Koreans develop AI diagnostic platform via MRI

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Researchers at Seoul National University Hospital (SNUH) have developed an AI platform using objective and biological indicators based on magnetic resonance imaging (MRI) brain images to diagnose autism spectrum disorder (ASD) rapidly and accurately.

reports have been increasing recently that abnormal development of the structure and function of the social brain also causes the disease.

The team, led by Professor Kim Bung-nyeon of the Department of Pediatrics, evaluated the diagnostic discrimination ability of 58 patients with ASD and 48 people in control groups through an MRI brain image-based machine learning AI algorithm. The participants were three to six years old, and the team only included low-functioning patients in the autism group.

Researchers built the machine learning algorithm as a classifier by applying machine learning, such as the random forest. As parameters for classification, the researchers used T1-weighted MRI images, diffusion tensor images, and multiple MRIs. Afterward, they evaluated the ability to distinguish between the autism group through a machine learning AI algorithm.

As a result, the multiple MRI model showed high diagnostic discrimination ability with 88.8 percent accuracy, 93 percent sensitivity, and 83.8 percent specificity.

Notably, the accuracy of the multiple MRI model improved by 10 percentage points compared to T1-weighted MRI and diffusion tensor imaging.

Also, the team confirmed that the most important imaging parameters for diagnosing autism spectrum disorder were the cortical thickness of the occipital lobe, diffusion of the cerebellar horn, and posterior cingulate gyrus connectivity.

Journal of Autism and Developmental Disorders has published the study results in its latest issue.