Large-scale tuberculosis (TB) screening is limited by the high cost and operational complexity of traditional diagnostics, creating a need for artificial-intelligence solutions. We propose DeepGB-TB, a non-invasive system that instantly assigns TB risk scores using only cough audio and basic demographic data. The model couples a lightweight one-dimensional convolutional neural network for audio processing with a gradient-boosted decision tree for tabular features. Its principal innovation is a Cross-Modal Bidirectional Cross-Attention module (CM-BCA) that iteratively exchanges salient cues between modalities, emulating the way clinicians integrate symptoms and risk factors. To meet the clinical priority of minimizing missed cases, we design a Tuberculosis Risk-Balanced Loss (TRBL) that places stronger penalties on false-negative predictions, thereby reducing high-risk misclassifications. DeepGB-TB is evaluated on a diverse dataset of 1,105 patients collected across seven countries, achieving an AUROC of 0.903 and an F1-score of 0.851, representing a new state of the art. Its computational efficiency enables real-time, offline inference directly on common mobile devices, making it ideal for low-resource settings. Importantly, the system produces clinically validated explanations that promote trust and adoption by frontline health workers. By coupling AI innovation with public-health requirements for speed, affordability, and reliability, DeepGB-TB offers a tool for advancing global TB control.

翻译：大规模结核病筛查受限于传统诊断方法的高成本与操作复杂性，亟需人工智能解决方案。我们提出DeepGB-TB，一种非侵入式系统，仅利用咳嗽音频和基本人口统计学数据即可即时评估结核病风险。该模型将用于音频处理的轻量级一维卷积神经网络与用于表格特征处理的梯度提升决策树相结合。其核心创新在于一个跨模态双向交叉注意力模块，该模块通过迭代交换模态间的显著线索，模拟临床医生综合症状与风险因素的决策过程。为满足临床中最大限度减少漏诊病例的优先需求，我们设计了一种结核病风险平衡损失函数，对假阴性预测施加更强惩罚，从而降低高风险误分类。DeepGB-TB在一个涵盖七国收集的1,105名患者的多样化数据集上进行评估，取得了0.903的AUROC和0.851的F1分数，代表了新的最优性能。其计算效率支持在常见移动设备上直接进行实时离线推理，使其特别适用于资源匮乏环境。重要的是，该系统能生成经过临床验证的解释，有助于提升一线卫生工作者的信任度和采纳意愿。通过将人工智能创新与公共卫生对速度、可负担性和可靠性的要求相结合，DeepGB-TB为推进全球结核病防控提供了一项有力工具。