Leveraging Computational Geometry for Data Augmentation in Medical Flow Fields Classification

The application of Machine Learning (ML) to Computational Fluid Dynamics (CFD) has gained significant attention due to its potential in speeding up simulations and approximating numerical solutions of physical equations. Still, the dominant role of ML, which is to infer expert labels that cannot be calculated from explicit equations, has received much less attention in CFD. A major challenge in this direction is the scarcity of large, annotated datasets required to train robust ML models for flow field classification. In this work, we address the problem of training a ML model to classify CFD flow fields, inferring pathologies affecting the human upper airways. We propose a novel data augmentation method to address this limitation which involves an automated pipeline to extract CFD-ready surfaces from CT scans and a computational geometry technique to generate synthetic training samples. By defining deformation functions for specific pathologies on a reference surface and mapping these to healthy anatomical surfaces, we create a large and diverse training set with minimal expert supervision. This method allows for the generation of a dataset with high anatomical variability and well-defined labels, enhancing the model’s ability to generalize to unseen geometries. We demonstrate that a Neural Network (NN) can accurately classify two common nasal pathologies, septal deviation and turbinate hypertrophy, achieving strong performance on real pathological patient data despite being trained solely on synthetic samples.