Nuclear medicine review. Central & Eastern Europe · 2004
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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Stefaniak Bogusław, Cholewiński Witold, Tarkowska Anna
Аннотация
BACKGROUND: In CAD, left ventricular function depends on the condition of myocardial perfusion, hence it may be presumed that blood flow abnormalities may enable the LVEF to be predicted. The aim of the study was to apply an Artificial Neural Network (ANN) to investigate the relationships between myocardial perfusion and LVEF, measured simultaneously.
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.
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.
Предметные рубрики MeSH
Artificial IntelligenceCoronary Artery DiseaseGated Blood-Pool ImagingHeart VentriclesHumansImage Interpretation, Computer-AssistedNeural Networks, ComputerPattern Recognition, AutomatedReproducibility of ResultsSensitivity and SpecificityStroke VolumeTomography, Emission-Computed, Single-PhotonVentricular Dysfunction, Left