Gerold Porenta

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In this paper we present an extensive comparison between several feedforward neural network types in the context of a clinical diagnostic task, namely the detection of coronary artery disease (CAD) using planar thallium-201 dipyridamole stress-redistribution scintigrams. We introduce results from well-known (e.g. multilayer perceptrons or MLPs, and radial(More)
Myocardial perfusion scintigraphy is a noninvasive diagnostic method for the evaluation of patients with suspected or proven coronary artery disease (CAD). We utilized case-based reasoning (CBR) methods to develop the computer-based image interpretation system SCINA which automatically derives from a scintigraphic image data set an assessment concerning the(More)
Neural networks are usually seen as obtaining all their knowledge through training on the basis of examples. In many AI applications appropriate for neural networks, however, symbolic knowledge does exist which describes a large number of cases relatively well, or at least contributes to partial solutions. From a practical point of view it appears to be a(More)
OBJECTIVE This study compared the diagnostic accuracy of different approaches of case-based reasoning (CBR) for the assessment of coronary artery disease (CAD) using thallium-201 myocardial perfusion scintigraphy in comparison with coronary angiography. METHODS AND MATERIAL For each scintigraphic image set, regional myocardial tracer uptake was obtained(More)
UNLABELLED This study presents and evaluates a model-based image analysis method to calculate from gated cardiac (18)F-FDG PET images diastolic and systolic volumes, ejection fraction, and myocardial mass of the left ventricle. The accuracy of these estimates was delineated using measurements obtained by MRI, which was considered the reference standard(More)
In case-based studies, controls are retrospectively assigned to patients in order to permit a statistical evaluation of the study results through a comparison of the main outcome measures for the patient and retrieved control groups. Inappropriate selection of the controls by using false retrieval parameters or a false algorithm might lead to an incorrect(More)
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