YSM Issue 94.3

FEATURE
Computational Biology
BIOETHICS IN THE
AGE OF COVID-19
LAUNDERING BIAS AND
SAVING LIVES THROUGH AI
BY RISHA CHAKRABORTY
AND JUSTIN YE
Over the past year and a half,
our hospitals, overwhelmed by
COVID-19 patients desperate
for oxygen, have been debilitated by
staff and resource shortages. While
many called for vaccines as a hopeful
cure-all, some recognized a faster
alternative: efficient and deliberate
distribution of hospital resources.
Fourth-year PhD candidate Amogh
Hiremath and Professor of Biomedical
Engineering Anant Madabhushi at
Case Western Reserve University were
among the bioengineers who confronted
this problem. “It’s particularly heartwrenching,
as a father myself, to
see pediatric wards filled up… kids
[who] require critical surgeries just
don’t have a bed,” Madabhushi said.
Recognizing that delayed or inaccurate
risk assessments could prove fatal,
Hiremath and Madabhushi developed
CIAIN (integrated clinical and AI
imaging nomogram), the first deeplearning
algorithm to predict the
severity of COVID-19 patients’
prognoses based on patient CT lung
scans as well as clinical factors.
Artificial intelligence, at its core,
endeavors to mimic processes within a
human brain. Similar to how humans
take lessons from past experiences and
apply them to novel situations, computers
“learn” information from a training set
and apply it to a testing set. In the case
of a prediction algorithm like CIAIN,
computers are initially fed information
ART BY NOORA SAID
from existing patient data to correlate
features of CT scans and clinical test
results with patient prognoses. Once
the algorithm is trained, it can then be
applied to novel patient information—
the testing group—and give prognoses
with a high degree of precision. CIAIN
is the “first prediction algorithm
to use a deep learning approach
in combination with clinical
parameters,” Hiremath said.
This makes it more accurate than
algorithms using imaging alone.
Another major advantage of
CIAIN lies in its speed of
deployability: given that
accessing medical datasets
is relatively difficult
compared to obtaining
a set of natural images,
Hiremath and Madabhushi
used roughly one-thousand
patient scans from hospitals
in Cleveland, Ohio and
China to train, fine-tune,
and test their model. And
notably, CIAIN is the
first algorithm designed
for COVID-19.
Given that their
paper only examined
unvaccinated patients,
Madabhushi and Hiremath
now want to investigate
if they can find the risk of
hospitalization for vaccinated
individuals. “As we hear
about new breakthrough infections, the
question is if we need to run the analysis
retrospectively on patients who have been
vaccinated,” Madabhushi said. However,
while it is one thing to create predictive
algorithms retrospectively, it is another
to apply such algorithms to novel patient
data without prior physician evaluation.
A prospective study—a study that follows
patients before their ultimate outcomes
are known—would employ a dualpronged
approach. First, the researchers
would evaluate the algorithm in the pilot
phase of a prospective non-interventional
trial, where radiologists would upload
a CT scan and the algorithm would
generate a risk score for a patient. In a
few months, if the tool performed well,
the study could then transition into a
prospective interventional form, and the
researchers could propose the algorithm
to the FDA for clinical approval.
Despite anticipating the usage of CIAIN
in the emergency room, Madabhushi was
careful to emphasize the limited role
28 Yale Scientific Magazine October 2021 www.yalescientific.org