Scandinavian test of artificial neural network for classification of myocardial perfusion images

Autor: Anders Lundin, S Ali, John Palmer, Lars Edenbrandt, Birger Hesse, Jens Toft, Dan Lindahl
Rok vydání: 2000
Předmět:
Zdroj: Clinical Physiology. 20:253-261
ISSN: 0144-5979
Popis: Artificial neural networks are systems of elementary computing units capable of learning from examples. They have been applied to automated interpretation of myocardial perfusion images and have been shown to perform even better than experienced physicians. It has been shown that physicians interpreting myocardial perfusion images benefit from the advice of such networks. These networks have been developed and validated in the same hospital. However, widespread use of neural networks will only take place if the networks can maintain a high accuracy in other hospitals, i.e. hospitals using different gamma cameras, different acquisition techniques, different study protocols, etc. The purpose of this study was to develop a neural network in one hospital and test it in another. An artificial neural network was trained to detect coronary artery disease using myocardial perfusion scintigrams from 135 patients at a Swedish hospital. Thereafter, this network was tested using scintigrams from 68 patients at a Danish hospital and compared to six criteria based on expert physician analysis and quantitative analysis by the CEqual program. The sensitivity of the network was significantly higher than that of one of the physician criteria (0. 92 versus 0.71) and two of the CEqual-based criteria (0.94 versus 0. 63 and 0.96 versus 0.65) compared at equal specificities. It was concluded that an artificial neural network can maintain high accuracy in a hospital other than the one where it was developed.
Databáze: OpenAIRE
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