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.
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.
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.