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Prediction of left ventricular ejection fraction in patients with coronary artery disease based on an analysis of perfusion patterns at rest. Assessment by an artificial neural network
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Abstract
MATERIAL AND METHODS: gSPECT examinations were performed in 95 patients with CAD, divided into training (n = 50) and testing (n = 45) groups. using the acquired data, in each subject the LVEF was calculated and a perfusion polar map was constructed and divided into 25 segments. Based on results obtained in the training group, a characteristic configuration of segments was defined, with features enabling differentiation between the individual subjects of that group. The set of those segments, as well as the corresponding LVEF values enabled the optimum network architecture to be constructed and trained. The trained ANN was verified by application to the testing group.
RESULTS: Using the above-described procedure, 15 polar map segments were defined which enabled the patients of the training group to be differentiated sufficiently enough to make their further recognition possible. The optimal network structure consisting 25 neurons was obtained by comparing the activity in those segments in individual subjects with corresponding LVEF values. Based on the above model, the obtained network was able to reproduce learning data (r = 0.832; learning error = 4.84%) and to apply the gained knowledge to the testing cases (r = 0.786; testing error = 4.99%).
CONCLUSIONS: The obtained network can generalise learned information. To predict LVEF, some polar map segments should be excluded from the analysis. Erroneous LVEF prediction may occur resulting mainly from conditions independent from perfusion abnormalities.
Abstract
MATERIAL AND METHODS: gSPECT examinations were performed in 95 patients with CAD, divided into training (n = 50) and testing (n = 45) groups. using the acquired data, in each subject the LVEF was calculated and a perfusion polar map was constructed and divided into 25 segments. Based on results obtained in the training group, a characteristic configuration of segments was defined, with features enabling differentiation between the individual subjects of that group. The set of those segments, as well as the corresponding LVEF values enabled the optimum network architecture to be constructed and trained. The trained ANN was verified by application to the testing group.
RESULTS: Using the above-described procedure, 15 polar map segments were defined which enabled the patients of the training group to be differentiated sufficiently enough to make their further recognition possible. The optimal network structure consisting 25 neurons was obtained by comparing the activity in those segments in individual subjects with corresponding LVEF values. Based on the above model, the obtained network was able to reproduce learning data (r = 0.832; learning error = 4.84%) and to apply the gained knowledge to the testing cases (r = 0.786; testing error = 4.99%).
CONCLUSIONS: The obtained network can generalise learned information. To predict LVEF, some polar map segments should be excluded from the analysis. Erroneous LVEF prediction may occur resulting mainly from conditions independent from perfusion abnormalities.
Keywords
myocardial perfusion; polar map; LVEF; artificial neural networks
Title
Prediction of left ventricular ejection fraction in patients with coronary artery disease based on an analysis of perfusion patterns at rest. Assessment by an artificial neural network
Journal
Issue
Pages
7-12
Published online
2004-01-22
Page views
581
Article views/downloads
1135
Bibliographic record
Nucl. Med. Rev 2004;7(1):7-12.
Keywords
myocardial perfusion
polar map
LVEF
artificial neural networks
Authors
Bogusław Stefaniak
Witold Cholewiński
Anna Tarkowska