Title:
A Neural Network Approach to Assess Myocardial Infarction
A Neural Network Approach to Assess Myocardial Infarction
| 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.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.identifier.uri | http://hdl.handle.net/1853/3717 | |
| 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 | Text | |
| dc.type.genre | Technical Report | |
| dspace.entity.type | Publication | |
| local.contributor.corporatename | GVU Center | |
| local.relation.ispartofseries | GVU Technical Report Series | |
| relation.isOrgUnitOfPublication | d5666874-cf8d-45f6-8017-3781c955500f | |
| relation.isSeriesOfPublication | a13d1649-8f8b-4a59-9dec-d602fa26bc32 |