Research looks to bring deep learning to radiology Posted on : Dec 08 - 2017

Some leading healthcare organizations are beginning to apply deep learning to research efforts intended to help radiological initiatives to better diagnose diseases.

Deep learning is a subset of artificial intelligence and is used by researchers to help solve many big data problems such as computer vision, speech recognition, and natural language processing. For healthcare organizations doing pioneer work with deep learning, this includes image recognition and the ability to pair that recognition with algorithms to assist in diagnosis.

Currently, few healthcare organizations have the technical capacity to do research in deep learning, but early efforts are beginning to unearth findings that hold promise within radiology, says Luciano Prevedello, MD, division chief in medical imaging informatics at The Ohio State University Wexner Medical Center.

Prevedello leads a lab at OSU Wexner that is looking at the use of augmented intelligence in imaging, staffed by two physicians, three engineers and one medical physicist, he said a presentation at the recent annual meeting of the Radiological Society of North America. The lab is able to use three supercomputers that can run a variety of open-source deep learning frameworks, including Python, Caffe and TensorFlow.

One of the early projects at OSU Wexner involves using deep learning to help support the prioritization of imaging studies, Prevedello says. “One of the problems is that 40 percent of inpatient studies are (ordered with high priority), so how do you sort them and know which ones should really be done first?” Early deep learning work has centered on using deep learning for examining images and extracting critical findings.

For example, in looking at computed tomography images of the head, deep learning efforts have been aimed at “training” the computer to separate out normal images from abnormal images. In a learning set of images, the OSU initiative has been able to make correct classification in 91 out of every 100 images that human radiologists have already studied; in stroke cases, the rate is 81 correct classifications out of every 100 images.

 “The idea here is to make our scanners more intelligent,” Prevedello says. If algorithms can be developed to identify problematic cases, we can then notify radiologists to read those cases sooner, or reshuffle schedules to have them read the highest priority cases first.” View More