Artificial neural network prediction of aerosol deposition in human lungs.
OBJECTIVE
To develop a rapid and reliable method for predicting the pattern of aerosol particle deposition within the human lungs, using artificial neural networks (ANNs).


METHODS
Experimental data from the literature were used to train multi-layer perceptron (MLP) networks to allow for prediction of regional and total aerosol particle deposition patterns in human lungs. These data covered particle sizes in the range 0.05-15 microm and three different breathing patterns (ranging from "quiet" breathing to breathing "under physical work conditions"). Three different MLPs were trained, to provide separate predictions of aerosol particle deposition in the laryngeal, bronchial, and alveolar regions. The total deposition fraction for a given set of breathing conditions was computed simply as the sum of the outputs produced from the corresponding regional deposition MLPs.


RESULTS
The ANNs developed are shown to give highly accurate predictions for both regional and total aerosol deposition patterns for all particle sizes and breathing conditions (with errors typically less than 0.04%).


CONCLUSIONS
We conclude that the current set of ANNs can be used to give good predictions of particle deposition from polydisperse pharmaceutical aerosols generated from breath-actuated dry powder inhalers, nebulizers, and metered dose inhalers with spacers.
Nazir
Javed
J
Department of Pharmacy, King's College, London, United Kingdom.
Barlow
David J
DJ
Lawrence
M Jayne
MJ
Richardson
Christopher J
CJ
Shrubb
Ian
I
D000336:Aerosols / Q000493:pharmacokinetics*; D006801:Humans; D008168:Lung / Q000378:metabolism*; D016571:Neural Networks, Computer; D010316:Particle Size; D011237:Predictive Value of Tests
D000336:Aerosols