A recently developed artificial intelligence (AI) tool has shown to accurately count brain lesions on MRI scans within seconds, once it is adapted to the clinic.
It is believed that the AI tool should help neuroradiologists to evaluate patients’ brain diseases at earlier stages.
This AI tool was developed at the University of Texas Health Science Centre.
Researcher, Mohamad Habes, says: “Certain kinds of brain lesions are tremendously difficult to quantify without AI.”
In a study published in JAMA Network Open, Habes and colleagues from eight institutions demonstrated the AI tool’s utility in identifying and counting enlarged perivascular spaces (ePVS’s).
These spaces, filled with cerebrospinal fluid, surround arteries and veins and are a marker of cerebral small-vessel disease that can lead to stroke and dementia. The study was a follow-up analysis of 1,026 individuals who participated in the Multi-Ethnic Study of Atherosclerosis (MESA).
Hates says: “We have developed an innovative deep-learning tool to precisely quantify every single enlarged perivascular space in the brain and provide us with a map of the patient’s small-vessel disease.”
Taking a painstaking task and making it possible
Before this tool, ePVS’s were ignored because of the difficulty of counting them on MRI scans.
Hates, says: “On average a middle-aged person might have maybe 500 or 600 of these small spaces on an MRI.”
“Think about a neuroradiologist who is going to sit down and count all of them. That’s not really going to happen. He or she would spend one or two hours per scan, or even more, and that amount of laborious effort is not feasible in the workflow of a busy clinic.
“We have trained an algorithm with expert knowledge to be able to quantify these lesions on its own.
“This tool recognises them, tells us their exact locations, counts them and tells us their volumes. It tells us a ton of information about them, far more than what a human can do.”
In the JAMA Network Open report, Habes and coauthors studied enlarged perivascular spaces throughout the brain.
Hates, says: “Before, people were not able to do whole-brain ePVS quantification.
“We can now do it with our advanced deep learning tools. In our study, we realised that enlarged perivascular spaces in two regions, the basal ganglia and the thalamus, are the most significant lesions because they showed association with stroke and small-vessel disease markers.”
The basal ganglia is a deep-brain region important for neurodegenerative disorders and is related to movement and decision-making.
The thalamus, a region near the basal ganglia, is related to sensory functions such as taste and touch.
