COVID-Net MLSys: Designing COVID-Net for the Clinical Workflow

TL;DR

This paper presents COVID-Net MLSys, a machine learning system designed to integrate COVID-19 detection and severity scoring into clinical workflows, featuring state-of-the-art neural networks and user interface support.

Contribution

It introduces an integrated COVID-19 screening system that considers real-world clinical workflow requirements, combining datasets, neural networks, and user interface design.

Findings

State-of-the-art neural network performance for COVID-19 detection and severity scoring.

An integrated system with automatic report generation for clinical decision support.

A comprehensive COVID-19 dataset (COVIDx) for ongoing model development.

Abstract

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.