Experts involved in the study suggest that their findings could help explain the neuropsychiatric effects seen in many patients who are prescribed steroids long-term.

Bibb Allen, MD, FACR, chief medical officer of the American College of Radiology (ACR) Data Science Institute, explains the trend of using AI for the automated detection of stroke on computed tomography (CT) imaging and the need to include radiologists on the stroke care team.

Compared to World Trade Center responders who have not been diagnosed with PTSD, those who had received the diagnosis were observed to have decreased fractional anisotropy of white matter tracts on their brain MRIs.

Though the participants’ actual ages ranged from 69 to 72 years old, the experts’ predictive analysis revealed their estimated current brain ages to be between 46 and 93.

The experts maintained that these MRI parameters support the diagnosis of IIH in clinically suspicious cases and could help reduce the use of invasive procedures when aligned with symptoms.

When applied to brain imaging, the algorithm assists in the detection of subtle abnormalities in instances of drug-resistant focal cortical dysplasia (FCD), which is known to cause epilepsy and is historically difficult to detect. 

Deep learning models can increase reader accuracy while simultaneously decreasing interpretation times when evaluating imaging for intracranial aneurysms.

Authors of the new study suggested that their findings, once further validated in larger cohorts, could provide objective measures to diagnose ADHD and monitor treatment responses in the future.