Optimizing high-volume ECG Scan Digitization: A Cloud-Driven Approach Using Terraform and Ansible

Abstract

Abstract

The digitization of medical data, especially Electrocardiography (ECG) scans, offers significant potential to enhance diagnostic accuracy and patient outcomes through advanced cloud automation and computational methods. This project aims to develop and evaluate a cloud-based automation system for ECG digitization using Terraform and Ansible orchestration, designed to streamline the analysis and interpretation of ECG signals. The primary objective is to assess the feasibility and transformative potential of automating ECG digitization through cloud infrastructure and computational techniques.

The project begins with a comprehensive literature review to understand current advancements in automated ECG analysis and to identify prevailing challenges. It involves the use of algorithms for ECG image preprocessing, data transformation, and feature extraction. This digitization tool is deployed on the OsloMet University OpenStack Cloud, leveraging Terraform and Ansible for infrastructure provisioning and configuration, to automate the ECG Paper Scan digitization process of a dataset comprising approximately 7,000,000 ECG scans from Akershus University Hospital in Oslo.

The evaluation of the system is thoroughly discussed, focusing on its relevance to healthcare applications. Key aspects such as scalability challenges, resource requirements, and computational efficiency are analyzed, including processing rates, vCPU demands, RAM utilization, and cost estimations for scaling the automation process to handle large datasets. The importance of cloud-based solutions and optimized workflows is highlighted to manage high-volume data processing tasks effectively.

Accuracy is assessed against ground truth annotations, providing insights into algorithm performance and identifying areas for improvement. Despite facing algorithmic challenges and dataset quality issues, the project underscores the transformative potential of automated ECG digitization in revolutionizing diagnostics and patient care. It emphasizes the need to address scalability, accuracy, and data privacy concerns to enhance healthcare delivery efficiency.

The project illustrates the critical role of collaborative efforts among researchers, clinicians, and technologists in advancing cloud-based ECG digitization solutions, paving the way for personalized, data-driven diagnostics and treatments.