ISLAMABAD, JAN 26 (ONLINE): It typically shows up in childhood and can significantly affect an individual\u2019s well-being and their ability to function in society.
\nIn the United States, approximately 6 millionTrusted Source children and teenagers aged 6 to 17 have received a diagnosis of ADHD.
\nExperts say diagnosing ADHD can be challenging with medical professionals often relying on self-reported surveys that are subjective in nature. They say there is a clear demand for more objective methods of diagnosis.<\/p>\n
In new research presented at the Radiological Society of North America\u2019s annual meeting in November, scientists reported on a deep learning type of artificial intelligence (AI) to examine MRI scans of teenagers with and without ADHD.
\nThe researchers said they discovered important differences in certain brain structures called white matter tracts in people with ADHD.
\nUsing AI deep learning to identify ADHD indicators
\nThe researchers said that their study, which hasn\u2019t been published yet in a peer-reviewed journal, is important because it\u2019s the first to utilize deep learning to identify indicators of ADHD.<\/p>\n
Deep learning is a type of AI that can automatically recognize patterns and connections within vast amounts of data.
\nJustin Huynh, MS, a study co-author and a researcher in the Department of Neuroradiology at the University of California San Francisco and a medical student at the Carle Illinois College of Medicine at Urbana-Champaign in Illinois, told Medical News Today that they analyzed a large dataset of brain images of adolescents with and without ADHD.
\n\u201cWe found that, on average, there were statistically significant differences in imaging between study participants with and without [attention deficit],\u201d he said.<\/p>\n
We hope that our findings serve as a promising step toward achieving a better understanding of ADHD from a biological standpoint as well as a more standard, objective, and accurate way to diagnose the condition.
\nJustin Huynh, MS
\nStudy of MRI scanning data and clinical surveys
\nThe research involved data from brain scans, clinical surveys, and other information gathered from 21 research sites in the United States.
\nThe brain imaging data they used included a specialized magnetic resonance imaging (MRI) technique known as diffusion-weighted imaging (DWI).<\/p>\n
Previous attempts to utilize AI for ADHD detection have faced difficulties due to small sample sizes and the intricate nature of the disorder, the researchers reported.
\nIn this study, the research team specifically chose 1,704 individuals that consisted of both adolescents with ADHD and those without the condition.
\nUsing DWI scans, they extracted measurements of fractional anisotropy (FA) along 30 major white matter tracts within the brain. FA measures how water molecules move through the fibers of these tracts.<\/p>\n
The FA values from 1,371 individuals were employed to train a deep-learning AI model, which was subsequently put to the test on 333 participants, comprising 193 who had been diagnosed with ADHD and 140 without ADHD.
\nThrough the use of AI, the researchers said they made a significant discovery. They reported that in individuals with ADHD, FA values were notably higher in nine white matter tracts.
\nThese distinctive MRI patterns in people with ADHD have not been observed in such intricate detail previously.<\/p>\n
In most cases, the irregularities detected in these white matter tracts aligned with the symptoms typically associated with ADHD.
\nThe breakthrough in the ADHD brain scan study
\nDr. David Lefkowitz, a neuroradiology specialist and medical director of MRIs at SimonMed Imaging who was not involved in this study, spoke to Medical News Today, saying, \u201cI agree with [the researchers\u2019] basic framing of ADHD as a complex disorder with potential structural and functional variations underpinning psychopathology.\u201d
\n\u201cHistorically, and with considerable effort, attempts to find structural correlations revealed by MRI to diagnose ADHD have been largely unsuccessful,\u201d Lefkowitz said.<\/p>\n
\u201cBut they may yet exist and the investigators here are using the best available tools to find such correlations by using a combination of DTI and deep learning,\u201d he explained.
\nWhile structural abnormalities in ADHD may exist if only we look hard enough, this is not the most appealing investigational approach. After all, ADHD is a behavioral disturbance. Logically, it would seem that an imaging technique that assesses function, not structure, would be more promising. So, studying functional networks (fMRI) or brain metabolism (PET) would be my bias. Nevertheless, I do think it\u2019s important to keep an open mind.<\/p>\n
Dr. David Lefkowitz
\n\u201cDiscoveries happen in unexpected places, so my skepticism should not be viewed as dismissive,\u201d Lefkowitz said. \u201cI would be very interested to see where this leads, especially when the study matures to a peer-reviewed publication.\u201d
\nTech developments may improve accuracy of ADHD diagnoses
\nLivia Lifes, the chief executive officer at Neuroute and an expert in artificial intelligence who also not involved in the research, told Medical News Today that \u201cthis research represents a significant advancement in the application of AI and imaging data analysis to the field of ADHD diagnosis.\u201d
\nUnsupervised deep learning techniques, such as autoencoders, have the potential to uncover subtle structural patterns that might be missed by traditional diagnostic methods. This can greatly enhance the accuracy of ADHD diagnosis and provide valuable insights into the underlying neurobiology of the disorder.<\/p>\n
Livia Lifes
\nLefkowitz agreed, saying, \u201can accurate non-invasive imaging technique for ADHD patients could be very helpful in clinical management and also in drug trials.\u201d
\n\u201cOne of the challenges with proving drug efficacy is patient selection,\u201d he said. \u201cThe costs of clinical drug trials are enormous, partly because large numbers of patients are required to achieve statistically significant results.\u201d
\n\u201cMore accurate diagnosis of ADHD and, further, the ability to stratify patients based on severity has the potential to reduce the required size and therefore cost of such trials,\u201d he added.
\nIn conclusion, Lefkowitz said, \u201cthe implications are beyond the patient population (which obviously would benefit), but to society more broadly.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"
ISLAMABAD, JAN 26 (ONLINE): It typically shows up in childhood and can significantly affect an individual\u2019s well-being and their ability to function in society. In the United States, approximately 6 millionTrusted Source children and teenagers aged 6 to 17 have received a diagnosis of ADHD. […]<\/p>\n
\n","protected":false},"author":16,"featured_media":100791,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[9821],"tags":[],"class_list":["post-100790","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-latest-health-news"],"yoast_head":"\n