A Hierarchical Computer Vision Pipeline for Physiological Data Extraction from Bedside Monitors

TL;DR

This paper introduces a computer vision pipeline that automatically captures and digitizes vital signs from bedside monitor screens, enabling integration into electronic health records in low-resource healthcare settings without hardware upgrades.

Contribution

It presents a hierarchical detection and OCR framework that accurately extracts physiological data from monitor screens under variable conditions, addressing interoperability gaps in low-resource environments.

Findings

Achieved 99.5% monitor detection accuracy

Attained 91.5% ROI localisation accuracy

End-to-end physiological data extraction over 98.9%

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.