open access

Vol 7, No 1 (2004)
Published online: 2004-01-22
Submitted: 2012-01-23
Get Citation

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

Bogusław Stefaniak, Witold Cholewiński, Anna Tarkowska
Nucl. Med. Rev 2004;7(1):7-12.

open access

Vol 7, No 1 (2004)
Published online: 2004-01-22
Submitted: 2012-01-23

Abstract

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.

Abstract

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.
Get Citation

Keywords

myocardial perfusion; polar map; LVEF; artificial neural networks

About this article
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

Nuclear Medicine Review

Issue

Vol 7, No 1 (2004)

Pages

7-12

Published online

2004-01-22

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

Important: This website uses cookies. More >>

The cookies allow us to identify your computer and find out details about your last visit. They remembering whether you've visited the site before, so that you remain logged in - or to help us work out how many new website visitors we get each month. Most internet browsers accept cookies automatically, but you can change the settings of your browser to erase cookies or prevent automatic acceptance if you prefer.

By "Via Medica sp. z o.o." sp.k., Świętokrzyska 73 street, 80–180 Gdańsk, Poland

tel.:+48 58 320 94 94, faks:+48 58 320 94 60, e-mail: viamedica@viamedica.pl