Convolutional neural network in the evaluation of myocardial ischemia from czt spect myocardial perfusion imaging: Comparison to automated quantification

Jui Jen Chen, Ting Yi Su, Wei Shiang Chen, Yen Hsiang Chang*, Henry Horng Shing Lu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

This study analyzes CZT SPECT myocardial perfusion images that are collected at Chang Gung Memorial Hospital, Kaohsiung Medical Center in Kaohsiung. This study focuses on the classification of myocardial perfusion images for coronary heart diseases by convolutional neural network techniques. In these gray scale images, heart blood flow distribution contains the most important features. Therefore, data-driven preprocessing is developed to extract the area of interest. After removing the surrounding noise, the three-dimensional convolutional neural network model is utilized to classify whether the patient has coronary heart diseases or not. The prediction accuracy, sensitivity, and specificity are 87.64%, 81.58%, and 92.16%. The prototype system will greatly reduce the time required for physician image interpretation and write reports. It can assist clinical experts in diagnosing coronary heart diseases accurately in practice.

Original languageEnglish
Article number514
Pages (from-to)1-11
Number of pages11
JournalApplied Sciences (Switzerland)
Volume11
Issue number2
DOIs
StatePublished - 2 Jan 2021

Keywords

  • Cardiovascular diseases
  • Convolutional neural network
  • Machine learning
  • Myocardial perfusion image

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