Figure depicting regions of shape differences between COVID-19 patients with mild and severe disease. The blue regions indicate areas in the lung with no shape differences between the patients and yellow to red indicates areas with significant differences in the lung shape between the two. (Credit: Case Western University)

Researchers have developed an online tool to help medical staff quickly determine which COVID-19 patients will need help breathing with a ventilator. The tool, developed through analysis of CT scans from nearly 900 COVID-19 patients diagnosed in 2020, was able to predict ventilator need with 84 percent accuracy.

They hope to use those results to try out the computational tool in real time at University Hospitals and Louis Stokes Cleveland VA Medical Center with COVID-19 patients. If successful, medical staff could upload a digitized image of the chest scan to a cloud-based application, where the AI would analyze it and predict whether that patient would likely need a ventilator.

Among the more common symptoms of severe COVID-19 cases is the need for patients to be placed on ventilators to ensure they will be able to continue to take in enough oxygen as they breathe. Yet, almost from the start of the pandemic, the number of ventilators needed to support such patients far outpaced available supplies—to the point that hospitals began “splitting” ventilators — a practice in which a ventilator assists more than one patient.

To date, physicians have lacked a consistent and reliable way to identify which newly admitted COVID-19 patients are likely to need ventilators — information that could prove invaluable to hospitals managing limited supplies.

They began by evaluating the initial scans taken in 2020 from nearly 900 patients from the U.S. and from Wuhan, China — among the first known cases of the disease caused by the novel coronavirus. Those CT scans revealed — with the help of AI —distinctive features for patients who later ended up in the intensive care unit (ICU) and needed help breathing.