A Novel Machine Learning Prediction Model for Aerosol Transport in Upper 17-Generations of the Human Respiratory Tract

Mohammad S. Islam (), Shahid Husain, Jawed Mustafa and Yuantong Gu
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Mohammad S. Islam: School of Mechanical and Mechatronic Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney (UTS), Ultimo, NSW 2007, Australia
Shahid Husain: Department of Mechanical Engineering, Zakir Husain College of Engineering & Technology, Aligarh Muslim University, Aligarh 202002, India
Jawed Mustafa: Mechanical Engineering Department, College of Engineering, Najran University, Najran P.O Box 1988, Saudi Arabia
Yuantong Gu: Faculty of Engineering, School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia

Future Internet, 2022, vol. 14, issue 9, 1-16

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.

Keywords: machine learning regression; tracheobronchial airways; deposition prediction; drug delivery; inhalation toxicology; aerosol therapy (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
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