In this paper, we propose a standardized framework for automatic tuberculosis (TB) detection from cough audio and routinely collected clinical data using machine learning. While TB screening from audio has attracted growing interest, progress is difficult to measure because existing studies vary substantially in datasets, cohort definitions, feature representations, model families, validation protocols, and reported metrics. Consequently, reported gains are often not directly comparable, and it remains unclear whether improvements stem from modeling advances or from differences in data and evaluation. We address this gap by establishing a strong, well-documented baseline for TB prediction using cough recordings and accompanying clinical metadata from a recently compiled dataset from several countries. Our pipeline is reproducible end-to-end, covering feature extraction, multimodal fusion, cougher-independent evaluation, and uncertainty quantification, and it reports a consistent suite of clinically relevant metrics to enable fair comparison. We further quantify performance for cough audio-only and fused (audio + clinical metadata) models, and release the full experimental protocol to facilitate benchmarking. This baseline is intended to serve as a common reference point and to reduce methodological variance that currently holds back progress in the field.

翻译：本文提出了一种利用机器学习从咳嗽音频及常规采集的临床数据中自动检测结核病的标准化框架。尽管基于音频的结核病筛查日益受到关注，但由于现有研究在数据集、队列定义、特征表示、模型族、验证协议及报告指标等方面存在显著差异，其进展难以衡量。因此，报告的性能提升往往无法直接比较，且改进究竟源于建模进展还是源于数据与评估方式的差异仍不明确。为弥补这一空白，我们基于近期整合的多国数据集中的咳嗽录音及伴随的临床元数据，建立了一个稳健且文档完善的结核病预测基线。我们的流程可实现端到端的复现，涵盖特征提取、多模态融合、咳嗽者无关的评估以及不确定性量化，并报告了一套一致的临床相关指标以实现公平比较。我们进一步量化了仅使用咳嗽音频的模型以及融合（音频+临床元数据）模型的性能，并公开完整的实验方案以促进基准测试。该基线旨在提供一个共同的参考点，并减少当前阻碍该领域进展的方法学差异。