Acoustic AI detects airway narrowing from patient speech recordings
Listening for Airway Stenosis: A Foundation Model-Based Method for Rapid and Accessible Detection
Sound
Summary
Breathing problems caused by narrowed airways can be hard to detect without special medical tests. This study shows that artificial intelligence can listen to a person's voice and quickly spot signs of this issue. The researchers used voice recordings from many people and found their AI model could accurately tell who had airway narrowing. This approach could help doctors screen patients more easily, without needing complex equipment.
What this means in practice
- •For primary care providers: Screen patients for airway narrowing using simple voice recordings during checkups to prioritize referrals for specialized exams.
- •For telehealth service teams: Incorporate voice-based screening tools into remote consultations to flag possible airway stenosis without in-person visits.
Authors
Jean Groeninger, Zihao Zhao, Juliana de Castilhos, Sven Nebelung, Daniel Truhn
Abstract
Airway stenosis can cause severe respiratory complications, yet its detection often relies on specialized examinations and medical imaging. This study explores the potential of acoustic AI for rapid and accessible airway stenosis detection using readily acquired patient voice recordings. We systematically investigate whether acoustic foundation models (AFMs) can extract acoustic representations associated with airway stenosis-related speech patterns. Experiments are conducted on a cohort of 748 participants from the Bridge2AI-Voice dataset, 134 with airway stenosis and 614 without. The best-performing model achieves an AUROC of 0.952 and an accuracy of 0.924 (means over five-fold cross-validation), highlighting the potential of AFMs to transfer beyond general-purpose speech applications to clinical diagnostic tasks. Further analysis reveals that the model primarily relies on connected-speech recordings rather than isolated acoustic tasks, such as sustained phonation and breathing. Overall, these results suggest that voice-based acoustic AI could complement existing diagnostic workflows by enabling rapid, low-burden, and widely accessible screening for airway stenosis.