Maybe, just maybe you want your stroke hospital to be up-to-date on this. What is your confidence level that that will occur? 0%?
Researchers Develop Artificial Intelligence Tool to Help Detect Brain Aneurysms
An artificial intelligence (AI) tool can help radiologists diagnose brain aneurysms, according to a study published in JAMA Network Open.
“Searching for an aneurysm is one of the most labour-intensive and critical tasks radiologists undertake,” said Kristen W. Yeom, MD, Stanford University, Stanford, California. “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.”
The tool, which is built around an algorithm called HeadXNet, improved clinicians’ ability to correctly identify aneurysms at a level equivalent to finding 6 more aneurysms in 100 scans that contain aneurysms. It also improved consensus among the interpreting clinicians.
Any AI algorithm will have strengths and weaknesses that reflect its programming and training. In the case of HeadXNet, the researchers focused on its ability to identify the presence aneurysms rather than on detecting their absence. As a result, the tool improved clinicians’ ability to see aneurysm but didn’t affect their ability to identify scans without them.
“There’s been a lot of concern about how machine learning will actually work within the medical field,” said Allison Park, Stanford University. “This research is an example of how humans stay involved in the diagnostic process, aided by an artificial intelligence tool.”
To train their algorithm, the researchers outlined clinically significant aneurysms detectable on 611 CT angiogram head scans.
“We labelled, by hand, every voxel -- the 3D equivalent to a pixel -- with whether or not it was part of an aneurysm,” said Christopher Chute, Stanford University. “Building the training data was a pretty gruelling task and there were a lot of data.”
Following the training, the algorithm decides for each voxel of a scan whether there is an aneurysm present. The end result of the HeadXNet tool is the algorithm’s conclusions overlaid as a semi-transparent highlight on top of the scan. This representation of the algorithm’s decision makes it easy for clinicians to still see what the scans look like without HeadXNet’s input.
“We were interested in how these scans, with AI-added overlays, would improve the performance of clinicians,” said Pranav Rajpurkar, Stanford University. “Rather than just having the algorithm say that a scan contained an aneurysm, we were able to bring the exact locations of the aneurysms to the clinician’s attention.”
Eight clinicians tested HeadXNet by evaluating a set of 115 brain scans for aneurysm -- once with the help of HeadXNet and once without. With the tool, the clinicians correctly identified more aneurysms, reduced the “miss” rate, and the clinicians were more likely to agree with one another. HeadXNet did not influence how long it took the clinicians to decide on a diagnosis or their ability to correctly identify scans without.
Although promising, a considerable hurdle remains in integrating any artificial intelligence medical tools with daily clinical workflow in radiology across hospitals given differences in scanner hardware and imaging protocols across different hospital centres. The researchers plan to address such problems through multicentre collaboration.
Current scan viewers aren’t designed to work with deep learning assistance, so the researchers had to custom-build tools to integrate HeadXNet within scan viewers. Similarly, variations in real-world data -- as opposed to the data on which the algorithm is tested and trained -- could reduce model performance. If the algorithm processes data from different kinds of scanners or imaging protocols, or a patient population that wasn’t part of its original training, it might not work as expected.
“Because of these issues, I think deployment will come faster not with pure AI automation, but instead with AI and radiologists collaborating,” said Andrew Y. Ng, PhD, Stanford University. “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.”
Reference: https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2735471
SOURCE: Stanford University
“Searching for an aneurysm is one of the most labour-intensive and critical tasks radiologists undertake,” said Kristen W. Yeom, MD, Stanford University, Stanford, California. “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.”
The tool, which is built around an algorithm called HeadXNet, improved clinicians’ ability to correctly identify aneurysms at a level equivalent to finding 6 more aneurysms in 100 scans that contain aneurysms. It also improved consensus among the interpreting clinicians.
Any AI algorithm will have strengths and weaknesses that reflect its programming and training. In the case of HeadXNet, the researchers focused on its ability to identify the presence aneurysms rather than on detecting their absence. As a result, the tool improved clinicians’ ability to see aneurysm but didn’t affect their ability to identify scans without them.
“There’s been a lot of concern about how machine learning will actually work within the medical field,” said Allison Park, Stanford University. “This research is an example of how humans stay involved in the diagnostic process, aided by an artificial intelligence tool.”
To train their algorithm, the researchers outlined clinically significant aneurysms detectable on 611 CT angiogram head scans.
“We labelled, by hand, every voxel -- the 3D equivalent to a pixel -- with whether or not it was part of an aneurysm,” said Christopher Chute, Stanford University. “Building the training data was a pretty gruelling task and there were a lot of data.”
Following the training, the algorithm decides for each voxel of a scan whether there is an aneurysm present. The end result of the HeadXNet tool is the algorithm’s conclusions overlaid as a semi-transparent highlight on top of the scan. This representation of the algorithm’s decision makes it easy for clinicians to still see what the scans look like without HeadXNet’s input.
“We were interested in how these scans, with AI-added overlays, would improve the performance of clinicians,” said Pranav Rajpurkar, Stanford University. “Rather than just having the algorithm say that a scan contained an aneurysm, we were able to bring the exact locations of the aneurysms to the clinician’s attention.”
Eight clinicians tested HeadXNet by evaluating a set of 115 brain scans for aneurysm -- once with the help of HeadXNet and once without. With the tool, the clinicians correctly identified more aneurysms, reduced the “miss” rate, and the clinicians were more likely to agree with one another. HeadXNet did not influence how long it took the clinicians to decide on a diagnosis or their ability to correctly identify scans without.
Although promising, a considerable hurdle remains in integrating any artificial intelligence medical tools with daily clinical workflow in radiology across hospitals given differences in scanner hardware and imaging protocols across different hospital centres. The researchers plan to address such problems through multicentre collaboration.
Current scan viewers aren’t designed to work with deep learning assistance, so the researchers had to custom-build tools to integrate HeadXNet within scan viewers. Similarly, variations in real-world data -- as opposed to the data on which the algorithm is tested and trained -- could reduce model performance. If the algorithm processes data from different kinds of scanners or imaging protocols, or a patient population that wasn’t part of its original training, it might not work as expected.
“Because of these issues, I think deployment will come faster not with pure AI automation, but instead with AI and radiologists collaborating,” said Andrew Y. Ng, PhD, Stanford University. “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.”
Reference: https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2735471
SOURCE: Stanford University
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