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November 24, 2020 Tuesday 04:08:14 PM IST

Deep Learning Helps Explore the Structural and Strategic Bases of Autism​

Teacher Insights

A group of researchers from KAIST in collaboration with the Yonsei University College of Medicine applied deep learning techniques to autism diagnosis. Magnetic resonance imaging (MRI) scans of brains of people known to have autism have been used by researchers and clinicians to try to identify structures of the brain they believed were associated with ASD. These researchers have achieved considerable success in identifying abnormal grey and white matter volume and irregularities in cerebral cortex activation and connections as being associated with the condition.

These findings have subsequently been deployed in studies attempting more consistent diagnoses of patients than has been achieved via psychiatrist observations during counseling sessions. While such studies have reported high levels of diagnostic accuracy, the number of participants in these studies has been small, often under 50, and diagnostic performance drops markedly when applied to large sample sizes or on datasets that include people from a wide variety of populations and locations.

So the team applied five different categories of deep learning models to an open-source dataset of more than 1,000 MRI scans from the Autism Brain Imaging Data Exchange (ABIDE) initiative, which has collected brain imaging data from laboratories around the world, and to a smaller, but higher-resolution MRI image dataset (84 images) taken from the Child Psychiatric Clinic at Severance Hospital, Yonsei University College of Medicine. In both cases, the researchers used both structural MRIs (examining the anatomy of the brain) and functional MRIs (examining brain activity in different regions).

The models allowed the team to explore the structural bases of ASD brain region by brain region, focusing in particular on many structures below the cerebral cortex, including the basal ganglia, which are involved in motor function (movement) as well as learning and memory.  Crucially, these specific types of deep learning models also offered up possible explanations of how the AI had come up with its rationale for these findings.  The deep learning models were also able to describe how much a particular aspect contributed to ASD, an analysis tool that can assist psychiatric physicians during the diagnosis process to identify the severity of the autism.

(Content Courtesy: https://news.kaist.ac.kr/newsen/html/news/?mode=V&mng_no=10051)


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