Machine Learning for Automation of Radiology Protocols for Quality and Efficiency Improvement.

Journal: Journal of the American College of Radiology : JACR
Published Date: Apr 9, 2020

Abstract

PURPOSE: The aim of this study was to enhance multispecialty CT and MRI protocol assignment quality and efficiency through development, testing, and proposed workflow design of a natural language processing (NLP)-based machine learning classifier.

Authors

  • Angad Kalra
    Department of Computer Science, University of Toronto, Toronto, Ontario, Canada. Electronic address: angadk@cs.toronto.edu.
  • Amit Chakraborty
    Department of Radiology, Stanford University Hospital, Palo Alto, California.
  • Benjamin Fine
    Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA (LAC); Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA (LAC); Department of Diagnostic Imaging and Operational Analytics Lab, Trillium Health Partners, Mississauga, ON, Canada (BF); Department of Medical Imaging, University of Toronto, ON, Canada (BF); Departments of Anesthesiology and Neurosurgery and the Center for Advanced Data Analytics, University of Virginia, Charlottesville, USA (DJS).
  • Joshua Reicher
    Department of Radiology, Palo Alto VA Medical Center, Palo Alto, California.

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