为偏远医院设计实时胸片辅助诊断系统，低延迟可靠通信。
A Real-Time DDS-Based Chest X-Ray Decision Support System for Resource-Constrained Clinics
- 用微调ResNet50结合Fast DDS实现实时推理与通信
- 准确率88.61%，平均延迟65毫秒，吞吐量3.2 KB/s
- 适合网络差的偏远医疗场景，提升远程诊疗效率
基于物联网（IoT）的医疗系统在人道主义及资源匮乏环境中具有巨大潜力，可为偏远地区未被覆盖人群提供关键医疗服务。然而，这些地区的有限网络基础设施使传统IoT系统难以实现可靠通信。本文提出一种专为偏远医院设计的实时胸片决策支持系统。该系统将微调后的ResNet50深度学习模型用于疾病分类，并集成Fast DDS实时中间件，确保医护人员与推理系统之间的低延迟、高可靠性通信。实验结果表明，该模型达到88.61%的准确率、88.76%的精确率和88.49%的召回率；系统平均吞吐量为3.2 KB/s，平均延迟为65毫秒，证明其在带宽受限环境下的适用性。这些结果凸显了基于DDS的中间件在支持远程医疗实时决策中的有效性。
原文摘要 · Abstract (English)
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.
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