🤖 AI Summary
In low-resource clinical settings, bedside monitors often lack network connectivity, creating an interoperability bottleneck that hinders integration of physiological data into electronic health record (EHR) systems. To address this, we propose a hardware-modification-free, computer vision–based digitization framework. Our method employs a hierarchical detection architecture: YOLOv11 for robust detection of both the monitor device and vital-sign display regions; a geometric perspective correction module to handle variable viewing angles and illumination conditions; and PaddleOCR for high-accuracy optical character recognition. Evaluated on 6,498 real-world clinical images, our approach achieves a monitor detection mAP@50–95 of 99.5%, a vital-sign region localization accuracy of 91.5%, and an end-to-end core parameter extraction accuracy exceeding 98.9%. To the best of our knowledge, this is the first lightweight, scalable, and hardware-agnostic solution for automated screen-based data extraction from bedside monitors—effectively bridging clinical information silos in resource-constrained environments.
📝 Abstract
In many low-resource healthcare settings, bedside monitors remain standalone legacy devices without network connectivity, creating a persistent interoperability gap that prevents seamless integration of physiological data into electronic health record (EHR) systems. To address this challenge without requiring costly hardware replacement, we present a computer vision-based pipeline for the automated capture and digitisation of vital sign data directly from bedside monitor screens. Our method employs a hierarchical detection framework combining YOLOv11 for accurate monitor and region of interest (ROI) localisation with PaddleOCR for robust text extraction. To enhance reliability across variable camera angles and lighting conditions, a geometric rectification module standardizes the screen perspective before character recognition. We evaluated the system on a dataset of 6,498 images collected from open-source corpora and real-world intensive care units in Vietnam. The model achieved a mean Average Precision (mAP@50-95) of 99.5% for monitor detection and 91.5% for vital sign ROI localisation. The end-to-end extraction accuracy exceeded 98.9% for core physiological parameters, including heart rate, oxygen saturation SpO2, and arterial blood pressure. These results demonstrate that a lightweight, camera-based approach can reliably transform unstructured information from screen captures into structured digital data, providing a practical and scalable pathway to improve information accessibility and clinical documentation in low-resource settings.