倾听气道狭窄：基于基础模型的快速可及检测方法
Listening for Airway Stenosis: A Foundation Model-Based Method for Rapid and Accessible Detection
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中文总结 AI 辅助
本研究利用声学基础模型分析语音记录，实现快速可及的气道狭窄检测，在748人队列中达到0.952的AUROC，证明其可补充现有诊断流程。
中文摘要 AI 辅助
气道狭窄可导致严重的呼吸系统并发症，然而其检测通常依赖于专门的检查和医学影像。本研究探索了声学人工智能在利用易于获取的患者语音记录进行快速且可及的气道狭窄检测方面的潜力。我们系统地研究了声学基础模型（AFMs）是否能够提取与气道狭窄相关语音模式相关联的声学表征。实验在来自Bridge2AI-Voice数据集的748名参与者队列上进行，其中134名患有气道狭窄，614名未患病。性能最佳的模型实现了0.952的AUROC和0.924的准确率（五次交叉验证的平均值），突显了AFMs从通用语音应用迁移到临床诊断任务的潜力。进一步分析表明，该模型主要依赖于连贯语音记录，而非孤立的声学任务，如持续发声和呼吸。总体而言，这些结果表明，基于语音的声学人工智能可以通过实现快速、低负担且广泛可及的气道狭窄筛查，来补充现有的诊断工作流程。
英文摘要
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.
发表机构
- University Hospital Aachen（亚琛大学医院）
- Télécom Paris（巴黎电信学院）
机构由 AI 辅助整理，请以论文原文为准。
