这篇论文详细评估了咳嗽结核病模型的泛化能力，揭示了数据收集和设备差异对模型泛化能力的影响，为结核病筛查模型的开发提供了重要参考。
咳嗽声学在非侵入性结核病筛查中具有潜力，但机器学习（ML）模型是否捕捉到疾病相关的声学特征或数据收集的伪影尚无定论。研究人员评估了经典ML和深度学习（DL）咳嗽结核病分类器在三个独立数据集上的跨数据集泛化能力。尽管在数据集内部表现良好（ROC-AUC高达0.755±0.056），但两种方法都无法泛化，外部性能通常低于0.6，表明数据可能存在局限性。此外，音频表示由录音设备和数据集组织，而不是结核病状态，预测结核病概率与CODA国家层面的流行率一致，设备不匹配会降低迁移能力，而设备多样化的训练则会提高它。此外，临床变量基线泛化更一致（ROC-AUC 0.655-0.711），表明获取特定变量的可变性是泛化能力差的主要驱动因素。数据集内部的高性能不足以保证泛化能力。在咳嗽结核病模型临床应用之前，外部验证是必要的。
Why ML-based cough models do not generalize: a systematic cross-dataset evaluation for tuberculosis screening
Cough acoustics are promising for non-invasive tuberculosis (TB) screening, yet whether machine learning (ML) models capture disease-related acoustics or artifacts of data collection remains unresolved. We evaluated the cross-dataset generalizability of classical ML and deep learning (DL) cough-based TB classifiers across three independent datasets. Despite moderate within-dataset performance (ROC-AUC up to $0.755 \pm 0.056$), both pipelines fail to generalize, with external performance frequently below 0.6, indicating a possible limitation of the data. We further observed audio representations are organized by recording device and dataset rather than TB status, predicted TB probability tracks country-level prevalence in CODA, and device mismatch degrades transfer while device-diverse training improves it. Additionally, a clinical-variable baseline generalizes more consistently (ROC-AUC $0.655 - 0.711$), indicating acquisition-specific variability is a stronger driver of poor generalizability than population shift. High within-dataset performance is not enough. External validation is essential before cough-based TB models are clinically ready.