The Bridge - Issue 1, 2021 - 30

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Using Artificial Intelligence to
Diagnose COVID-19 Pneumonia

while humans can easily ignore slight imperfections
in an image, those same glitches can trip up even the
best machine.

by David Levin

For patients with COVID-19, terrifying
shortness of breath can set in virtually
overnight. In many cases, it's caused by
an aggressive pneumonia infection in the
lungs, which fills them with thick fluid and
robs the body of life-giving oxygen.
Detecting these severe cases early on is essential for
treating them successfully. At the moment, however,
the only way to tell whether a patient's pneumonia
is caused by the coronavirus is by examining X-ray
and CT scans of the chest-and as cases rack up
worldwide, radiologists are being deluged with
images, creating a backlog that may delay critical
decisions about care.

Using X-rays and CT scans from an international
COVID-19 database, her lab is training AI software
to comb through thousands of images, matching
those that share similar traits. By comparing X-rays of
pneumonia caused by bacterial infections, chronic
smoking, and COVID-19, she says, the AI can
gradually learn to identify features unique to each
one, be it a particular shape, area of contrast, or other
trait. Once the software finds potential matches, it
uses statistical analysis to sort COVID cases from nonCOVID ones.
Panetta's COVID-19 work builds on research that
her lab has already been doing to detect cancerous
tumors. In breast cancer, she notes, her AI software
looks at the nuclei of individual cells in a biopsy
sample, and searches for distinct
patterns that match known cases.
Cancer-free samples tend to have
orderly nuclei contained in an oval
structure, but if the cancer progresses,
those patterns tend to break down.
Using AI and machine learning, it's
possible to train the AI to spot new
cancer cases autonomously based on
those traits.

Lung X-ray images, from left, of COVID-19, normal, and viral pneumonia patients.
Images courtesy of Dean of Graduate Education Karen Panetta, Tufts University
School of Engineering.

One solution, said Dr. Karen Panetta, may involve
taking some of that workload away from humans.
Panetta, the Dean of Graduate Education and a
professor of electrical and computer engineering at
Tufts University School of Engineering, is working to
create artificial intelligence (AI) that can spot cases of
COVID-19 pneumonia and flag them for review.
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" We had already developed all these
tools for image processing, machine
learning, and AI methods for cancer,
so COVID-19 was just a more timely
application of the same technology, "
she said. " We're just tuning the
software for a different use case. "

The results are already promising. So far, her lab's
software has been successful at identifying COVID-19
pneumonia in more than 99 percent of the images
it processes.
Getting to that point hasn't been so simple, however.
The machine learning tools she uses to train the
software are only as good as the data they're fed-and

" X-ray and CT scans aren't always in pristine condition.
They require a lot of enhancement and pre-processing
to clean up those imperfections so they're on equal
footing, " she says. The AI also has to be smart enough
not to misdiagnose an image because it sees anomaly.
" Everyone thinks AI is this magical black box, but it's
not Zoltar, " Panetta said, referring to the all-knowing
fortune-telling machine from the Tom Hanks movie,
Big. " You have to constantly tweak it to improve it. "
Another complication, she added, is that while AI can
identify images that look like other cases of COVID
pneumonia, it can't tell exactly why those images meet
the criteria from a medical point of view. To fill in those
gaps, Panetta is looking to team with experienced
radiologists at Tufts, and wants to add medical
annotation and context to each image.
Even if that improved AI software isn't available to
clinicians during the current pandemic - which it
very well may not be, since FDA approval can take

years-Panetta hopes it could still be used in the future
to educate medical personnel. If another outbreak
happens down the road, she reasons, hospitals will
need all the training they can get.
" Right now, even doctors on the front lines have
probably only seen a few hundred cases of COVID-19
pneumonia, but there are hundreds of thousands
of cases happening worldwide, " she said. " If we can
aggregate all that data into one place with images,
symptoms, and patient info, it may be possible to use
AI to study the disease more effectively. "
That could help identify the patterns the cases all
share. " For doctors who have never seen a patient with
COVID-19, " said Panetta, " it could generate a portfolio
that tells them what to look out for. "
A team of dedicated graduate students works with
Panetta in the Vision and Sensing Lab. The School of
Engineering is accepting applications now for talented
MS and PhD students to join their ranks conducting
life-changing research and scholarship at Tufts
University. Learn more at go.tufts.edu/engmasters.

FEATURED RESEARCHER
Dr. Karen Panetta
Epsilon Delta
Dean of Graduate Education for the
School of Engineering, Tufts University
Professor, Electrical &
Computer Engineering
IEEE Fellow
IEEE-HKN (Eta Kappa Nu)
Honor Society President 2019

Considering Graduate School?
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You can watch this session and the dozens of others presented during the 2020 HKN Experience by
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31


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The Bridge - Issue 1, 2021

Table of Contents for the Digital Edition of The Bridge - Issue 1, 2021

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The Bridge - Issue 1, 2021 - Cover1
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