Artificial Intelligence To Assist Brain Research: Stanford

PALO ALTO, CA — Doctors could soon get some help from an artificial intelligence tool when diagnosing brain aneurysms – bulges in blood vessels in the brain that can leak or burst open, potentially leading to stroke, brain damage or death, the Stanford News Service reported.

The AI tool, developed by researchers at Stanford University and detailed in a paper published June 7 in JAMA Network Open, highlights areas of a brain scan that are likely to contain an aneurysm.

“There’s been a lot of concern about how machine learning will actually work within the medical field,” said Allison Park, a Stanford graduate student in statistics and co-lead author of the paper. “This research is an example of how humans stay involved in the diagnostic process, aided by an artificial intelligence tool.”

This tool, which is built around an algorithm called HeadXNet, improved clinicians’ ability to correctly identify aneurysms at a level equivalent to finding six more aneurysms in 100 scans that contain aneurysms. It also improved consensus among the interpreting clinicians, the News Service announced.

Combing brain scans for signs of an aneurysm can mean scrolling through hundreds of images. Aneurysms come in many sizes and shapes and balloon out at tricky angles – some register as no more than a blip within the movie-like succession of images.

“Search for an aneurysm is one of the most labor-intensive and critical tasks radiologists undertake,” said Kristen Yeom, associate professor of radiology and co-senior author of the paper. “Given inherent challenges of complex neurovascular anatomy and potential fatal outcome of a missed aneurysm, it prompted me to apply advances in computer science and vision to neuroimaging.”

Yeom brought the idea to the AI for Healthcare Bootcamp run by Stanford’s Machine Learning Group, which is led by Andrew Ng, adjunct professor of computer science and co-senior author of the paper.

The central challenge was creating an artificial intelligence tool that could accurately process these large stacks of 3D images and complement clinical diagnostic practice, the News Service added.

The machine learning methods at the heart of HeadXNet could likely be trained to identify other diseases inside and outside the brain. For example, Yeom imagines a future version could focus on speeding up identifying aneurysms after they have burst, saving precious time in an urgent situation. But a considerable hurdle remains in integrating any artificial intelligence medical tools with daily clinical workflow in radiology across hospitals.