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

• URL: http://arxiv.org/abs/2109.06421v1
• Date: Tue, 14 Sep 2021 04:13:24 GMT
• Title: COVID-Net MLSys: Designing COVID-Net for the Clinical Workflow
• Authors: Audrey G. Chung, Maya Pavlova, Hayden Gunraj, Naomi Terhljan, Alexander MacLean, Hossein Aboutalebi, Siddharth Surana, Andy Zhao, Saad Abbasi, and Alexander Wong
• Abstract summary: This study takes a machine learning and systems (MLSys) perspective to design a system for COVID-19 patient screening. 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. 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.
• Score: 101.45411528425939
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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