Newly developed AI (ML) capable of identifying prostate cancer with ‘near-perfect accuracy’
Newly developed AI (ML) capable
of identifying prostate cancer with ‘near-perfect accuracy’
by John Anderer July 28,
2020
PITTSBURGH — Human error
can be charming in an endearing kind of way, but no one appreciates mistakes
when it comes to a topic as serious as cancer. On that note, researchers from
the University of Pittsburgh developed a new artificial
intelligence program with the most accurate record to date when it
comes to recognizing prostate cancer.
“Humans are good at recognizing
anomalies, but they have their own biases or past experience,” says senior
author Dr. Rajiv Dhir, chief pathologist and vice chair of pathology at UPMC
Shadyside and professor of biomedical informatics at UPitt, in a release. “Machines are detached from the whole story.
There’s definitely an element of standardizing care.”
What separates this AI from
the rest of the robotic pack? Dr. Dhir and his team “fed” their program
images from over a million parts of tissue slides extracted from prostate
cancer patient biopsies. Then, the AI program was tested on 1,600 different
slide images collected from 100 suspected prostate cancer patients.
The AI performed incredibly well on that test. Results show 98%
sensitivity and 97% specificity at finding and identifying prostate cancer.
Those stats are much higher than scores recorded by previous cancer-detecting
algorithms.
Moreover, this is the first
algorithm that does far more than just detect cancer. This program also scores
well in categories including tumor grading and sizing, and assessment of
surrounding nerve invasion by cancer cells.
The AI even detects cancer in
six slides that had slipped by a human pathologist.
While all of this is very
promising, the study’s authors caution that the AI program isn’t quite ready to
fully replace human doctors just yet. For example, regarding
those six slides that went unnoticed by a human doctor, that pathologist could
have just seen enough evidence to warrant a cancer diagnosis before coming
upon those particular slides.
Still, Dr. Dhir says the
algorithm, at the very least, can serve as a great failsafe.
“Algorithms like this are especially useful in lesions
that are atypical,” Dr. Dhir comments. “A nonspecialized person may not be able
to make the correct assessment. That’s a major advantage of this kind of
system.”
Of course, this project only
focuses on prostate cancer. While an entirely new algorithm would have to be
trained for each type of cancer, the research team is optimistic their results
can be recreated with other cancer variations.
The study is published in The Lancet Digital Health.
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