Abstract
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.
Original language | English |
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Article number | 247 |
Journal | Future Internet |
Volume | 14 |
Issue number | 9 |
DOIs | |
Publication status | Published - Sep 2022 |
Externally published | Yes |
Keywords
- aerosol therapy
- deposition prediction
- drug delivery
- inhalation toxicology
- machine learning regression
- tracheobronchial airways