Nasal air resistance is a key indicator of respiratory health and is essential for understanding nasal physiology and functions. Accurately measuring this quantity, however, remains challenging both experimentally and computationally. Data-driven methods, particularly deep learning models, offer a promising avenue for the rapid and reliable prediction of flow features, but they require large and diverse training datasets to generalize effectively to unseen cases. This study has two primary objectives: first, to develop machine learning models for respiratory flow simulations capable of accurately predicting the air resistance; and second, to introduce a data augmentation strategy for generating large virtual populations from a limited number of real patient geometries. Due to the complex and unstructured nature of nasal cavity geometries, training samples are represented as graphs, allowing direct use of computational fluid dynamic simulations as model inputs. The model is implemented as a distributed graph convolutional neural network to efficiently handle large-scale datasets, demonstrated here with 8000 graphs and scalable to even larger populations. Results show that the model achieves an R2 score of 0.999 in predicting the pressure drop, and that the prediction error on unseen cases decreases substantially as the virtual population is expanded from a limited set of real geometries.
Calmet et al. (2026) studied this question.