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Photo of Mattias Ohlsson

Mattias Ohlsson


Photo of Mattias Ohlsson

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


  • 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.


  • Computational Biology and Biological Physics - Undergoing reorganization
  • Nuclear medicine, Malmö

Publishing year







European Journal of Nuclear Medicine and Molecular Imaging





Document type

Journal article




  • Radiology, Nuclear Medicine and Medical Imaging


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



Research group

  • Nuclear medicine, Malmö


  • ISSN: 1619-7070