A press release by the Radiological Society of North America focuses on a recent paper published in Radiology: Artificial Intelligence. The Cincinnati Children’s and UC authors were led by Lili He, Ph.D. (Neonatology) and include our own Jonathan Dillman, MD, MS.
The study applied deep learning to enhance existing MRI-based detection techniques in 973 ADHD and control subjects. The team found that a deep model using learning multi-scale brain connectome data significantly improved MRI performance in detecting ADHD, over the use of a single-scale method.
By improving diagnostic accuracy, deep-learning-aided MRI-based diagnosis could be critical in implementing early interventions for ADHD patients. Approximately 5% of American pre-school and school-aged children have been diagnosed with ADHD. These children and adolescents face a high risk of failing in academic study and building social relationships, which can result in financial hardship for families and create a tremendous burden on society.
The approach also has potential beyond ADHD, according to Dr. He.
“This model can be generalized to other neurological deficiencies,” she said. “We already use it to predict cognitive deficiency in preterm infants. We scan them soon after birth to predict neurodevelopmental outcomes at two years of age.”
See the full press release here: https://press.rsna.org/timssnet/media/pressreleases/14_pr_target.cfm?ID=2148
Contributed by Gail Pyne-Geithman, (Medical Writer) and edited by Glenn Miñano, BFA.