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.

The demand is especially great for asymptomatic individuals of all races and ethnicities.

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. 

Experts involved in the research believe their findings could help to guide providers in making treatment decisions. 

“The value of MRI added to CT in patients such as these should not be presumed."

Better risk stratification can improve clinical decision making and better outcomes, experts involved in the study explained in EJR.