Machine Learning for the Interventional Radiologist.

Journal: AJR. American journal of roentgenology
Published Date: Jun 5, 2019

Abstract

The purpose of this article is to describe key potential areas of application of machine learning in interventional radiology. Machine learning, although in the early stages of development within the field of interventional radiology, has great potential to influence key areas such as image analysis, clinical predictive modeling, and trainee education. A proactive approach from current interventional radiologists and trainees is needed to shape future directions for machine learning and artificial intelligence.

Authors

  • Ryan D Meek
    Charles T. Dotter Department of Interventional Radiology, Oregon Health & Science University, 3181 SW Sam Jackson Park Rd, Portland, OR 97239-3011.
  • Matthew P Lungren
  • Judy W Gichoya
    The Johns Hopkins Hospital, Department of Radiology, 601 N Caroline St, Room 4223, Baltimore, MD 21287 (S.K.); Cleveland Clinic, Department of Radiation Oncology, Cleveland, Ohio (H.E.); Emory University School of Medicine, Department of Radiology, Atlanta, Georgia (J.G.); University of Pennsylvania, Department of Radiology, Philadelphia, Pennsylvania (C.E.K.).

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