A Neural Network Approach to Assess Myocardial Infarction

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dc.contributor.author Pazos, Alejandro
dc.contributor.author Maojo, Victor
dc.contributor.author Martin, Fernando
dc.contributor.author Ezquerra, Norberto F.
dc.date.accessioned 2004-12-07T18:09:15Z
dc.date.available 2004-12-07T18:09:15Z
dc.date.issued 1991
dc.identifier.uri http://hdl.handle.net/1853/3717
dc.description A. Panos is a typographical error, author is actually Alejandro Pazos, University of Coruna.
dc.description.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. en
dc.format.extent 1511965 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.publisher Georgia Institute of Technology en
dc.relation.ispartofseries GVU Technical Report;GIT-GVU-91-24
dc.subject Myocardial infarction en
dc.subject Medical imaging en
dc.subject SPECT en
dc.subject Knowledge-based systems en
dc.subject Neural imagery en
dc.subject Diagnostic cardiology en
dc.title A Neural Network Approach to Assess Myocardial Infarction en
dc.type Technical Report en

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