Artificial intelligence-based analysis of whole-body bone scintigraphy: The quest for the optimal deep learning algorithm and comparison with human observer performance.

Journal: Zeitschrift fur medizinische Physik
PMID: 36932023

Abstract

PURPOSE: Whole-body bone scintigraphy (WBS) is one of the most widely used modalities in diagnosing malignant bone diseases during the early stages. However, the procedure is time-consuming and requires vigour and experience. Moreover, interpretation of WBS scans in the early stages of the disorders might be challenging because the patterns often reflect normal appearance that is prone to subjective interpretation. To simplify the gruelling, subjective, and prone-to-error task of interpreting WBS scans, we developed deep learning (DL) models to automate two major analyses, namely (i) classification of scans into normal and abnormal and (ii) discrimination between malignant and non-neoplastic bone diseases, and compared their performance with human observers.

Authors

  • Ghasem Hajianfar
    Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran.
  • Maziar Sabouri
    Department of Medical Physics, School of Medicine, Iran University of Medical Science, Tehran, Iran; Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran.
  • Yazdan Salimi
    Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland.
  • Mehdi Amini
    From the Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital.
  • Soroush Bagheri
    Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran.
  • Elnaz Jenabi
    Research Centre for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran.
  • Sepideh Hekmat
    Hasheminejad Hospital, Iran University of Medical Sciences, Tehran, Iran.
  • Mehdi Maghsudi
    Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran.
  • Zahra Mansouri
    Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Maziar Khateri
    Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
  • Mohammad Hosein Jamshidi
    Department of Medical Imaging and Radiation Sciences, School of Allied Medical Sciences, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
  • Esmail Jafari
    The Persian Gulf Nuclear Medicine Research Center, Department of Molecular Imaging and Radionuclide Therapy, Bushehr Medical University Hospital, School of Medicine, Bushehr University of Medical Sciences, Bushehr, Iran.
  • Ahmad Bitarafan Rajabi
    Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran. bitarafan@hotmail.com.
  • Majid Assadi
    Department of Nanotechnology, The Persian Gulf Nuclear Medicine Research Center, The Persian Gulf Biomedical Sciences Institute, Bushehr University of Medical Sciences, Bushehr, Iran.
  • Mehrdad Oveisi
    Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran; Department of Computer Science, University of British ColumbiaVancouver, BC V6T 1Z4, Canada.
  • Isaac Shiri
    Biomedical and Health Informatics, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran.
  • Habib Zaidi
    Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland. habib.zaidi@hcuge.ch.

Keywords

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