Improved diagnostic accuracy for myocardial perfusion imaging using artificial neural networks on different input variables including clinical and quantification data.

Journal: Revista espanola de medicina nuclear e imagen molecular
Published Date: Aug 8, 2019

Abstract

OBJECTIVE: Diagnostic accuracy of myocardial perfusion imaging (MPI) is not optimal to predict the result of angiography. The current study aimed at investigating the application of artificial neural network (ANN) to integrate the clinical data with the result and quantification of MPI.

Authors

  • R Rahmani
    Cardiology Department, Imam-Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran.
  • P Niazi
    Department of Nuclear Medicine, Vali-asr Hospital, Tehran University of Medical Sciences, Tehran, Iran.
  • M Naseri
    Department of Nuclear Medicine, Vali-asr Hospital, Tehran University of Medical Sciences, Tehran, Iran.
  • M Neishabouri
    Department of Nuclear Medicine, Vali-asr Hospital, Tehran University of Medical Sciences, Tehran, Iran.
  • S Farzanefar
    Department of Nuclear Medicine, Vali-asr Hospital, Tehran University of Medical Sciences, Tehran, Iran.
  • M Eftekhari
    Research Institute for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran.
  • F Derakhshan
    Department of Nuclear Medicine, Vali-asr Hospital, Tehran University of Medical Sciences, Tehran, Iran.
  • R Mollazadeh
    Cardiology Department, Imam-Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran.
  • A Meysami
    Department of Social Medicine, Tehran University of Medical Sciences, Tehran, Iran.
  • M Abbasi
    Department of Nuclear Medicine, Vali-asr Hospital, Tehran University of Medical Sciences, Tehran, Iran. Electronic address: meabbasi@tums.ac.ir.

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