AI Rivals Expert Radiologists at Detecting Brain Hemorrhages

An algorithm developed by scientists at UC San Francisco and UC Berkeley did better than two out of four expert radiologists at finding tiny brain hemorrhages in head scans--an advance that one day may help doctors treat patients with traumatic brain injuries (TBI), strokes and aneurysms. The continued increase in diagnostic imaging studies, including 3D imaging studies such as computed tomography (CT), means that radiologists are looking at thousands of images each day, searching for tiny abnormalities that can signal life-threatening emergencies. The number of images from each brain scan can be so large that on a busy day, radiologists may opt to scroll through some large 3D stacks of images using mice with frictionless wheels, almost like viewing a movie. But it could be much more efficient--and potentially more accurate--if AI technology could pick out the images with significant abnormalities, so radiologists could examine them more closely. "We wanted something that was practical, and for this technology to be useful clinically, the accuracy level needs to be close to perfect," said Esther Yuh, MD, PhD, associate professor of radiology at UCSF and co-corresponding author of the study, published the week of Monday, Oct. 21, 2019, in Proceedings of the National Academy of Sciences (PNAS).