OpenSLM: Introduction to stochastic lung model and website

Background and ObjectiveInhaled aerosols may pose varying health risks depending on their deposition patterns in the human respiratory system. Accurate estimation of particle deposition requires models that account for individual anatomical variability and realistic breathing patterns. The Stochastic Lung Model (SLM), addresses these challenges using Monte Carlo methods to simulate particle transport through randomly generated airway geometries based on measured anatomical data. This stochastic approach improves upon traditional deterministic models by capturing intra- and inter-individual variability in airway structure.MethodsTo make this advanced modelling tool accessible to researchers, healthcare professionals, and any other interested users we developed OpenSLM, a web-based platform that enables users to simulate aerosol deposition by customizing input parameters such as particle size, density, and breathing characteristics. The platform, implemented with Rust for computational efficiency and a web interface for ease of use, provides downloadable results detailing regional and airway generation-specific deposition patterns.ResultsOpenSLM supports applications ranging from environmental exposure assessment to personalized inhalation therapy planning, contributing to improved risk assessment and therapeutic optimization. By making high precision simulation tools publicly accessible, OpenSLM promotes broader adoption of respiratory modeling in scientific research, education, and clinical practice.ConclusionsThis paper presents an overview of the numerical basis of SLM, details of its Rust-based implementation, and the architecture of the OpenSLM web platform, highlighting its potential to advance public health and environmental safety through accessible and reproducible aerosol deposition simulations, in alignment with the FAIR (findable, accessible, interoperable, reusable) principles.