Deep learning has the potential to help doctors cut down on diagnostic errors, said cardiologist Rima Arnaout in a talk at the GPU Technology Conference.
An assistant professor of medicine at the University of California, San Francisco, Arnaout is focusing on the potential of AI to analyze cardiac ultrasounds and detect congenital heart disease from fetal ultrasounds.
“In medicine, a picture is worth more than a thousand words,” she said. “It really can be worth a patient’s life, in some cases.”
What Can AI Do to Help?
Arnaout outlined a few key challenges for humans analyzing medical images. For one, people sometimes make mistakes. There’s also a physical limit to how much data cardiac imaging specialists like cardiologists and radiologists can analyze.
“We cannot allow those kinds of shortcomings,” she said. “We need accuracy, precision, and we need it delivered at scale.”
While AI models are not without their limitations, Arnaout said, they can help clinicians use medical imaging techniques like ultrasound to their full potential.
She turned to echocardiogram data because “it’s balanced in terms of information richness and clinical volume compared to other cardiovascular imaging tools.” Since echocardiograms can be used for the diagnosis and management of almost every cardiovascular disease, she said, there’s very little selection bias in the datasets.
Echocardiograms are a challenging training dataset, however, because one ultrasound study consists of still images and videos captured from over a dozen angles. A study Arnaout’s team published in npj Digital Medicine used deep learning to classify 15 of these standard views, achieving 91.7 percent accuracy on low-resolution images.