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An open-source framework of neural networks for diagnosis of coronary artery disease from myocardial perfusion SPECT

  • Original Article
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Journal of Nuclear Cardiology Aims and scope

Abstract

Background

The purpose of this study is to develop and analyze an open-source artificial intelligence program built on artificial neural networks that can participate in and support the decision making of nuclear medicine physicians in detecting coronary artery disease from myocardial perfusion SPECT (MPS).

Methods and Results

Two hundred and forty-three patients, who had MPS and coronary angiography within three months, were selected to train neural networks. Six nuclear medicine residents, one experienced nuclear medicine physician, and neural networks evaluated images of 65 patients for presence of coronary artery stenosis. Area under the curve (AUC) of receiver operating characteristics analysis for networks and expert was .74 and .84, respectively. The AUC of the other physicians ranged from .67 to .80. There were no significant differences between expert, neural networks, and standard quantitative values, summed stress score and total stress defect extent.

Conclusions

The open-source neural networks developed in this study may provide a framework for further testing, development, and integration of artificial intelligence into nuclear cardiology environment.

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Acknowledgments

We would like to thank residents of the Department of Nuclear Medicine, Dr Unal, Dr Cakir, Dr Sucak, Dr Doksoz, and Dr Sahiner who have participated in the experiments of this study.

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Authors and Affiliations

  1. Department of Nuclear Medicine, Gazi University School of Medicine, Besevler, Ankara, Turkey

    Levent A. Guner MD, MS, Nese Ilgin Karabacak MD, Ozgur U. Akdemir MD & Mustafa Unlu MD

  2. Department of Computer Engineering, ODTU, Middle East Technical University, Ankara, Turkey

    Pinar Senkul Karagoz PhD

  3. Department of Cardiology, Gazi University School of Medicine, Besevler, Ankara, Turkey

    Sinan A. Kocaman MD & Atiye Cengel MD

Authors
  1. Levent A. Guner MD, MS
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  2. Nese Ilgin Karabacak MD
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  3. Ozgur U. Akdemir MD
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  4. Pinar Senkul Karagoz PhD
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  5. Sinan A. Kocaman MD
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  6. Atiye Cengel MD
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  7. Mustafa Unlu MD
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Correspondence to Levent A. Guner MD, MS.

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Guner, L.A., Karabacak, N.I., Akdemir, O.U. et al. An open-source framework of neural networks for diagnosis of coronary artery disease from myocardial perfusion SPECT. J. Nucl. Cardiol. 17, 405–413 (2010). https://doi.org/10.1007/s12350-010-9207-5

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  • DOI: https://doi.org/10.1007/s12350-010-9207-5

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