Internet of Things (IoT)-based healthcare systems offer significant potential for improving healthcare delivery in humanitarian and resource-constrained environments, providing essential services to underserved populations in remote areas. However, limited network infrastructure in such regions makes reliable communication challenging for traditional IoT systems. This paper presents a real-time chest X-ray decision support system designed for hospitals in remote locations. The proposed system integrates a fine-tuned ResNet50 deep learning model for disease classification with Fast DDS real-time middleware to ensure reliable and low-latency communication between healthcare practitioners and the inference system. Experimental results show that the model achieves an accuracy of 88.61%, precision of 88.76%, and recall of 88.49%. The system attains an average throughput of 3.2 KB/s and an average latency of 65 ms, demonstrating its suitability for deployment in bandwidth-constrained environments. These results highlight the effectiveness of DDS-based middleware in enabling real-time medical decision support for remote healthcare applications.

翻译：基于物联网的医疗系统为人道主义及资源受限环境中的医疗服务提供了重要改进潜力，能够为偏远地区服务不足的人群提供基础医疗服务。然而，此类地区有限的网络基础设施使得传统物联网系统难以实现可靠通信。本文提出了一种专为偏远地区医院设计的实时胸部X光决策支持系统。该系统将用于疾病分类的微调ResNet50深度学习模型与Fast DDS实时中间件相结合，以确保医疗从业者与推理系统之间实现可靠、低延迟的通信。实验结果表明，该模型达到了88.61%的准确率、88.76%的精确率和88.49%的召回率。系统实现了平均3.2 KB/s的吞吐量和65 ms的平均延迟，证明了其在带宽受限环境中部署的适用性。这些结果凸显了基于DDS的中间件在实现远程医疗实时决策支持方面的有效性。