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Mattias Ohlsson

Mattias Ohlsson

Professor

Mattias Ohlsson

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

Author

  • Milan Lomsky
  • Peter Gjertsson
  • Lena Johansson
  • Jens Richter
  • Mattias Ohlsson
  • Deborah Tout
  • Andries van Aswegen
  • S. Richard Underwood
  • Lars Edenbrandt

Summary, in English

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). Conclusions 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.

Department/s

  • Computational Biology and Biological Physics
  • Nuclear medicine, Malmö

Publishing year

2008

Language

English

Pages

1523-1529

Publication/Series

European Journal of Nuclear Medicine and Molecular Imaging

Volume

35

Issue

8

Document type

Journal article

Publisher

Springer

Topic

  • Radiology, Nuclear Medicine and Medical Imaging

Keywords

  • radionuclide imaging
  • neural networks (computer)
  • image interpretation
  • computer assisted
  • heart function tests
  • heart disease

Status

Published

Research group

  • Nuclear medicine, Malmö

ISBN/ISSN/Other

  • ISSN: 1619-7070