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.
@article{arxiv.2412.07818,
title = {A Real-Time DDS-Based Chest X-Ray Decision Support System for Resource-Constrained Clinics},
author = {Omar H. Khater and Basem Almadani and Farouq Aliyu and Esam Al-Nahari},
journal= {arXiv preprint arXiv:2412.07818},
year = {2026}
}