At every level, COVID-19 has had a profound impact on government, education, our ability to earn a living, and, of course, upon human health and wellness. Further, between bombast and misinformation, it’s easy to overlook the significant innovation of data and computer scientists in combatting the novel coronavirus.
The truth is that innovation abounds, particularly in the areas of COVID-19 testing and treatment. Here, I will share some of those innovations, and, perhaps, a bit of hope for a safer, healthier future.
One example of innovation in testing technology comes to us from Oxford’s Department of Physics, where they have developed an accurate COVID-19 test capable of detecting SARS-CoV2 directly in patient samples using an approach based on machine learning. Far from being “just another” testing method, it offers numerous advantages, including:
According to Tech Crunch contributor Darrell Etherington, “The technology that makes this possible works by labeling any virus particles found in a sample collected by a patient using short, fluorescent DNA strands that act as markers. A microscope images the sample and the labeled viruses present, and then machine learning software takes over using algorithmic analysis developed by the [Oxford] team to automatically identify the virus, using differences that each one produces in terms of its fluorescent light emitted owing to their different physical surface makeup, size, and individual chemical composition.”
In an article published on medRxiv, the development team explained, “[Our approach] uses a convolutional neural network to distinguish between microscopy images of single intact particles of different viruses. Our assay achieves labeling, imaging, and virus identification in less than five minutes and does not require any lysis, purification, or amplification steps. The trained neural network was able to differentiate SARS-CoV-2 from negative clinical samples, as well as from other common respiratory pathogens such as influenza and seasonal human coronaviruses, with high accuracy. Single-particle imaging combined with deep learning offers a promising alternative to traditional viral diagnostic methods, and has the potential for significant impact.”
Currently, the team is attempting to form a commercial enterprise responsible for commercializing the technology, with product development expected in early 2021. However, the Oxford team is just one of many working to provide humanity with powerful new testing and identification tools.
