The new technology will help immensely.
Attention deficit hyperactivity disorder (ADHD) presents challenges in sustaining attention, managing energy levels, and controlling impulses, often manifesting in childhood and impacting overall well-being and social functioning. In the United States, an estimated 6 million children and teenagers aged 6 to 17 have been diagnosed with ADHD, highlighting the prevalence of this condition.
Diagnosing ADHD poses difficulties, with subjective self-reported surveys being the primary method. Recognizing the need for more objective diagnostic approaches, recent research, presented at the Radiological Society of North America’s annual meeting, showcased the application of deep learning artificial intelligence (AI) to analyze MRI scans of teenagers with and without ADHD.
The study, a pioneering effort in utilizing deep learning for ADHD indicators, revealed significant differences in certain brain structures, specifically white matter tracts, among individuals with ADHD. Justin Huynh, MS, a co-author of the study, emphasized the importance of their findings in advancing the understanding of ADHD from a biological perspective and the potential for a more standardized and accurate diagnostic approach.
Drawing data from 21 research sites in the U.S., the research involved a diverse dataset of brain scans, clinical surveys, and information from adolescents with and without ADHD. Utilizing diffusion-weighted imaging (DWI), a specialized MRI technique, the research team extracted fractional anisotropy (FA) measurements along 30 major white matter tracts, assessing how water molecules move through these fibers.
Overcoming challenges faced by previous attempts, the researchers specifically selected 1,704 individuals, including adolescents with and without ADHD, for their study. The deep-learning AI model, trained on FA values from 1,371 participants, was tested on 333 individuals. The results unveiled higher FA values in nine white matter tracts among individuals with ADHD, providing unique insights into the disorder’s intricacies.
These findings represent a significant advancement, offering potential avenues for a more nuanced and objective diagnostic approach to ADHD, contributing to a better understanding of the condition’s biological underpinnings.
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