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Evaluation of a decision support system for interpretation of myocardial perfusion gated SPECT

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European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

Abstract

Purpose

We have recently presented a decision support system for interpreting myocardial perfusion scintigraphy (MPS). In this study, we wanted to evaluate the system in a separate hospital from where it was trained and to compare it with a quantification software package.

Methods

A completely automated method based on neural networks was trained for the interpretation of MPS regarding myocardial ischaemia and infarction using 418 MPS from one hospital. Features from each examination describing rest and stress perfusion, regional and global function were used as inputs to different neural networks. After the training session, the system was evaluated using 532 MPS from another hospital. The test images were also processed with the quantification software package Emory Cardiac Toolbox (ECTb). The images were interpreted by experienced clinicians at both the training and the test hospital, regarding the presence or absence of myocardial ischaemia and/or infarction and these interpretations were used as gold standard.

Results

The neural network showed a sensitivity of 90% and a specificity of 85% for myocardial ischaemia. The specificity for the ECTb was 46% (p < 0.001), measured at the same sensitivity. The neural network sensitivity for myocardial infarction was 89% and the specificity 96%. The corresponding specificity for the ECTb was 54% (p < 0.001).

Conclusion

A decision support system based on neural networks presents interpretations more similar to experienced clinicians compared to a conventional automated quantification software package. This study shows the feasibility of disseminating the expertise of experienced clinicians to less experienced physicians by the use of neural networks.

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Acknowledgements

This study was supported by grants from the Gothenburg Medical Society, Gothenburg, Sweden and the Sahlgrenska University Hospital, Gothenburg, Sweden.

Conflict of interest

Lars Edenbrandt, Jens Richter and Mattias Ohlsson are shareholders in Exini Diagnostics AB, which owns a neural-network-based decision support system for analysis of myocardial perfusion images.

Author information

Authors and Affiliations

  1. Department of Clinical Physiology, Sahlgrenska University Hospital, 413 45, Gothenburg, Sweden

    Milan Lomsky, Peter Gjertsson, Lena Johansson & Lars Edenbrandt

  2. Exini Diagnostics AB, Lund, Sweden

    Jens Richter

  3. Department of Theoretical Physics, Lund University, Lund, Sweden

    Mattias Ohlsson

  4. Department of Nuclear Medicine, Royal Brompton Hospital, London, UK

    Deborah Tout, Andries van Aswegen & S. Richard Underwood

  5. Imperial College, London, UK

    S. Richard Underwood

Authors
  1. Milan Lomsky
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  2. Peter Gjertsson
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  3. Lena Johansson
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  4. Jens Richter
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  5. Mattias Ohlsson
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  6. Deborah Tout
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  7. Andries van Aswegen
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  8. S. Richard Underwood
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  9. Lars Edenbrandt
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Corresponding author

Correspondence to Peter Gjertsson.

Additional information

This study was supported by grants from the Gothenburg Medical Society, Gothenburg, and the Sahlgrenska University Hospital, Gothenburg, Sweden.

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Lomsky, M., Gjertsson, P., Johansson, L. et al. Evaluation of a decision support system for interpretation of myocardial perfusion gated SPECT. Eur J Nucl Med Mol Imaging 35, 1523–1529 (2008). https://doi.org/10.1007/s00259-008-0746-9

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  • DOI: https://doi.org/10.1007/s00259-008-0746-9

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