Transformers for rapid detection of airway stenosis and stridor

Abstract

Upper airway stenosis is a potentially life-threatening condition involving the narrowing of the airway. In more severe cases, airway stenosis may be accompanied by stridor, a type of disordered breathing caused by turbulent airflow. Patients with airway stenosis have a higher risk of airway failure and additional precautions must be taken before medical interventions like intubation. However, stenosis and stridor are often misdiagnosed as other respiratory conditions like asthma/wheezing, worsening outcomes. This report presents a unified dataset containing recorded breathing tasks from patients with stridor and airway stenosis. Customized transformer-based models were also trained to perform stenosis and stridor detection tasks using low-cost data from multiple acoustic prompts recorded on common devices. These methods achieved AUC scores of 0.875 for stenosis detection and 0.864 for stridor detection, demonstrating the potential to add value as screening tools in real-world clinical workflows, particularly in high-volume settings like emergency departments.

Fig. 1

The data processing and modeling pipeline for stenosis and stridor detection.

Fig. 2

Left: mean ROC curve for the airway stenosis detection model. Right: Model performance (mean AUC score) across age and gender identity groups.

Fig. 3

Left: mean ROC-AUC curve for the stridor detection model. Right: Model performance (mean AUC score) across age and gender identity groups.