Here’s how the tech giant and doctors from NYU are testing machine learning to accelerate common exams.
Gina Ciavarra is sitting in a dark room at NYU Langone Health in Manhattan. It’s a reading room, a space for radiologists like her to examine X-ray and MRI scans. The monitors in front of her display grayscale images of a de-identified patient’s knee, and in them she detects one key problem: a torn ACL. “This is definitely abnormal,” Ciavarra explains.
But there’s another evaluation that Ciavarra must make, in addition to scanning the swirls of bone, ligaments, fat, cartilage, and tendons for problems like tears or arthritis. Was this particular knee scan created by artificial intelligence, or did it emerge from an MRI machine the traditional way? “My gut says it’s AI,” she says, without certainty. “It just looks a little blurry.”
Ciavarra and her NYU colleagues were participants in a study that pitted the quality of AI-created scans against traditional ones. By pairing artificial intelligence with MRI machines, computer scientists and radiologists think they can greatly speed up a common type of medical exam—a boon for patients and hospitals alike. That could mean cutting a ten-minute knee scan to five minutes, or an hour-long cardiac scan to half an hour. It could also save hospitals money, and reduce the need to anesthetize pediatric patients who may have trouble holding still.
The study, which NYU is now preparing to submit for academic review, is part of a project between two strange bedfellows: the NYU School of Medicine and Facebook. The partnership, initiated by the Facebook Artificial Intelligence Research division and announced over a year ago, has a simple goal: use AI to develop quick yet high-quality MRI scans that could someday allow busy medical centers to care for more people, countries with scant resources to make better use of the equipment they do have, and the elderly, young, and claustrophobic to spend less time in a narrow and loud magnetic tube.
The upshot of using AI in this way is that it requires a lot less information than the well-established approach (called an inverse Fourier transform) does when creating the images that give doctors an inside look at the human body. “In MRI we acquire a certain amount of data and then use reconstruction methods to create an image,” says Michael Recht, the chair of the radiology department at NYU Langone Health. “But it turns out we’ve always collected more data than we probably need.” Think of it like a fuel-efficient car replacing a gas-guzzling clunker: The new algorithm needs less data, from fewer measurements, to go the same distance (or in this case, get the right picture) as the MRI machine.
Image Credit: Facebook / NYU School of Medicine