Sun Xin, Xing Zhiheng, Wan Zhen, Ding Wenlong, Wang Li, Zhong Lingshan, Zhou Xinran, Gong Xiu-Jun, Li Yonghui, Zhang Xiao-Dong
Haihe Hospital, Tianjin University, Tianjin, China.
Tianjin Union Medical Center, Nankai University, Tianjin, China.
Front Med (Lausanne). 2024 Jul 22;11:1391184. doi: 10.3389/fmed.2024.1391184. eCollection 2024.
Tuberculosis (TB) stands as a paramount global health concern, contributing significantly to worldwide mortality rates. Effective containment of TB requires deployment of cost-efficient screening method with limited resources. To enhance the precision of resource allocation in the global fight against TB, this research proposed chest X-ray radiography (CXR) based machine learning screening algorithms with optimization, benchmarking and tuning for the best TB subclassification tasks for clinical application.
This investigation delves into the development and evaluation of a robust ensemble deep learning framework, comprising 43 distinct models, tailored for the identification of active TB cases and the categorization of their clinical subtypes. The proposed framework is essentially an ensemble model with multiple feature extractors and one of three fusion strategies-voting, attention-based, or concatenation methods-in the fusion stage before a final classification. The comprised de-identified dataset contains records of 915 active TB patients alongside 1,276 healthy controls with subtype-specific information. Thus, the realizations of our framework are capable for diagnosis with subclass identification. The subclass tags include: secondary tuberculosis/tuberculous pleurisy; non-cavity/cavity; secondary tuberculosis only/secondary tuberculosis and tuberculous pleurisy; tuberculous pleurisy only/secondary tuberculosis and tuberculous pleurisy.
Based on the dataset and model selection and tuning, ensemble models show their capability with self-correction capability of subclass identification with rendering robust clinical predictions. The best double-CNN-extractor model with concatenation/attention fusion strategies may potentially be the successful model for subclass tasks in real application. With visualization techniques, in-depth analysis of the ensemble model's performance across different fusion strategies are verified.
The findings underscore the potential of such ensemble approaches in augmenting TB diagnostics with subclassification. Even with limited dataset, the self-correction within the ensemble models still guarantees the accuracies to some level for potential clinical decision-making processes in TB management. Ultimately, this study shows a direction for better TB screening in the future TB response strategy.
结核病是全球首要的健康问题，对全球死亡率有重大影响。有效控制结核病需要在资源有限的情况下采用成本效益高的筛查方法。为提高全球结核病防治资源分配的精准度，本研究提出了基于胸部X线摄影（CXR）的机器学习筛查算法，并进行了优化、基准测试和调整，以实现最佳的结核病临床应用亚型分类任务。
本研究深入探讨了一个强大的集成深度学习框架的开发和评估，该框架由43个不同模型组成，用于识别活动性结核病病例及其临床亚型分类。所提出的框架本质上是一个集成模型，在最终分类前的融合阶段有多个特征提取器和三种融合策略之一——投票、基于注意力或拼接方法。所包含的去标识数据集包含915例活动性结核病患者的记录以及1276名健康对照的记录，并带有亚型特异性信息。因此，我们框架的实现能够进行亚型识别诊断。亚型标签包括：继发性肺结核/结核性胸膜炎；非空洞/空洞；仅继发性肺结核/继发性肺结核和结核性胸膜炎；仅结核性胸膜炎/继发性肺结核和结核性胸膜炎。
基于数据集以及模型选择和调整，集成模型显示出其在亚型识别方面具有自我校正能力，并能做出可靠的临床预测。具有拼接/注意力融合策略的最佳双卷积神经网络提取器模型可能是实际应用中亚型任务的成功模型。通过可视化技术，对集成模型在不同融合策略下的性能进行了深入分析验证。
研究结果强调了这种集成方法在增强结核病亚型诊断方面的潜力。即使数据集有限，集成模型内部的自我校正仍能在一定程度上保证准确性，为结核病管理中的潜在临床决策过程提供支持。最终，本研究为未来结核病应对策略中更好的结核病筛查指明了方向。
