ADHD: How artificial intelligence may help in diagnosing children

Evan Walker
Evan Walker TheMediTary.Com |
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Researchers say a certain type of brain scan could be helpful in diagnosing ADHD. in children. Andrew Brookes/Getty Images
  • In new research presented at the Radiological Society of North America’s annual meeting, researchers used artificial intelligence to review brain scans of adolescents with and without attention deficit hyperactivity disorder (ADHD).
  • This approach identified differences in the white matter tracts of the brains of individuals with ADHD, providing further insights into the condition.
  • ADHD affects around 6 million children and teenagers in the United States, making early diagnosis and intervention crucial for improved well-being in a society increasingly influenced by distractions.

Attention deficit hyperactivity disorder (ADHD) can cause difficulty in maintaining attention, managing energy levels, and controlling impulses.

It typically shows up in childhood and can significantly affect an individual’s well-being and their ability to function in society.

In the United States, approximately 6 million children and teenagers aged 6 to 17 have received a diagnosis of ADHD.

Experts 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.

In new research presented at the Radiological Society of North America’s 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.

The researchers said they discovered important differences in certain brain structures called white matter tracts in people with ADHD.

Dr. 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, “I agree with [the researchers’] basic framing of ADHD as a complex disorder with potential structural and functional variations underpinning psychopathology.”

“Historically, and with considerable effort, attempts to find structural correlations revealed by MRI to diagnose ADHD have been largely unsuccessful,” Lefkowitz said.

“But 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,” he explained.

While 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’s important to keep an open mind.

Dr. David Lefkowitz

“Discoveries happen in unexpected places, so my skepticism should not be viewed as dismissive,” Lefkowitz said. “I would be very interested to see where this leads, especially when the study matures to a peer-reviewed publication.”

Livia 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 “this research represents a significant advancement in the application of AI and imaging data analysis to the field of ADHD diagnosis.”

Unsupervised 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.

Livia Lifes

Lefkowitz agreed, saying, “an accurate non-invasive imaging technique for ADHD patients could be very helpful in clinical management and also in drug trials.”

“One of the challenges with proving drug efficacy is patient selection,” he said. “The costs of clinical drug trials are enormous, partly because large numbers of patients are required to achieve statistically significant results.”

“More 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,” he added.

In conclusion, Lefkowitz said, “the implications are beyond the patient population (which obviously would benefit), but to society more broadly.”

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