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Updates on How AI Being Employed to Speed COVID-19 Treatments and Management



Medical researchers are employing AI to search through databases of known drugs to see if any can be associated with a treatment for COVID-19. (GETTY IMAGES)

By AI Trends Staff

Medical researchers are employing AI to search through databases of known drugs to see if any can be associated with a treatment for the new COVID-19 coronavirus.

An early success story comes from BenevolentAI of London, which using tools developed to search through medical literature,  identified rheumatoid arthritis drug baricitinib as a possible treatment for COVID-19.

In a pilot study at the end of March, 12 adults with moderate COVID-19 admitted to the hospital in either Alessandria or Prato, Italy, received a daily dose of baricitinib, along with an anti-HIV drug combination of lopinavir and ritonavir, for two weeks. Another study group of 12 received just lopinavir and ritonavir.

After their two-week treatment, the patients who received baricitinib had mostly recovered, according to a recent account in The Scientist.  Their coughs and fevers were gone; they were no longer short of breath. Seven of the 12 had been discharged from the hospital. In contrast, the group who didn’t get baricitinib still had elevated temperatures, nine were coughing, and eight remained short of breath. Just one patient from the lopinavir-ritonavir–only group had been discharged.

Researchers at Benevolent AI, along with collaborator Justin Stebbing, an oncologist at Imperial College London, published a letter to The Lancet on February 4, describing how they used AI to identify baricitinib’s potential to treat COVID-19.

AI “makes higher-order correlations that a human wouldn’t be capable of making, even with all the time in the world. It links datasets that a human wouldn’t be able to link,” stated Stebbing.

Benevolent researchers used the company’s knowledge graph—a digital storehouse of biomedical information and connections inferred and enhanced by machine learning—to identify two human protein targets to focus on: AP2-associated protein kinase 1 (AAK1) and cyclin g-associated kinase (GAK).

Justin Stebbing, an oncologist at Imperial College London

The team used another algorithm to find existing drugs that could hit the protein targets, completing the work in a few days. Drugs not approved by regulators were eliminated, cutting the list to about 30. Eli Lilly, the company that makes baricitinib, has entered into an agreement with the National Institute of Allergy and Infectious Diseases to study the drug’s effectiveness in COVID-19 patients in the US.

“Even if the trial doesn’t work, we’re going to find out a huge amount of who it might work in and when it might work,” stated Stebbing. “It’s all about personalized medicine, which means treating the right person at the right time with the right disease with the right drugs. Hopefully, this will be a powerful part of the jigsaw.”

MIT-IBM Watson AI Lab Funding 10 Projects

Elsewhere, the MIT-IBM Watson AI Lab is funding 10 research projects incorporating AI to address the health and economic consequences of the pandemic.

One project seeks to establish early detection of sepsis in COVID-19 patients. About 10 percent of COVID-19 patients get sick with sepsis within a week of showing symptoms, but only about half survive, according to the account from MIT News. Identifying patients at risk for sepsis can lead to earlier, more aggressive treatment and a better chance of survival.

In a project led by MIT Professor Daniela Rus, researchers will develop a machine learning system to analyze images of patients’ white blood cells for signs of an activated immune response against sepsis.

Another project led by MIT professors Daron Acemoglu, Simon Johnson, and Asu Ozdaglar will model the effects of targeted lockdowns on the economy and public health. The team analyzed the relative risk of infection, hospitalization, and death for different age groups. When they compared uniform lockdown policies against those targeted to protect seniors, they found that a targeted approach could save more lives. Building on this work, researchers will consider how antigen tests and contact tracing apps can further reduce public health risks.

Other studies are looking at: which material makes the best face masks; a privacy-first approach to contact tracing; overcoming hurdles to global access to a COVID-19 vaccine, and leveraging electronic medical records to find a treatment for COVID-19.

A COVID Symptom Study app created by researchers at King’s College of London and Mass General Hospital of Boston, aims to predict who is at risk of having the COVID-19 virus, without a test. The app has been downloaded by over three million people worldwide. A prediction system was developed by examining data from 25 million people in the UK and US between the dates of March 24 and April 21, who actively used the app to update their health status.

When the AI-based model was applied to over 800,000 app users who displayed exact symptoms, it revealed that some 17 percent were likely to have coronavirus, information that could be of high value in heavily-populated areas especially.

Vanderbilt University Researcher Also Working with King’s College

A tool in development at Vanderbilt University to study human immune responses to rhinovirus, a cause of the common cold, is being applied to Covid-19-related research in partnership with King’s College of London and Guy’s and St Thomas’ NHS Foundation Trust. The research is being led by Jonathan Irish, associate professor of cell and developmental biology and scientific director of the Cancer & Immunology Core at Vanderbilt.

Jonathan Irish, Scientific Director of the Cancer & Immunology Core at Vanderbilt

In the race to understand the inner-workings of  COVID-19, the tool helps by parsing through vast quantities of data to identify extremely rare immune cells that specifically respond to viruses.

The tool employs aspects of high dimensional (HD) cytometry, a technique that takes measurements of many features of a single blood cell simultaneously. The resulting huge volume of data is challenging to analyze. “We think that HD cytometry can be particularly useful in understanding COVID-19,” stated Irish in a press release from Vanderbilt.

The quickly-developing trial was to  begin treating 19 patients the last week of May. The research hopes to identify immune cells that are reacting to the virus, on the order of a couple of hundred in a sample of 10 million blood cells.

The goal of the joint research is to identify which human immune cells are specific to coronavirus infections, and distinguish these cells from each person’s immune fingerprint.  “Understanding and identifying the types of immune cells that help to fight off the virus could help us optimize vaccine and treatment strategies,” Irish stated.

Researching the Best Strategies for Exiting Social Distancing

How best to exit the isolation strategies for dealing with COVID-19 is the subject of experimentation at the University of Luxumbourg’s SnT, Interdisciplinary Centre for Security, Reliability and Trust. The idea of the research is to make it possible for governments around the world to analyze how various exit strategies will impact the spread of COVID-19 in a six-month time frame.

Yves Le Traon, vice-director of SnT, brought together two teams to collaborate on this project. To generate its predictions, the tool uses data publicly available from the Google COVID-19 dataset, as well as data from Johns Hopkins University. A user is able to understand how policies related to each activity impact the spread of the disease, by selecting a country and changing the value that represents the intensity of any given isolation measure.

The Adaptive Exit Strategies simulator is available here: https://serval-snt.github.io/covid19/

“The saying ‘knowledge is power’ may be overused, but when it comes to the coronavirus it takes on new meaning as every piece of data has the potential to impact the lives of people around the world,” stated Prof. Le Traon, in a release published on EurekAlert!  “Given the enormous amount of data to analyze, we have developed this tool to support exit strategy planning. As many countries in Europe are beginning to execute on their plans already, we wanted to release our work as soon as possible.” EurekAlert!

Simon Fraser University Working on Bio-Image Detection of Covid-19 from X-rays

Researchers at Simon Fraser University and Providence Health Care (PHC) are collaborating on a new tool incorporating AI to help speed the diagnosis of Covid-10 patients. PHC leveraged the expertise of SFU researchers to validate a deep learning AI tool that enables a clinician to feed a patient’s chest x-ray image into a computer, run a bio-image detection analysis and determine a positive pneumonia case that is consistent with COVID-19.The tool is currently in the validation phase at St. Paul’s Hospital in Vancouver, Canada.

YaĞiz Aksoy, an assistant professor in the School of Computing Science’s GrUVi Lab, and MAGPIE Group researcher Vijay Naidu, a mathematician, helped refine the machine learning system using X-ray images of both COVID-19 and non-COVID-19 patients, to identify the unique characteristics found in the virus.

“Instead of doctors checking each X-ray image individually, this system is trained to use algorithms and data to identify it for them,” stated Aksoy. Naidu also shared his expertise in bio-sequence analysis to create a database of COVID-19 biological signatures, or unique identifiers, to zero in on those found in positive patients.

The beta version of the tool – still in an early testing phase – has uploaded to the United Nations Global Platform and is whitelisted in the AWS Machine Learning Marketplace.

NYU College of Dentistry Develops Mobile App to Detect Covid-19 Severity

Researchers at the NYU College of Dentistry have developed a mobile app to help clinicians determine which patients testing positive for Covid-19 are likely to have severe cases. The app uses AI to assess risk factors and key biomarkers from blood tests to provide a Covid-19 “severity score.” Current tests for Covid-19 detect whether someone does or does not have the virus, but they do not provide clues as to how sick a patient might become. 

“Identifying and monitoring those at risk for severe cases could help hospitals prioritize care and allocate resources like ICU beds and ventilators,” stated John T. McDevitt, PhD, professor of biomaterials at NYU College of Dentistry, who led the research. “Likewise, knowing who is at low risk for complications could help reduce hospital admissions while these patients are safely managed at home.” 

Using data from 160 hospitalized Covid-19 patients in Wuhan, China, the researchers identified four biomarkers measured in blood tests that were significantly elevated in patients who died versus those who recovered: C-reactive protein (CRP), myoglobin (MYO), procalcitonin (PCT), and cardiac troponin I (cTnI). These biomarkers can signal complications that are relevant to Covid-19, including acute inflammation, lower respiratory tract infection, and poor cardiovascular health.

The researchers then built a model using the biomarkers as well as age and sex, two established risk factors. They trained the model using a machine learning algorithm to define the patterns of COVID-19 disease and predict its severity. When a patient’s biomarkers and risk factors are entered into the model, it produces a numerical Covid-19 severity score ranging from 0 (mild or moderate) to 100 (critical).

The model was validated using data from 12 hospitalized COVID-19 patients from Shenzhen, China, which confirmed that the model’s severity scores were significantly higher for the patients that died versus those who were discharged. These findings are published in Lab on a Chip, a journal of the Royal Society of Chemistry.

Read the source articles at The Scientist, at MIT News, in a press release from Vanderbilt University, at the Covid Symptom Study, at EurekAlert!, in a press release from Simon Fraser University  and in a press release from the NYU College of Dentistry.

 



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