AI can Spot Tough-to-Find Brain Lesions in Seconds

Artificial intelligence (AI) has taken over the world, but in a good way. The applications of AI in healthcare are endless. AI can help with sperm selection, identifying cancer, catching inflamed gums and what not. Now there is an AI tool that can help count brain lesions. The University of Texas Health Science Center in San Antonio has created an artificial intelligence (AI) application that quickly and correctly counts brain lesions shown on MRI scans. The AI technology should assist neuroradiologists in assessing patients' brain disorders at early stages after it has been modified for the clinic.

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AI Tool can Help Identify Hidden Brain Lesions

“Certain kinds of brain lesions are tremendously difficult to quantify without AI,” said researcher Mohamad Habes, PhD, of the health science center’s Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases. Habes is assistant professor of radiology and director of the Biggs Institute neuroimaging core (1^✔ ✔Trusted Source
Assessment of Risk Factors and Clinical Importance of Enlarged Perivascular Spaces by Whole-Brain Investigation in the Multi-Ethnic Study of Atherosclerosis

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Habes and colleagues from eight universities showed the AI tool's usefulness in recognizing and counting enlarged perivascular spaces (ePVS) in a research that was published in JAMA Network Open.

These areas, which are surrounded by arteries and veins and are filled with cerebrospinal fluid, are a sign of cerebral small-vessel disease, which can cause a stroke and dementia. A follow-up analysis of 1,026 people who took part in the Multi-Ethnic research of Atherosclerosis (MESA) was conducted in the research.

“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,” Habes said.

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AI Makes Finding Enlarged Perivascular Spaces (ePVS) Easy

ePVSs were previously neglected due to the difficulties in counting them on MRI images.

“On average a middle-aged person might have maybe 500 or 600 of these small spaces on an MRI,” Habes said. “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.”

The researchers published their automated deep-learning approach for detecting ePVS in the journal Neuroimage: Reports (2^✔ ✔Trusted Source
Deep learning based detection of enlarged perivascular spaces on brain MRI

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“We have trained an algorithm with expert knowledge to be able to quantify these lesions on its own,” Habes said. “This tool recognizes 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.”

Habes and colleagues investigated increased perivascular gaps across the brain in the JAMA Network Open paper.

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Enlarged Perivascular Spaces in Basal Ganglia and Thalamus Linked to Stroke

“Before, people were not able to do whole-brain ePVS quantification,” Habes said. “We can now do it with our advanced deep learning tools. In our study, we realized 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.” According to Habes, the basal ganglia is a deep-brain area critical for neurodegenerative illnesses and is associated with movement and decision-making. The thalamus, a region near the basal ganglia, is associated with sensory activities including taste and touch.

The researchers expect that the AI method for counting brain lesions will be researched further at the Alzheimer's Disease Research Centers (ADRCs), which are National Institute on Aging-designated Centers of Excellence in the United States. The South Texas ADRC, Texas' sole such facility, is a cooperation between the Biggs Institute at UT Health Science Center San Antonio and The University of Texas Rio Grande Valley.

“This is a great breakthrough for our ADRC, which is focusing a lot on cerebrovascular disease and its contribution to dementia,” Habes said. “This is one of the unique themes of our ADRC, and we think our novel AI methodology can benefit large-scale studies conducted across the nation’s ADRCs.”

The AI tool leverages the power of UT Health Science Center San Antonio’s Genie supercomputer, Habes said.

References:

1. Assessment of Risk Factors and Clinical Importance of Enlarged Perivascular Spaces by Whole-Brain Investigation in the Multi-Ethnic Study of Atherosclerosis - (https://pubmed.ncbi.nlm.nih.gov/37093602/)
2. Deep learning based detection of enlarged perivascular spaces on brain MRI - (https://www.sciencedirect.com/science/article/pii/S2666956023000077?via%3Dihub)

Source-Medindia