Enhancing airway obstruction diagnosis with multimodal 3D shape analysis

Enlarged adenoids that obstruct nasal breathing can cause significant health complications, including cognitive deficits, cardiovascular risks, and developmental delays. Early and accurate diagnosis is critical for effective treatment planning, but current diagnostic methods-such as polysomnography and clinical visual inspection-are either time-consuming, expensive, or lack sufficient accuracy. As cone-beam computed tomography (CBCT) scans are frequently available for these patients and may complement diagnosis, we propose an open-source, automated deep learning tool for quantitative airway obstruction assessment. Our method leverages CBCT scans, which are automatically segmented and processed to extract 3D airway morphology.

Our approach combines two advanced techniques for 3D shape analysis: multi-view and point cloud representations to capture both global and local airway features, enhancing classification and regression performance.

Our model achieves an accuracy of 81.88% in classifying the presence or absence of adenoid hypertrophy and demonstrates improved performance in predicting the nasopharynx airway obstruction ratio. While the model performs well in detecting severe cases, further refinement is needed to improve classification and regression across all severity levels.

This tool has the potential to enhance clinical workflows by providing rapid, quantitative, and reproducible assessments of airway obstruction, offering a promising solution for improving diagnostic efficiency and patient outcomes in clinical practice.