A Guideline for Open-Source Tools to Make Medical Imaging Data Ready for Artificial Intelligence Applications: A Society of Imaging Informatics in Medicine (SIIM) Survey.

Journal: Journal of imaging informatics in medicine
PMID: 38558368

Abstract

In recent years, the role of Artificial Intelligence (AI) in medical imaging has become increasingly prominent, with the majority of AI applications approved by the FDA being in imaging and radiology in 2023. The surge in AI model development to tackle clinical challenges underscores the necessity for preparing high-quality medical imaging data. Proper data preparation is crucial as it fosters the creation of standardized and reproducible AI models while minimizing biases. Data curation transforms raw data into a valuable, organized, and dependable resource and is a fundamental process to the success of machine learning and analytical projects. Considering the plethora of available tools for data curation in different stages, it is crucial to stay informed about the most relevant tools within specific research areas. In the current work, we propose a descriptive outline for different steps of data curation while we furnish compilations of tools collected from a survey applied among members of the Society of Imaging Informatics (SIIM) for each of these stages. This collection has the potential to enhance the decision-making process for researchers as they select the most appropriate tool for their specific tasks.

Authors

  • Sanaz Vahdati
    Department of Radiology, Radiology Informatics Lab, Mayo Clinic, Rochester, MN 55905, United States.
  • Bardia Khosravi
    Department of Radiology, Radiology Informatics Lab, Mayo Clinic, Rochester, MN 55905, United States.
  • Elham Mahmoudi
    Department of Radiology, Radiology Informatics Lab, Mayo Clinic, Rochester, MN 55905, United States.
  • Kuan Zhang
    From the Department of Radiology, Mayo Clinic, 200 1st St SW, Rochester, MN 55905.
  • Pouria Rouzrokh
    Department of Radiology, Mayo Clinic, Radiology Informatics Laboratory, Rochester, MN.
  • Shahriar Faghani
    Mayo Clinic Artificial Intelligence Lab, Department of Radiology, Mayo Clinic, 200 1st Street, S.W., Rochester, MN, 55905, USA.
  • Mana Moassefi
    Mayo Clinic Artificial Intelligence Lab, Department of Radiology, Mayo Clinic, 200 1st Street, S.W., Rochester, MN, 55905, USA.
  • Aylin Tahmasebi
    Department of Radiology, Thomas Jefferson University, Philadelphia, PA, USA.
  • Katherine P Andriole
    Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (W.F.W., M.T.C., K.M., S.A.G., E.G., M.H.R., G.C.G., K.P.A.); and MGH & BWH Center for Clinical Data Science, Boston, Mass (W.F.W., M.T.C., K.M., K.P.A.).
  • Peter Chang
    Department of Urology, Beth Israel Deaconess Medical Center, Boston, MA, USA.
  • Keyvan Farahani
    Image-Guided Interventions and Imaging Informatics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA.
  • Mona G Flores
    NVIDIA Corporation, Bethesda, Maryland, USA.
  • Les Folio
    Moffitt Cancer Center, Tampa, FL, USA.
  • Sina Houshmand
    Department of Radiology, University of California San Francisco, San Francisco, CA, USA.
  • Maryellen L Giger
    Department of Radiology, University of Chicago, 5841 S Maryland Ave., Chicago, IL, 60637, USA.
  • 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.).
  • Bradley J Erickson
    Department of Radiology, Radiology Informatics Lab, Mayo Clinic, Rochester, MN 55905, United States.

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