PACMAN: a framework for pulse oximeter digit detection and reading in a low-resource setting

TL;DR

This paper introduces PACMAN, a low-resource deep learning framework that accurately detects and reads pulse oximeter digits from images, improving patient data entry accuracy during the COVID-19 pandemic.

Contribution

The study presents a novel, low-resource computer vision framework using YOLOv5 and clustering algorithms for digit detection and reading in pulse oximeter images, outperforming existing OCR methods.

Findings

YOLOv5 achieved 81-89.5% accuracy in digit recognition.

Auto-orientation and clustering improved detection performance.

Framework integrated into hospital patient monitoring systems.

Abstract

In light of the COVID-19 pandemic, patients were required to manually input their daily oxygen saturation (SpO2) and pulse rate (PR) values into a health monitoring system-unfortunately, such a process trend to be an error in typing. Several studies attempted to detect the physiological value from the captured image using optical character recognition (OCR). However, the technology has limited availability with high cost. Thus, this study aimed to propose a novel framework called PACMAN (Pandemic Accelerated Human-Machine Collaboration) with a low-resource deep learning-based computer vision. We compared state-of-the-art object detection algorithms (scaled YOLOv4, YOLOv5, and YOLOR), including the commercial OCR tools for digit recognition on the captured images from pulse oximeter display. All images were derived from crowdsourced data collection with varying quality and alignment. YOLOv5 was the best-performing model against the given model comparison across all datasets, notably the correctly orientated image dataset. We further improved the model performance with the digits auto-orientation algorithm and applied a clustering algorithm to extract SpO2 and PR values. The accuracy performance of YOLOv5 with the implementations was approximately 81.0-89.5%, which was enhanced compared to without any additional implementation. Accordingly, this study highlighted the completion of PACMAN framework to detect and read digits in real-world datasets. The proposed framework has been currently integrated into the patient monitoring system utilized by hospitals nationwide.