Neural network technology to predict intracellular water volume.
Artificial neural network (ANN) is increasingly applied in clinical medicine. We therefore constructed an ANN to predict intracellular water (ICW) volume in 44 healthy Taiwaners. Demographic and anthropometric data were recorded as predictors, and ICW volume measured by bioelectrical impedance analysis (ICW-BIA) was the reference. ICW volume predicted by ANN (ICW-ANN) was compared with ICW-BIA. ICW-BIA (21.26 +/- 0.58 l) and ICW-ANN (21.25 +/- 0.57 l) was insignificantly different (p = 0.76). ICW-BIA and ICW-ANN were strongly correlated (r = 0.94, p < 0.0001) with a significant agreement (mean difference, 0.01; lower and upper limits of agreement, -2.31 and 2.33) in Bland-Altman plot. Passing-Bablok regression was described as ICW-BIA = 1.04 x ICW-ANN - 0.49, with 95% confidence interval for slope 0.94-1.14 and for intercept -2.76-1.49, indicating that both methods were interchangeable. ANN provided an excellent alternative of BIA to predict ICW volume in healthy subjects.
Chiu
J-S
JS
Department of Nuclear Medicine, Taipei Medical University, Taipei City, Taiwan.
Chen
C-A
CA
Lee
C-H
CH
Li
Y-C
YC
Lin
Y-F
YF
Wang
Y-F
YF
Yu
F-C
FC
D000328:Adult; D000368:Aged; D000886:Anthropometry; D001834:Body Water / Q000737:chemistry*; D017097:Electric Impedance; D005260:Female; D006801:Humans; D007424:Intracellular Fluid / Q000737:chemistry*; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D012044:Regression Analysis