The process relied on a convolutional neural network that in addition to over 4,396 CT scans. That's a relatively small number of samples, but the abnormalities were detailed "at the pixel level," according to UCSF. In other words, they were far less likely to misinterpret noise and other errors like hemorrhages. The technique also had AI training on part of an image at a time rather than anything at once, reducing the chances that it would make incorrect assumptions based on minuscule changes.
Like other AI-based detection systems, this wouldn ' Do not completely replace doctors. It only takes about a second to provide a report, though, and it can automatically classify different hemorrhage types. That could save doctors valuable time in emergencies, and could ensure that they catch hard-to-find hemorrhages that could be fatal in the worst circumstances. While scientists are still testing the algorithm against CT scans from trauma centers, there could be a day when it's used to quickly screen patients and help doctors focus on saving lives.