As the COVID-19 pandemic continues to devastate globally, one promising field of research is machine learning-driven computer vision to streamline various parts of the COVID-19 clinical workflow. These machine learning methods are typically stand-alone models designed without consideration for the integration necessary for real-world application workflows. In this study, we take a machine learning and systems (MLSys) perspective to design a system for COVID-19 patient screening with the clinical workflow in mind. The COVID-Net system is comprised of the continuously evolving COVIDx dataset, COVID-Net deep neural network for COVID-19 patient detection, and COVID-Net S deep neural networks for disease severity scoring for COVID-19 positive patient cases. The deep neural networks within the COVID-Net system possess state-of-the-art performance, and are designed to be integrated within a user interface (UI) for clinical decision support with automatic report generation to assist clinicians in their treatment decisions.
@article{arxiv.2109.06421,
title = {COVID-Net MLSys: Designing COVID-Net for the Clinical Workflow},
author = {Audrey G. Chung and Maya Pavlova and Hayden Gunraj and Naomi Terhljan and Alexander MacLean and Hossein Aboutalebi and Siddharth Surana and Andy Zhao and Saad Abbasi and Alexander Wong},
journal= {arXiv preprint arXiv:2109.06421},
year = {2021}
}