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Patient gender and radiopharmaceutical tracer is of minor importance for the interpretation of myocardial perfusion images using an artificial neural network
Author(s) -
Tägil Kristina,
Richard Underwood S.,
Davies Glyn,
Latus Katherine A.,
Ohlsson Mattias,
Götborg Cecilia Wallin,
Edenbrandt Lars
Publication year - 2006
Publication title -
clinical physiology and functional imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.608
H-Index - 67
eISSN - 1475-097X
pISSN - 1475-0961
DOI - 10.1111/j.1475-097x.2006.00668.x
Subject(s) - medicine , artificial neural network , perfusion , myocardial perfusion scintigraphy , tracer , scintigraphy , thallium , artificial intelligence , myocardial perfusion imaging , nuclear medicine , pattern recognition (psychology) , radiology , computer science , coronary angiography , chemistry , physics , myocardial infarction , nuclear physics , inorganic chemistry
Summary The purpose of this study was to assess the influence of patient gender and choice of perfusion tracer on computer‐based interpretation of myocardial perfusion images. For the image interpretation, an automated method was used based on image processing and artificial neural network techniques. A total of 1000 patients were studied, all referred to the Royal Brompton Hospital in London for myocardial perfusion scintigraphy over a period of 1 year. The patients were randomized to receive either thallium or one of the two technetium tracers, methoxyisobutylisonitrile or tetrofosmin. Artificial neural networks were trained with either mixed gender or gender‐specific and mixed tracer or tracer‐specific training sets of different sizes. The performance of the networks was assessed in separate test sets, with the interpretation of experienced physicians regarding the presence or absence of fixed or reversible defects in the images as the gold standard. The neural networks trained with large mixed gender training sets were as good as the networks trained with gender‐specific data sets. In addition, the neural networks trained with large mixed tracer training sets were as good as or better than the networks trained with tracer‐specific data sets. Our results indicate that the influence of patient gender and perfusion tracer are of minor importance for the computer‐based interpretation of the myocardial perfusion images. The differences that occur can be compensated for by larger training sets.