to apply machine learning to a broad spectrum of hundreds of struggling students."

The team did this by providing the computer algorithm with a lot of cognitive test data from each child, including measurements of listening comprehension, spatial reasoning, problem solving, vocabulary and memory. Based on these data, the algorithm suggests that the children best fit into four clusters of difficulties.

These clusters closely match other data about the children, such as parents' reports about their communication problems and educational data about reading and arithmetic.

But there was no correspondence with their previous diagnoses. To check whether these groups corresponded to biological differences, the groups were checked for MRI brain scans of 184 children.

The groupings reflected patterns in connectivity within parts of the children's brains, suggesting that machine learning identified differences that partially reflect the underlying biology.

Two of the four identified groups had difficulties with working memory skills and difficulties in processing sounds in words. Difficulties with working memory – the storage and manipulation of information in the short term – have been associated with wrestling with mathematics and with tasks such as following lists. Difficulties in processing the sounds in words, called phonological skills, have been associated with struggling with reading.

Dr. Astle said, "Research in the past that has been selected for children with poor reading ability has shown a close relationship between struggling with reading and problems with word processing in words, but by looking at children with a wide range of problems, we unexpectedly that many children with problems in the processing of words not only have difficulty reading, but also with mathematics.

"If researchers study learning disabilities, we need to go beyond the diagnostic label and we hope that this study will help to develop better interventions that are more specifically focused on the individual cognitive problems of children."

Dr Joni Holmes, senior author of the study, said: "Our work suggests that children who find the same subjects difficult, might struggle for very different reasons, which has important implications for selecting suitable interventions."

The other two identified clusters were: children with broad cognitive problems in many areas and children with typical cognitive test results for their age.

The researchers noted that the children in the group with cognitive test results that were typical of their age still had other problems that affected their schooling, such as behavioral problems that were not included in machine learning.

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