Why ML-Based Cough Models Do Not Generalize: A Systematic Cross-Dataset Evaluation for Tuberculosis Screening
Score
73首次系统揭示咳嗽声学模型跨数据集泛化失败的具体机制，填补了该领域外部验证研究的空白。
研究设计严谨，多数据集、多流水线对照，分析维度全面，结果呈现清晰。
对咳嗽筛查模型开发者提供明确的验证标准和设备多样性训练建议，可直接指导实践。
AI医疗落地需求紧迫，但该主题面向特定疾病领域，时效性中等。
arXiv预印本经作者团队多轮实验验证，数据和方法详实，但未经同行评审。
Base 81.5 × T2 Source weight ×0.9 = 73
Curation line 70Selected
Why selected
The study is rigorously designed with large-scale data, but the topic is niche to TB cough screening, with moderate impact on the broader AI community.
AI commentary
「This paper is a must-read for anyone working in medical AI audio analysis—it shatters the illusion that high internal accuracy is enough with a simple three-dataset validation. For teams building cough screening products, device diversity and external validation are not optional; they are a matter of survival.」
Quick overview
This paper systematically evaluates the cross-dataset generalizability of machine learning and deep learning cough-based tuberculosis screening classifiers across three independent datasets. Despite moderate within-dataset performance (ROC-AUC up to 0.755 ± 0.056), external performance frequently drops below 0.6, indicating models largely learn artifacts of recording devices and data collection rather than disease-related acoustic features. Audio representations cluster by device and dataset rather than TB status; device mismatch degrades transfer, while device-diverse training improves generalization. A clinical-variable baseline generalizes more consistently (ROC-AUC 0.655-0.711), suggesting acquisition-specific variability is a stronger driver of poor generalizability than population shift. The study emphasizes that external validation is essential before cough-based TB models are clinically ready.
- A systematic evaluation across three independent datasets shows that cough-based TB classifiers generalize poorly, with external ROC-AUC frequently below 0.6.
- Audio representations cluster by recording device and dataset rather than TB status, indicating models capture acquisition artifacts rather than disease signals.
- Device mismatch significantly degrades transfer, while device-diverse training improves generalization; a clinical-variable baseline outperforms acoustic models in stability.
- The study warns that high within-dataset performance is insufficient for clinical deployment; external validation is a necessary prerequisite.
Full text
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