AI Tool Analyzes CT Scans to Accurately Predict Which COVID-19 Patients Will Need Ventilator to Breathe

The tool, developed by researchers at Case Western Reserve University (Cleveland, OH, USA) through analysis of computed tomography (CT) scans from nearly 900 COVID-19 patients diagnosed in 2020, was able to predict ventilator need with 84% accuracy. The researchers now hope to use those results to try out the computational tool in real time 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 tool 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. While 2021’s climbing vaccination rates dramatically reduced COVID-19 hospitalization rates - and, in turn, the need for ventilators - the recent emergence of the Delta variant has again led to shortages in some areas of the US and in other countries.

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. Researchers at Case Western Reserve University began their efforts to provide such a tool by evaluating the initial scans taken in 2020 from nearly 900 patients from the US 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 deep-learning computers, or AI - distinctive features for patients who later ended up in the intensive care unit (ICU) and needed help breathing. The patterns on the CT scans couldn’t be seen by the naked eye, but were revealed only by the computers.

“That could be important for physicians as they plan how to care for a patient—and, of course, for the patient and their family to know,” said Anant Madabhushi, the Donnell Institute Professor of Biomedical Engineering at Case Western Reserve and head of the Center for Computational Imaging and Personalized Diagnostics (CCIPD). “It could also be important for hospitals as they determine how many ventilators they’ll need.”

“This tool would allow for medical workers to administer medications or supportive interventions sooner to slow down disease progression,” said Amogh Hiremath, a graduate student in Madabhushi’s lab and lead author on the paper. “And it would allow for early identification of those at increased risk of developing severe acute respiratory distress syndrome—or death. These are the patients who are ideal ventilator candidates.”

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