A New Technique fighting COVID-19 with AI

Artificial intelligence may have been hyped - but when it comes to medication, it already has a verified track record. The loss of the pandemic in expressions of lives and economic deprivation will be disastrous, great uncertainty surrounded estimates of just how terrible, and of how successful both non-pharmaceutical and pharmaceutical responses can be. Improving AI, one of the most encouraging data analytic tools to have been developed over the past decade or so, to help reduce these uncertainties, is worthwhile pursuance. Both COVID-19 and SARS expanded across continents, contaminate animals and humans, and use comparable mechanics to enter and infect the cell. On the frontline, the tactical answer to COVID-19 is similar to that of SARS but one major difference exists: in the 17 years since SARS, a powerful new tool has emerged that could potentially be instrumental in keeping this virus within reasonable limits—namely, artificial intelligence (AI). Few would argue that AI is causing a paradigm shift in health care and there might be value in the application of AI to the current COVID-19 outbreak, for example, in predicting the location of the next outbreak. People think the apocalypse is occurring but a report reflecting that this pandemic is going to decrease considerably as we move into spring might provide some reassurance, and AI can play a role here.

So can machine learning rise to this trial of discovering a remedy for this terrible disease?

The practice is divided into three parts:

1. trawl through all the modern treatment associated with the disease

2. analyze the DNA and structure of the virus

3. consider the suitability of various drugs relating to the medication

Medication identification has traditionally been slow.

For those operating in the field of AI drug identification, there are two options when it comes to corona-virus :

1. find a solely new drug but wait a couple of years for it to be approved as safe for use

2. repurpose existing drugs

AI remains one of our most potent paths to achieve a tangible clarification but there is a significant demand for high quality, large, and clean data sets. Now more than ever there is a need to consolidate these diverse drug discovery data sources to allow AI researchers to implement their paperback machine-learning methods to generate new therapies for COVID-19 as soon as possible.

AI research has yielded surprising results, including:

-> the approach the virus may attack brain tissues, which may explain why some people lose their sense of taste or smell

-> the prediction it may also attack the reproductive system of both men and women

A disease phenotype is unusually due to a failure of one gene or proteid on its own - strength is not that simple - but the result of a cascading outcome in an arrangement of synergies between several proteins.

The utility of AI comes into action by diminishing the burden on clinicians in a synopsis such as the current COVID-19 outbreak. This is where Infervision's AI application could help. From a lung CT scan, the AI is intended to instantly detect lesions of possible coronavirus pneumonitis, to measure its volume, shape, and density, and to compare changes of multiple lung lesions from the image, which all provide a quantitative report to assist doctors making a fast judgment. While a standard read of a CT scan can take up to 15 minutes, AI can finish reading the image in 10 seconds. The application of this technology in COVID-19 has not yet been published in a peer-reviewed journal.