Machine learning applications in imaging analysis for patients with pituitary tumors: a review of the current literature and future directions.

Journal: Pituitary
Published Date: Jun 1, 2020

Abstract

PURPOSE: To provide an overview of fundamental concepts in machine learning (ML), review the literature on ML applications in imaging analysis of pituitary tumors for the last 10 years, and highlight the future directions on potential applications of ML for pituitary tumor patients.

Authors

  • Ashirbani Saha
    Department of Radiology, Duke University School of Medicine, 2424 Erwin Road, Suite 302, Durham, NC, 27705, USA. ashirbani.saha@duke.edu.
  • Samantha Tso
    Division of Neurosurgery, St. Michael's Hospital, Toronto, ON, Canada.
  • Jessica Rabski
    Division of Neurosurgery, St. Michael's Hospital, Toronto, ON, Canada.
  • Alireza Sadeghian
    Department of Computer Science, Faculty of Science, Toronto Metropolitan University, Toronto, ON, Canada.
  • Michael D Cusimano
    Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada; Division of Neurosurgery, St Michael's Hospital, Toronto, Ontario, Canada; Neuroscience Research Program, Keenan Research Centre of the Li Ka Shing Knowledge Institute of St. Michael's Hospital, University of Toronto, Toronto, Ontario, Canada. Electronic address: injuryprevention@smh.ca.

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