AI-DWI shows differences in brains of children with, without ADHD

CHICAGO -- AI-assisted diffusion-weighted imaging (DWI) shows differences in the brains of children with and without attention-deficit/hyperactivity disorder (ADHD), a study presented November 29 at the 2023 RSNA annual meeting has found. In his presentation, Justin Huynh from the University of California, San Francisco found that the deep-learning model revealed significant differences in nine brain white matter tracts in children with ADHD. “These differences in MRI signatures in individuals with ADHD have never been seen before at this level of detail,” Huynh said. “In general, the abnormalities seen in the nine white matter tracts coincide with the symptoms of ADHD.” Reports estimate that 5.7 million U.S. children and adolescents between the ages of 6 and 17 years have been diagnosed with ADHD, which can continue into adulthood. Early diagnosis and intervention strategies help manage this condition. Huynh noted that diagnosing ADHD is complex and relies on criteria that are susceptible to subjective biases. He said that imaging could provide abundant data on the underlying psychopathology and etiology of this condition. However, Huynh added that the variations seen in imaging between children with and without ADHD aren’t well understood. Huynh and colleagues used DWI with unsupervised deep learning to visualize derived white matter tractography and fractional anisotropy measurements among adolescent patients with ADHD. Child undergoing pediatric MRI. Image...
Source: AuntMinnie.com Headlines - Category: Radiology Authors: Tags: Subspecialties MRI Neuroradiology RSNA 2023 Source Type: news