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Tag: Public Health

AI-Powered Detection of COVID-19

Posted on by Encode Justice

The COVID-19 pandemic is the defining public health crisis of the 21st century, and efforts to improve treatment, diagnostic testing, and prediction of clinical severity are paramount. Leading researchers across the globe are employing AI to automate parts of the COVID-19 response.

Image Credit: Healthcare Global

AI To Detect COVID-19 Through Cough Recordings

A team of researchers at MIT developed an algorithm that identifies the coughs of asymptomatic people with COVID-19 using patterns in four vocal biomarkers: vocal cord strength, sentiment, lung and respiratory performance, and muscular degradation. The MIT Open Voice Model uses acoustics to pre-screen for COVID-19 from cough recordings before a viral test. The model was tested on cough recordings from over 5,000 individuals, and it accurately identified 98.5% of coughs from people with confirmed COVID-19 and 100% of coughs from asymptomatic people who tested positive for the virus.

The group is developing a smartphone app that would serve as a “free, non-invasive, real-time, any-time, instantly distributable, large-scale COVID-19 screening tool” and are awaiting FDA approval for launch.

AI To Detect COVID-19 in Chest X-Rays & Predict Severity of Cases

Researchers from New York and China developed an AI-based tool to predict future clinical COVID-19 severity, allowing for early intervention. It could help physicians assess which patients with moderate COVID-19 symptoms can safely go home to recover and reduce already heavy burdens on hospital staff and resources.

Taking into account demographics, laboratory data, and radiological imaging, the study analyzed the differences in patients with mild symptoms, cough, fever, and upset stomachs, who went to develop severe symptoms, such as pneumonia and Acute Respiratory Distress Syndrome (ARDS) or fluid build-up in the lungs, versus those with the same initial symptoms who did not.

The team found that changes in the levels of liver enzyme alanine aminotransferase (ALT), reported myalgia, and hemoglobin levels were most accurate in predicting severe COVID-19, not markers considered hallmarks of the disease like patterns in lung images, fever, strong immune response, age or gender. Altogether, the model predicted the risk of developing ARDS after mild COVID-19 symptoms with 70–80% accuracy. The model is still in its early stages, only trained on a small dataset of patients from two hospitals, but it could be vital in early intervention and allocation of hospital beds as COVID-19 cases continue to rise.

Similarly, radiologists at the University of California at San Diego are using AI to augment lung imaging analysis to find signs of early pneumonia. The machine-learning algorithm overlays color-coded maps showing the probability of pneumonia over a patient’s x-ray. Chest x-rays are a cost-effective and quick diagnostic tool to predict the future severity of a patient’s COVID-19 case and the probability of developing pneumonia.

Image Credit: University of California

AI can provide quick, accurate, and non-invasive diagnostic testing for COVID-19, help healthcare professionals predict the future severity of cases, and determine which patients can go home safely when hospital resources run low.

Machine-Learning Model Can Inform Quarantine Measures to Reduce the Spread of COVID-19

Posted on by Encode Justice
source: Jonas França

A machine-learning model developed by researchers at MIT analyzes and compares how the number of COVID-19 infections across 70 countries in Europe, North America, South America, and Asia differed with how effectively the nations’ governments maintained their quarantine measures. This diagnostic tool is highly accessible and trained on all publicly available COVID-19 data sets, so it could help policymakers inform better quarantine measures across the globe.

The model is based on a traditional SIR model, an epidemiological model used to predict disease spread based on the number of people who are considered “susceptible,” “infectious,” or “recovered” It was enhanced with a neural network then trained on international COVID-19 data to identify patterns in infections and recovery. To determine ‘quarantine strength,’ the algorithm calculated the number of infected individuals who are not transmitting COVID-19 to others by following the quarantine measures in place in their region. As new data is published, the model can show how that area’s quarantine strength changes and evolves over time as safety regulations change.

The MIT team focused on the United States and used their model to calculate how effectively a state has enforced its safety measures and limited the spread of the disease. In spring and early summer, parts of the southern and central United States began reopening businesses and relaxing strict quarantine measures, which led to a sharp increase in COVID-19 cases in those regions. The model calculated that if these states did not re-open so early on or re-opened with strictly enforced safety measures like wearing masks and social distancing, 40% of COVID-19 infections could have been avoided. In Texas and Florida, specifically, maintaining stricter quarantine and stay-at-home measures would have avoided as many as 100,000 infections.

The research paper’s lead author, Raj Dandekar, a graduate student in MIT’s Department of Civil and Environmental Engineering, emphasizes that “If you look at these numbers, simple actions on an individual level can lead to huge reductions in the number of infections and can massively influence the global statistics of this pandemic.” As the number of COVID-19 cases across the United States rise and cities like Los Angeles run out of available ICU beds, this machine-learning model could be vital in informing what level of quarantine measures to put in place. Co-author Christopher Rackauckas, an applied mathematics professor at MIT, says “What I think we have learned quantitatively is, jumping around from hyper-quarantine to no quarantine and back to hyper-quarantine definitely doesn’t work. Instead, good consistent application of policy would have been a much more effective tool.”

This novel machine-learning model can help policymakers determine the best course of action for quarantine measures in different countries, illuminate patterns of COVID-19 spread across different demographics, such as socioeconomic level and race, and save millions of lives.