Background: This study proposes a Vision-Language Model (VLM) leveraging the SIGLIP encoder and Gemma-3b transformer decoder to enhance automated chronic tuberculosis (TB) screening. By integrating chest X-ray images with clinical data, the model addresses the challenges of manual interpretation, improving diagnostic consistency and accessibility, particularly in resource-constrained settings. Methods: The VLM architecture combines a Vision Transformer (ViT) for visual encoding and a transformer-based text encoder to process clinical context, such as patient histories and treatment records. Cross-modal attention mechanisms align radiographic features with textual information, while the Gemma-3b decoder generates comprehensive diagnostic reports. The model was pre-trained on 5 million paired medical images and texts and fine-tuned using 100,000 chronic TB-specific chest X-rays. Results: The model demonstrated high precision (94 percent) and recall (94 percent) for detecting key chronic TB pathologies, including fibrosis, calcified granulomas, and bronchiectasis. Area Under the Curve (AUC) scores exceeded 0.93, and Intersection over Union (IoU) values were above 0.91, validating its effectiveness in detecting and localizing TB-related abnormalities. Conclusion: The VLM offers a robust and scalable solution for automated chronic TB diagnosis, integrating radiographic and clinical data to deliver actionable and context-aware insights. Future work will address subtle pathologies and dataset biases to enhance the model's generalizability, ensuring equitable performance across diverse populations and healthcare settings.

翻译：背景：本研究提出一种利用SIGLIP编码器和Gemma-3b Transformer解码器的视觉语言模型（VLM），以增强慢性结核病（TB）的自动化筛查。通过整合胸部X光图像与临床数据，该模型解决了人工判读的挑战，提升了诊断的一致性和可及性，尤其在资源受限的环境中。方法：VLM架构结合了用于视觉编码的Vision Transformer（ViT）和基于Transformer的文本编码器，以处理临床背景信息（如患者病史和治疗记录）。跨模态注意力机制将影像特征与文本信息对齐，而Gemma-3b解码器则生成全面的诊断报告。该模型在500万对医学图像和文本上进行了预训练，并使用10万张慢性结核病特异性胸部X光片进行了微调。结果：该模型在检测关键慢性结核病病理特征（包括纤维化、钙化肉芽肿和支气管扩张）方面表现出高精度（94%）和召回率（94%）。曲线下面积（AUC）得分超过0.93，交并比（IoU）值高于0.91，验证了其在检测和定位结核病相关异常方面的有效性。结论：VLM为自动化慢性结核病诊断提供了一个稳健且可扩展的解决方案，通过整合影像学和临床数据，提供可操作且具有情境感知的见解。未来的工作将针对细微病理特征和数据集偏差，以增强模型的泛化能力，确保在不同人群和医疗环境中实现公平的性能。