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Assessment of quantitative artificial neural network analysis in a metabolically dynamic ex vivo 31 p NMR pig liver study
Author(s) -
AlaKorpela Mika,
Changani K. K.,
Hiltunen Y.,
Bell J. D.,
Fuller B. J.,
Bryant David J.,
TaylorRobinson S. D.,
Davidson B. R.
Publication year - 1997
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.1910380522
Subject(s) - artificial neural network , metabolite , artificial intelligence , biological system , nuclear magnetic resonance , interpretation (philosophy) , pattern recognition (psychology) , computer science , chemistry , biology , physics , biochemistry , programming language
Quantitative artificial neural network analysis for 1550 ex vivo 31 P 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 (0.8655 ≤ R ≤ 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.