GVU Technical Report Number:
GIT-GVU-91-24
Title:
A Neural Network Approach to Assess Myocardial Infarction
Authors:
A. Panos
V. Maojo
F. Martin
N. Ezquerra
Abstract:
The assessment of myocardial infarction is a complex information
intensive process that involves the analysis and interpretation of
cardiovascular nuclear medicine images. For a number of years, a
knowledge-based approach has been under development jointly between
Georgia Tech and Emory University to assist in making this clinical
assessment, using images obtained from Thallium-201 single-photon
emission computed tomography (SPECT) images. This paper discusses recent
attempts to extend this knowledge-based system to incorporate the concept
of myocardial thickening as a possible measure of myocardial viability,
using Tc-99m and connectionist methods. The implementation of neural
networks, its linkage to the knowledge-based system, and the use of
Sestamibi Tc-99 (instead of T1-201 imagery), introduce novel informatics
methods to diagnostic cardiology.
Keywords:
Myocardial infarction, medical imaging, SPECT, knowledge-based systems,
neural imagery, diagnostic cardiology
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