A brain aneurysm is a bulge that forms in the blood vessel of your brain that could lead to severe health issues and possibly death. The diagnosis of this aneurysms is a critically important clinical task.
Now, a team of researchers at Stanford University has developed an artificial intelligence (AI) tool that can help detect brain aneurysms. The tool 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.”
It is built around a deep learning algorithm, HeadXNet, which helped clinicians correctly identify up to six more aneurysms in 100 scans and led to an improvement in overall consensus.
While the success of HeadXNet in these experiments is promising, researchers expertise in machine learning, radiology, and neurosurgery advises that further investigation is needed to evaluate the generalizability of the AI tool before it can be released in real-time clinical deployment.
The research began when Kristen Yeom, MD, an associate professor of radiology at Stanford and a co-senior author of the study, brought the idea to the school’s AI for Healthcare Bootcamp run by Stanford’s Machine Learning Group.
“Search for an aneurysm is one of the most labor-intensive and critical tasks radiologists undertake,” said Yeom. “Given the inherent challenges of complex neurovascular anatomy and potentially fatal outcome of a missed aneurysm, it prompted me to apply advances in computer science and vision to neuroimaging.”
To train their algorithm, Yeom and her team outlined clinically significant aneurysms detectable on 611 computerized tomographies (CT) angiogram head scans. Eight clinicians tested HeadXNet by interpreting 115 separate scans both with the algorithm and without it. Overall, the automated detection tool led to improvements in sensitivity, mean accuracy and mean interrater agreement. On the other hand, no significant changes were observed in mean specificity or time to diagnosis.
The tool could now be further trained to identify other diseases inside and outside the brain. But, there are some considerable issues remains with this line of work. Current scan viewers and other machines are simply not designed to work with deep learning assistance, as noted in the press release. Therefore, the researchers had to custom-build tools to integrate HeadXNet within scan viewers.
“Because of these issues, I think deployment will come faster not with pure AI automation, but instead with AI and radiologists collaborating,” said Andrew Ng, Ph.D., adjunct professor of computer science at Stanford and a co-senior author of the study. “We still have technical and non-technical work to do, but we as a community will get there and AI-radiologist collaboration is the most promising path.”
The research is detailed in a paper published June 7 in JAMA Network Open.