A deep learning algorithm for detecting lytic bone lesions of multiple myeloma on CT.

Journal: Skeletal radiology
Published Date: Jan 1, 2023

Abstract

BACKGROUND: Whole-body low-dose CT is the recommended initial imaging modality to evaluate bone destruction as a result of multiple myeloma. Accurate interpretation of these scans to detect small lytic bone lesions is time intensive. A functional deep learning) algorithm to detect lytic lesions on CTs could improve the value of these CTs for myeloma imaging. Our objectives were to develop a DL algorithm and determine its performance at detecting lytic lesions of multiple myeloma.

Authors

  • Shahriar Faghani
    Mayo Clinic Artificial Intelligence Lab, Department of Radiology, Mayo Clinic, 200 1st Street, S.W., Rochester, MN, 55905, USA.
  • Francis I Baffour
    Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA.
  • Michael D Ringler
    Division of Musculoskeletal Radiology, Department of Radiology, Mayo Clinic, Rochester, MN, USA.
  • Matthew Hamilton-Cave
    Mayo Clinic Alix School of Medicine, Rochester, MN, USA.
  • Pouria Rouzrokh
    Department of Radiology, Mayo Clinic, Radiology Informatics Laboratory, Rochester, MN.
  • Mana Moassefi
    Mayo Clinic Artificial Intelligence Lab, Department of Radiology, Mayo Clinic, 200 1st Street, S.W., Rochester, MN, 55905, USA.
  • Bardia Khosravi
    Department of Radiology, Radiology Informatics Lab, Mayo Clinic, Rochester, MN 55905, United States.
  • Bradley J Erickson
    Department of Radiology, Radiology Informatics Lab, Mayo Clinic, Rochester, MN 55905, United States.

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