期刊
FUTURE INTERNET
卷 14, 期 9, 页码 -出版社
MDPI
DOI: 10.3390/fi14090247
关键词
machine learning regression; tracheobronchial airways; deposition prediction; drug delivery; inhalation toxicology; aerosol therapy
This study develops a novel machine learning model to accurately predict particle deposition in the lower airways. The model reduces computational time and uses four ML regression models, with the MLP model displaying the most accurate estimates. The model predicts deposition efficiency for different particle sizes and flow rates, as well as lobe-specific efficiency for various flow rates.
The main challenge of the health risk assessment of the aerosol transport and deposition to the lower airways is the high computational cost. A standard large-scale airway model needs a week to a month of computational time in a high-performance computing system. Therefore, developing an innovative tool that accurately predicts transport behaviour and reduces computational time is essential. This study aims to develop a novel and innovative machine learning (ML) model to predict particle deposition to the lower airways. The first-ever study uses ML techniques to explore the pulmonary aerosol TD in a digital 17-generation airway model. The ML model uses the computational data for a 17-generation airway model and four standard ML regression models are used to save the computational cost. Random forest (RF), k-nearest neighbour (k-NN), multi-layer perceptron (MLP) and Gaussian process regression (GPR) techniques are used to develop the ML models. The MLP regression model displays more accurate estimates than other ML models. Finally, a prediction model is developed, and the results are significantly closer to the measured values. The prediction model predicts the deposition efficiency (DE) for different particle sizes and flow rates. A comprehensive lobe-specific DE is also predicted for various flow rates. This first-ever aerosol transport prediction model can accurately predict the DE in different regions of the airways in a couple of minutes. This innovative approach and accurate prediction will improve the literature and knowledge of the field.
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