Computers and “Computing” in Medical Imaging
Tuesday, January 15, 2019
3:30PM – 5PM
Prior to the advent of the x-ray CT scanner in 1973, there were no computers in radiology. Medical images were analog in origin and nature. Humans, often radiologists, analyzed and interpreted these ‘radiographs’ by direct visual observation of the analog image. By 2010 essentially all medical images were digital in origin and nature, but still analyzed and interpreted by humans, based on direct visual observation of the now digital images. Until very recently, any computing that was performed for image interpretation was performed by humans. Today, thanks to computer hardware and algorithmic advances, digital medical images are being analyzed and beginning to be interpreted by computers. I predict that within 10 years, no medical image will be viewed by a human until it has been analyzed and at least partially interpreted by a computer.
The impact that computers have had on medical imaging cannot be overestimated and selective examples of their transformative impact on image creation, presentation, quantitative analysis and new research and clinical applications will be presented. However, it is the rapidly evolving AI/ML image analysis techniques that are of greatest contemporary interest. For radiologists the key question is, “Can a machine do what we do?” There are secondary, corollary questions with technological implications. “Can we teach a machine what we know and to do what we do,” “Can a machine by itself learn what we know and to do what we do,” or “Can a machine learn more than what we now know and use this new knowledge to make better clinical decisions?” Though computational technology is the topic of general interest, these specific questions relate the new technology to current human performance. Being a radiologist, and not a computer scientist, I will focus on what I think radiologists’ know and do in the image interpretative process. I will attempt to compare and contrast these human processes with what I think computer algorithms’ do, or might do when applied to similar task. Through this process I hope to support my self-serving bias that for the foreseeable future, medical image interpretations, including final diagnosis, will be a finely coordinated, joint product of the computer and radiologist, benefitting from their complementary computing strengths.
R. Nick Bryan is the chair of the Department of Diagnostic Medicine for Dell Medical School. Bryan came to Austin from the Perelman School of Medicine at the University of Pennsylvania, where he served as the Eugene P. Pendergrass Professor and Chair of Radiology. A nationally known leader, thinker and innovator in the field, he also is past president of the Radiological Society of North America, the American Society of Neuroradiology, and the American Society of Head and Neck Radiology.
Bryan previously served at the Johns Hopkins University School of Medicine, where he was professor of radiology and neurosurgery, director of the Neuroradiology Division and vice chairman of the Department of Radiology. He spent much of his childhood in Texas and earned degrees at the University of Texas Medical Branch in Galveston. He previously was director of neuroradiology at Houston Methodist and a professor of radiology at Baylor College of Medicine.
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