COVID-Net MLSys: Designing COVID-Net for the Clinical Workflow
This work addresses the challenge of deploying AI tools in real-world clinical settings for COVID-19 patient management, though it is incremental as it builds on existing methods with a focus on workflow integration.
The study tackled the problem of integrating machine learning models into clinical workflows for COVID-19 screening by designing the COVID-Net system, which includes datasets and deep neural networks for detection and severity scoring, achieving state-of-the-art performance and integration into a user interface for clinical decision support.
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.