The Covid-19 virus since its spread to other countries from January and becoming a pandemic has created a sense of panic among everyone. The very reason for this virus to become a pandemic is it being asymptomatic. Which means that you might get Covid-19 and not show any symptoms. In this whole confusion of who has this virus and who doesn't, developers are trying to do their bit by creating new apps and software to help the government in any possible way. The easiest would be to help detect the virus as early as possible.
Canadian startup DarwinAI and researchers from the University of Waterloo are open-sourcing COVID-Net, a convolutional neural network that aims to detect COVID-19 in X-ray imagery. In response to the pandemic, a global community of health care and AI researchers have produced a number of AI systems for identifying COVID-19 in CT scans. Companies like Alibaba and AI startups RadLogics and Lunit claim they've created systems capable of recognizing COVID-19 in X-ray or CT scans with more than 90% accuracy. Early work from Chinese medical researchers and a system published in the journal Radiology last week demonstrated similar results. Like other companies making AI to detect COVID-19 from chest X-rays, DarwinAI said it's creating COVID-Net and the accompanying COVIDx data set to give doctors a way to quickly triage and screen potential cases.
Although most of the Artificial Intelligence systems are still in the stage of infancy, they have proved their usefulness in a variety of trials across the wide spectrum of the ecosystem. And a lot of people are wondering why we are not utilizing the technology to help tackle the current outbreak. The simple answer is that it could lead to ill-informed decisions where public money is spent on unproven AI technology. Having said that AI has shown great promise in the healthcare sector with the prediction, diagnosis, and treatment of various diseases. An interesting perspective with regards to the current pandemic is that an AI company called BlueDot, which uses machine learning to monitor outbreaks of infectious diseases, was the first to alert its clients -- governments, hospitals, and businesses, of an outbreak in China on December 30th.
COVID-Net is a convolutional neural network, a type of AI that is particularly good at recognizing images. Developed by Linda Wang and Alexander Wong at the University of Waterloo and the AI firm DarwinAI in Canada, COVID-Net was trained to identify signs of Covid-19 in chest x-rays using 5,941 images taken from 2,839 patients with various lung conditions, including bacterial infections, non-Covid viral infections, and Covid-19. The data set is being provided alongside the tool so that researchers--or anyone who wants to tinker--can explore and tweak it.
A research team has proposed non-contrast thoracic chest CT scans as an effective tool for detecting, quantifying, and tracking COVID-19. As of March 16, the COVID-19 pandemic had a confirmed infection total of more than 170,000 people around the globe. The speed of transmission of COVID-19 has surprised the world and had a massive impact on people's daily lives and the global economy. To accelerate COVID-19 detection and support efforts to combat the epidemic, researchers from RADLogics, Tel-Aviv University, New York Mount Sinai Hospital and University of Maryland School of Medicine developed an AI-based approach designed to help identify infected patients and quantify disease burden by analyzing thoracic CT (Computer Tomography, aka CAT) exams. Data sources for new epidemic diseases such as COVID-19 remain limited, as does expertise.