Using AI and MRI to Detect ADHD

Researchers from theCincinnati Children ' s Hospital Medical Center are utilizingmultichannel deep neural network model (mcDNN) in conjunction with MRI to predict attention deficit hyperactivity disorder (ADHD) in children, according to a  studyrecently published inRadiology: Artificial Intelligence. In the United States, a total of 6.1 million children have been diagnosed with ADHD. Many children with ADHD also struggle with at least one other mental, emotional, or behavioral condition, and 30 percent of youth suffer from anxiety. To lessen the symptoms, many children undergo a combination of behavioral therapy and medication. However, there ’s no defined imaging exam to effectively determine if a child has ADHD or not. Instead, they’re typically assessed with psychological testing. In their study, the group of researchers led byLili He, PhD, used data from 973 patients from the Neuro Bureau ADHD-200 to better understand neurological differences in children who will develop ADHD. They createdmulti-scale functional brain connectomes using anatomic and functional criteria. Connectomes are created with spatial regions on MRI scans. To identify ADHD, the mcDNN model analyzed the connectome data and personal characteristic data. They examined the classification performance with cross-validation and hold-out validation methods using metrics such as accuracy, sensitivity, and specificity. The researchers found that this multiple connectome maps using numerous brain parcellatio...

Source: radRounds - January 18, 2020 Category: Radiology Authors: Julie Morse Source Type: blogs