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

Artificial neural networks are systems of elementary computing units capabl e of learning from examples. They have been applied to automated interpreta tion of myocardial perfusion images and have been shown to perform even bet ter than experienced physicians. It has been shown that physicians interpre ting myocardial perfusion images benefit from the advice of such networks. These networks have been developed and validated in the same hospital. Howe ver, widespread use of neural networks will only take place if the networks can maintain a high accuracy in other hospitals, i.e. hospitals using diff erent gamma cameras, different acquisition techniques, different study prot ocols, etc. The purpose of this study was to develop a neural network in on e hospital and test it in another. An artificial neural network was trained to detect coronary artery disease using myocardial perfusion scintigrams f rom 135 patients at a Swedish hospital. Thereafter, this network was tested using scintigrams from 68 patients at a Danish hospital and compared to si x 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 t he CEqual-based criteria (0.94 versus 0.63 and 0.96 versus 0.65) compared a t equal specificities. It was concluded that an artificial neural network c an maintain high accuracy in a hospital other than the one where it was dev eloped.