Assessment of quantitative artificial neural network analysis in a metabolically dynamicex vivo31p NMR pig liver study
| Autor: | Simon D. Taylor-Robinson, Mika Ala-Korpela, Brian R. Davidson, Jimmy D. Bell, K. Kumar Changani, David J. Bryant, Yrjö Hiltunen, Barry Fuller |
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| Rok vydání: | 1997 |
| Předmět: |
Magnetic Resonance Spectroscopy
Artificial neural network Swine Metabolite Analytical chemistry Biology chemistry.chemical_compound Animal model Liver chemistry Animals Radiology Nuclear Medicine and imaging Neural Networks Computer Biological system Quantitative analysis (chemistry) Pig liver Ex vivo |
| Zdroj: | Magnetic Resonance in Medicine. 38:840-844 |
| ISSN: | 1522-2594 0740-3194 |
| DOI: | 10.1002/mrm.1910380522 |
| Popis: | Quantitative artificial neural network analysis for 1550 ex vivo 31P nuclear magnetic resonance spectra from hypothermically reperfused pig livers was assessed. These spectra show wide ranges of metabolite concentrations and have been analyzed using metabolite prior knowledge based lineshape fitting analysis which had proved robust in its biochemical interpretation. This finding provided a good opportunity to assess the performance of artificial neural network analysis in a biochemically complex situation. The results showed high correlations (0.865or = Ror = 0.992) between the lineshape fitting and artificial neural network analysis for the metabolite values, and the artificial neural network analysis was able to fully represent the trends in the metabolic fluctuations during the experiments. |
| Databáze: | OpenAIRE |
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