GIT-GVU-92-06

PERFEX: An Expert System for Interpreting 3D Myocardial Perfusion

Norberto Ezquerra
Rakesh Mullick
David Cooke
Ernest Garcia
Elizabeth Krawczynska

Interpreting three-dimensional (3D) data is generally recognized as an ill-defined and informationintensive task. The task becomes increasingly difficult in the context of medical diagnostic imagery, wherein the visual information must be interpreted in conjunction with other, non-visual information. A novel approach is presented to perform the interpretation of such multidimensional information, concentrating on a medically important application: the interpretation of 3D tomograms of myocardial perfusion distribution. The overall goal is to assist in the diagnosis of coronary artery disease. The approach employs knowledge-based methods to process and map the 3D visual information into symbolic representations, which are subsequently used to infer structure (anatomy) from function (physiology), as well as to interpret the temporal effects of perfusion redistribution, and assess the extent and severity of cardiovascular disease both quantitatively and qualitatively. The knowledge-based system presents the resulting diagnostic recommendations in both visual and textual forms in an interactive framework, thereby enhancing overall utility. This article presents the methodology underlying this approach, including the implementation and testing of this system within an actual clinical environment.

Knowledge-based systems, medical expert systems, artificial intelligence in medicine

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