Researchers use artificial intelligence to predict which COVID-19
patients will need a ventilator to breathe
Date:
September 2, 2021
Source:
Case Western Reserve University
Summary:
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.
FULL STORY ========================================================================== Researchers at Case Western Reserve University 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% 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, 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." Next, Madabhushi said he hopes 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, he said medical staff at the two hospitals could upload
a digitized image of the chest scan to a cloud-based application, where
the AI at Case Western Reserve would analyze it and predict whether that patient would likely need a ventilator.
Dire need for ventilators 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 United States and in other countries.
"These can be gut-wrenching decisions for hospitals -- deciding who is
going to get the most help against an aggressive disease," Madabhushi
said.
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 in Madabhushi's lab began their efforts to provide such
a tool 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.
========================================================================== Madabhushi said those CT scans revealed -- with the help of deep-learning computers, or Artificial Intelligence (AI) -- distinctive features for
patients who later ended up in the intensive care unit (ICU) and needed
help breathing.
The research behind the tool appeared this month in the IEEE Journal of Biomedical and Health Informatics.
Amogh Hiremath, a graduate student in Madabhushi's lab and lead author
on the paper, said patterns on the CT scans couldn't be seen by the
naked eye, but were revealed only by the computers.
"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." Further research into 'immune architecture' Madabhushi's lab also recently published research comparing autopsy tissues scans taken from patients
who died from the H1N1 virus (Swine Flu) and from COVID-19. While the
results are preliminary, they do appear to reveal information about
what Madabhushi called the "immune architecture" of the human body in
response to the viruses.
"This is important because the computer has given us information that
enriches our understanding of the mechanisms in the body against viruses,"
he said.
"That can play a role in how we develop vaccines, for example." Germa'n Corredor Prada, a research associate in Madabhushi's lab who was the
primary author on the paper, said computer vision and AI techniques
allowed the scientists to study how certain immune cells organize in
the lung tissue of some patients.
"This allowed us to find information that may not be obvious by simple
visual inspection of the samples," Corredor said. "These COVID-19-related patterns seem to be different from those of other diseases such as H1N1, a comparable viral disease." Eventually, when combined with other clinical
work and further tests in larger sets of patients, this discovery could
serve to improve the world's understanding of these diseases and maybe
others, he said.
Madabhushi established the CCIPD at Case Western Reserve in 2012. The
lab now includes more than 60 researchers. Some were involved in this
most recent COVID-19 work, including graduate students Hiremath, Pranjal Vaidya; research associates Corredor and Paula Toro; and research faculty
Cheng Lu and Mehdi Alilou.
========================================================================== Story Source: Materials provided by Case_Western_Reserve_University. Note: Content may be edited for style and length.
========================================================================== Journal Reference:
1. Amogh Hiremath, Kaustav Bera, Lei Yuan, Pranjal Vaidya, Mehdi
Alilou,
Jennifer Furin, Keith Armitage, Robert Gilkeson, Mengyao Ji, Pingfu
Fu, Amit Gupta, Cheng Lu, Anant Madabushi. Integrated Clinical and
CT based Artificial Intelligence nomogram for predicting severity
and need for ventilator support in COVID-19 patients: A multi-site
study. IEEE Journal of Biomedical and Health Informatics, Aug. 13,
2021; DOI: 10.1109/ JBHI.2021.3103389 ==========================================================================
Link to news story:
https://www.sciencedaily.com/releases/2021/09/210902174814.htm
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