Journal of Nuclear Cardiology

, Volume 9, Issue 2, pp 169–173 | Cite as

Value of exercise data for the interpretation of myocardial perfusion SPECT

  • Henrik Haraldsson
  • Mattias Ohlsson
  • Lars Edenbrandt
Article

Abstract

Background

Artificial neural networks have successfully been applied for automated interpretation of myocardial perfusion images. So far the networks have used data from the myocardial perfusion images only. The purpose of this study was to investigate whether the automated interpretation of myocardial perfusion images with the use of artificial neural networks was improved if clinical data were assessed in addition to the perfusion images.

Methods and Results

A population of 229 patients who had undergone both rest-stress myocardial perfusion scintigraphy in conjunction with an exercise test and coronary angiography, with no more than 3 months elapsing between the 2 examinations, were studied. The networks were trained to detect coronary artery disease or myocardial ischemia with the use of 2 different gold standards. The first was based on coronary angiography, and the second was based on all data available (including perfusion scintigrams, coronary angiography, exercise test, resting electrocardiography, patient history, etc). The performance of the neural networks was quantified as areas under the receiver operating characteristic curves. The results showed that the neural networks trained with perfusion images performed better than those trained with exercise data (0.78 vs 0.55, P < .0001), with coronary angiography used as the gold standard. Furthermore, the networks did not improve when data from the exercise test were used as input in addition to the perfusion images (0.78 vs 0.77, P = .6).

Conclusions

The results show that the clinically important information in combined exercise test and myocardial scintigraphy could be found in the perfusion images. Exercise test information did not improve upon the accuracy of automated neural network interpretation of myocardial perfusion images in a receiver operator characteristic analysis of test accuracy. (J Nucl Cardiol 2002;9:169–73.)

Key Words

Computer-assisted diagnosis artificial intelligence neural networks ischemic heart disease 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Fujita H, Katafuchi T, Uehara T, Nishimura T. Application of artificial neural network to computer-aided diagnosis of coronary artery disease in myocardial SPECT bull’s-eye images. J Nucl Med 1992;33:272–6.PubMedGoogle Scholar
  2. 2.
    Porenta G, Dorffner G, Kundrat S, Petta P, Duit-Schedlmayer J, Sochor H. Automated interpretation of planar thallium-201-dipyridamole stress-redistribution scintigrams using artificial neural networks. J Nucl Med 1994;35:2041–7.PubMedGoogle Scholar
  3. 3.
    Hamilton D, Riley PJ, Miola UJ, Amro AA. A feed forward network for classification of bull’s-eye myocardial perfusion images. Eur J Nucl Med 1995;22:108–15.PubMedCrossRefGoogle Scholar
  4. 4.
    Lindahl D, Palmer J, Ohlsson M, Peterson C, Lundin A, Edenbrandt L. Automated interpretation of SPECT myocardial perfusion images using artificial neural networks. J Nucl Med 1997;38:1870–5.PubMedGoogle Scholar
  5. 5.
    Lindahl D, Lanke J, Lundin A, Palmer J, Edenbrandt L. Improved classifications of myocardial bull’s-eye scintigrams with computer-based decision support system. J Nucl Med 1999; 40:96–101.PubMedGoogle Scholar
  6. 6.
    Lindahl D, Toft J, Hesse B, Palmer J, Ali S, Lundin A, et al. Scandinavian test of artificial neural network for classification of myocardial perfusion images. Clin Physiol 2000;20:253–61.PubMedCrossRefGoogle Scholar
  7. 7.
    Jarund A, Edenbrandt L, Ohlsson M, Boralv E. Internet based artificial neural networks for the interpretation of medical images. In: Malmgren H, Borga M, Niklasson L, eds. Artificial neural networks in medicine and biology. Springer-Verlag: London, 2000:p. 87–92.Google Scholar
  8. 8.
    Åstrand I. Aerobic work capacity in men and women with special reference to age. Acta Physiol Scand 1960;49.Google Scholar
  9. 9.
    Neal RM. Bayesian training of backpropagation networks by the hybrid Monte Carlo method. Technical report CRG-TR-92-1. Toronto, Ontario, Canada: Department of Computer Science, University of Toronto; 1992.Google Scholar
  10. 10.
    Neal RM. Software for flexible Bayesian modeling and Markov chain sampling. Available at Accessed October 1, 2000.Google Scholar
  11. 11.
    Neal RM. Bayesian learning for neural networks [PhD thesis]. Toronto, Ontario, Canada: Department of Computer Science, University of Toronto; 1995.Google Scholar
  12. 12.
    Nordenfelt I, Adolfsson L, Nilsson JE, Olsson S. Reference values for exercise tests with continuous increase in load. Clin Physiol 1985;5:161–72.PubMedCrossRefGoogle Scholar
  13. 13.
    Wehrens R, Putter H, Buydens LMC. The bootstrap. a tutorial. Chemom Intell Lab Syst 2000;54:35–52.CrossRefGoogle Scholar

Copyright information

© American Society of Nuclear Cardiology 2002

Authors and Affiliations

  • Henrik Haraldsson
    • 1
  • Mattias Ohlsson
    • 1
  • Lars Edenbrandt
    • 2
  1. 1.Complex Systems Division ,Department of Theoretical PhysicsLund University HospitalLundSweden
  2. 2.Department of Clinical PhysiologyLund University HospitalLundSweden

Personalised recommendations