Artificial neural networks classify myocardial perfusion images

In the studies of this thesis, a method for automated classification of myocardial perfusion images was successfully developed and evaluated. The results show that the method, based on artificial neural networks, was equally good, or even better than human experts. It was also found that physicians interpreting myocardial perfusion images benefit from the advice of the artificial neural networks. Furthermore the neural networks could be trained to present clinical interpretations including the information regarding extent and severity of reversible and irreversible defects. At last, it was shown that the networks can maintain it's high accuracy also in a hospital separate from where it was developed. In conclusion, these studies show the feasibility of developing a decision-support system for the interpretation of myocardial perfusion images. The clinical implication of such a decision-support system could be significant. For patients newly presenting with possible coronary artery disease, it has been shown that investigative strategies using myocardial perfusion imaging are more cost-effective than strategies that don't. Decision support for the interpretation of myocardial perfusion images, such as artificial neural network, could further improve this cost-effectiveness