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.
@article{arxiv.2511.23355,
title = {A Hierarchical Computer Vision Pipeline for Physiological Data Extraction from Bedside Monitors},
author = {Vinh Chau and Khoa Le Dinh Van and Hon Huynh Ngoc and Binh Nguyen Thien and Hao Nguyen Thien and Vy Nguyen Quang and Phuc Vo Hong and Yen Lam Minh and Kieu Pham Tieu and Trinh Nguyen Thi Diem and Louise Thwaites and Hai Ho Bich},
journal= {arXiv preprint arXiv:2511.23355},
year = {2025}
}