Artificial intelligence and machine learning for medical imaging: A technology review.

Journal: Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Published Date:

Abstract

Artificial intelligence (AI) has recently become a very popular buzzword, as a consequence of disruptive technical advances and impressive experimental results, notably in the field of image analysis and processing. In medicine, specialties where images are central, like radiology, pathology or oncology, have seized the opportunity and considerable efforts in research and development have been deployed to transfer the potential of AI to clinical applications. With AI becoming a more mainstream tool for typical medical imaging analysis tasks, such as diagnosis, segmentation, or classification, the key for a safe and efficient use of clinical AI applications relies, in part, on informed practitioners. The aim of this review is to present the basic technological pillars of AI, together with the state-of-the-art machine learning methods and their application to medical imaging. In addition, we discuss the new trends and future research directions. This will help the reader to understand how AI methods are now becoming an ubiquitous tool in any medical image analysis workflow and pave the way for the clinical implementation of AI-based solutions.

Authors

  • Ana Barragán-Montero
    Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, UCLouvain, Belgium. Electronic address: ana.barragan@uclouvain.be.
  • Umair Javaid
    Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, UCLouvain, Belgium.
  • Gilmer Valdes
    Department of Radiation Oncology, University of California, San Francisco, California.
  • Dan Nguyen
    University of Massachusetts Chan Medical School, Worcester, Massachusetts.
  • Paul Desbordes
    Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvain, Belgium.
  • Benoit Macq
    Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvain, Belgium.
  • Siri Willems
    ESAT/PSI, KU Leuven Belgium & MIRC, UZ Leuven, Belgium.
  • Liesbeth Vandewinckele
    Department Oncology, Laboratory of Experimental Radiotherapy, KU Leuven, Belgium; Department of Radiation Oncology, UZ Leuven, Belgium. Electronic address: liesbeth.vandewinckele@uzleuven.be.
  • Mats Holmström
    RaySearch Laboratories AB, Sweden.
  • Fredrik Löfman
    RaySearch Laboratories, Stockholm, Sweden.
  • Steven Michiels
    Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, UCLouvain, Belgium.
  • Kevin Souris
    Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, UCLouvain, Belgium.
  • Edmond Sterpin
    Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), UCLouvain, Brussels, Belgium.
  • John A Lee
    Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, UCLouvain, Belgium.