Research looks to bring deep learning to radiology

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.