The director of IT and Neuroinformatics Development at the Buffalo Neuroimaging Analysis Center provided thoughts on the trending topics within the neuroimaging space.
"We can use AI to measure and assess the quality of traditional tools. It’s very complementary and not something that’s going to replace it, but instead augment what we can do."
MRI has become the most widely used imaging technique in the investigation and diagnosis of multiple sclerosis (MS). It has been an invaluable tool in understanding and monitoring the disease, as well as distinguishing it from other central nervous system demyelinating diseases. Various types of MRI cans are using in MS, including gadolinium, a contrast agent, which is injected into the veins during an MRI to help detect areas of new inflammation.
At the Buffalo Neuroimaging Analysis Center (BNAC), efforts by Michael Dwyer, PhD, and his esteemed team have helped push the field forward by improving the capabilities of MRI through artificial intelligence (AI). Dwyer the director of IT and Neuroinformatics at BNAC, and an assistant professor of neurology and biomedical engineering at the University at Buffalo, believes that while lesion volume is an important area to monitor, brain atrophy can sometimes take a backseat, potentially creating more harmful problems to a patient’s brain than some of the lesions themselves.
In an interview with NeurologyLive, Dwyer provided background on topics within the neuroimaging space that interest researchers and the importance of focusing on multiple types of biomarkers when evaluating progression of MS. He also provided comments on how AI can play a key role in alleviating the process of collecting large datasets and helping clinicians apply them to clinical practice.