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Accuracy of an artificial neural network for detecting a regional abnormality in myocardial perfusion SPECT

Annals of Nuclear Medicine volume 33, pages 86–92 (2019)Cite this article

Abstract

Objectives

The patient-based diagnosis with an artificial neural network (ANN) has shown potential utility for the detection of coronary artery disease; however, the region-based accuracy of the detected regions has not been fully evaluated. The aim of this study was to demonstrate the accuracy of all detected regions compared with expert interpretation.

Methods

A total of 109 abnormal regions including 33 regions with stress defects and 76 regions with ischemia were examined, which were derived from 21 patients who underwent myocardial perfusion SPECT within 45 days of coronary angiography. The gray and color scale images, a polar map of stress, rest and difference, and left ventricular function were displayed on the monitor to score the extent and severity of stress defect and ischemia. Two experienced nuclear medicine physicians (Observers A and B) scored the abnormality with a 4-point scale and draw abnormal regions on a polar map. The gold standard was determined by the final judgment of normal or abnormal by the consensus of two other independent expert nuclear cardiologists, and was compared with the stress defect and ischemia derived from ANN.

Results

The concordance rate of ANN to the gold standard was higher than that of two observers. Furthermore, the κ coefficient indicated moderate to substantial agreement for stress defect and slight to the fair agreement for ischemia. The area under the curve (AUC) of ANN was the highest for both stress defect and ischemia; in particular, the ANN of ischemia showed significantly higher AUC than Observer A (p = 0.005). The ANN of stress defect showed higher specificity compared with two observers, while the ANN of ischemia showed higher sensitivity. Consequently, the accuracy of ANN showed the highest in this study.

Conclusion

The ANN-based regional diagnosis showed a high concordance rate with the gold standard and comparable or even higher than the interpretation by nuclear medicine physicians.

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Authors and Affiliations

  1. Department of Quantum Medical Technology, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, 5-11-80, Kodatsuno, Kanazawa, Ishikawa, Japan

    Takayuki Shibutani & Masahisa Onoguchi

  2. Department of Nuclear Medicine, Kanazawa University Hospital, Kanazawa, Japan

    Kenichi Nakajima, Hiroshi Wakabayashi, Hiroshi Mori, Shinro Matsuo & Seigo Kinuya

  3. Department of Radiological Technology, Kanazawa University Hospital, Kanazawa, Japan

    Hiroto Yoneyama & Takahiro Konishi

  4. Department of Physics, Kanazawa Medical University, Kahoku, Japan

    Koichi Okuda

Authors
  1. Takayuki Shibutani
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  2. Kenichi Nakajima
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  3. Hiroshi Wakabayashi
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  4. Hiroshi Mori
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  5. Shinro Matsuo
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  6. Hiroto Yoneyama
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  7. Takahiro Konishi
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  8. Koichi Okuda
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  9. Masahisa Onoguchi
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  10. Seigo Kinuya
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Corresponding author

Correspondence to Takayuki Shibutani.

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Conflict of interest

K. Nakajima has a collaborative research work with FUJIFILM RI Pharma Co., Ltd., Tokyo, Japan, which developed the cardioREPO software program.

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Shibutani, T., Nakajima, K., Wakabayashi, H. et al. Accuracy of an artificial neural network for detecting a regional abnormality in myocardial perfusion SPECT. Ann Nucl Med 33, 86–92 (2019). https://doi.org/10.1007/s12149-018-1306-4

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  • DOI: https://doi.org/10.1007/s12149-018-1306-4

Keywords

  • Artificial neural network
  • Computer-aided diagnosis
  • Myocardial perfusion
  • SPECT
  • Artificial intelligence