Deep learning models can improve the detection of attention deficit hyperactivity disorder (ADHD), according to new findings published in Radiology: Artificial Intelligence.
Diagnosing ADHD can be challenging, the study’s authors explained, and brain MRI scans could potentially help researchers address those challenges. The connectome—a comprehensive map of a patient’s brain—is a key component of brain MRI’s potential for detecting ADHD. And improved diagnoses could help more patients get the care they need beginning at an earlier age.
The authors aimed to detect ADHD using multiple connectome maps at once, using existing data from more than 900 patients who were treated at one of eight facilities. All patients had no history of psychiatric, neurologic or medical disorders—other than ADHD.
The team’s multichannel deep neural network (mcDNN) was trained to detect ADHD using both brain connectome data and the patients’ personal characteristic data. Overall, one model achieved an area under the ROC curve (AUC) of 0.82. Single-channel deep neural networks, meanwhile, achieved AUCs of 0.67, 0.69 and 0.77.
“Our results emphasize the predictive power of the brain connectome,” senior author Lili He, PhD, Cincinnati Children's Hospital Medical Center, said in a prepared statement. “The constructed brain functional connectome that spans multiple scales provides supplementary information for the depicting of networks across the entire brain.”
The team’s technique isn’t limited just to ADH, He noted.
“This model can be generalized to other neurological deficiencies,” she said in the same statement. “We already use it to predict cognitive deficiency in pre-term infants. We scan them soon after birth to predict neurodevelopmental outcomes at two years of age.”