Health

AI to predict Covid patients in dire need of ventilators

It could also be important for hospitals as they determine how many ventilators they’ll need.

New York: US researchers have developed an online tool using Artificial Intelligence (AI) to help medical staff quickly determine which Covid-19 patients will need help breathing with a ventilator.

The team at Case Western Reserve University developed the tool, through analysis of CT scans from nearly 900 Covid-19 patients diagnosed in 2020, and is able to predict ventilator need with 84 per cent accuracy.

“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, Professor of Biomedical Engineering at Case Western Reserve.

“It could also be important for hospitals as they determine how many ventilators they’ll need,” he added.

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.

“These can be gut-wrenching decisions for hospitals deciding who is going to get the most help against an aggressive disease,” Madabhushi said.

The findings are detailed in the IEEE Journal of Biomedical and Health Informatics.

The team began their efforts to develop the 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.

Madabhushi said 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 CT scans could not be seen by the naked eye, but were revealed only by the computers, said Amogh Hiremath, a graduate student in Madabhushi’s lab.

“This tool would allow for medical workers to administer medications or supportive interventions sooner to slow down disease progression,” Hiremath said.

“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,” he noted.

  • Agencies