By Brianna Abbott
Google's health research unit said it has developed an
artificial-intelligence system that can match or outperform
radiologists at detecting breast cancer, according to new research.
But doctors still beat the machines in some cases.
The model, developed by an international team of researchers,
caught cancers that were originally missed and reduced
false-positive cancer flags for patients who didn't actually have
cancer, according to a paper published on Wednesday in the journal
Nature. Data from thousands of mammograms from women in the U.K.
and the U.S. was used to train the AI system.
But the algorithm isn't yet ready for clinical use, the
researchers said.
The model is the latest step in Google's push into health care.
The Alphabet Inc. company has developed similar systems to detect
lung cancer, eye disease and kidney injury.
Google and Alphabet have come under scrutiny for privacy
concerns related to the use of patient data. A deal with Ascension,
the second-largest health system in the U.S., allows Google to use
AI to mine personal, identifiable health information from millions
of patients to improve processes and care.
The health data used in the breast-cancer project doesn't
include identifiable information, Google Health officials said, and
the data was stripped of personal indicators before given to
Google.
Radiologists and AI specialists said the model is promising, and
officials at Google Health said the system could eventually support
radiologists in improving breast-cancer detection and outcomes, as
well as efficiency in mammogram reading.
"There's enormous opportunity, not just in breast cancer but
more widely, to use this type of technology to make screening more
equitable and more accurate," said Dominic King, the U.K. lead at
Google Health. "It feels like this is another step towards this
technology actually making a difference in the real world."
Breast cancer is the second-leading cause of cancer death in
women after lung cancer, and roughly one in eight women in the U.S.
are likely to develop breast cancer throughout their lifetime,
according to the American Cancer Society. Early breast-cancer
detection and treatment can save lives, experts said, and most
health systems have screening protocols.
But many cases of breast cancer are missed. And sometimes
mammograms are flagged for women who don't have breast cancer or
whose cancer is generally harmless, leading to extra testing or
unnecessary treatment.
"It's this balance of finding the important cancers and not
causing undue distress over false positives that aren't going to
hurt a woman," said Emily Conant, a radiologist and division chief
of breast imaging at Penn Medicine.
When developing the AI system from the U.K. dataset, researchers
fed the algorithm mammograms from the U.K. National Health
Service's breast-screening program. The U.S. dataset comprised
mammograms taken from Northwestern Memorial Hospital in Chicago.
Whether a woman had breast cancer was previously determined, and
researchers told the algorithm which cases had confirmed breast
cancer.
The AI system was then tested on different mammograms of more
than 25,000 women in the U.K. and 3,000 women in the U.S. from
those datasets. The AI system reduced missed cases by 9.4% in the
U.S. and 2.7% in the U.K. compared with the original radiologist
diagnoses. It also reduced incorrect positive readings by 5.7% and
1.2%, respectively.
In the U.K., where two radiologists typically read a mammogram,
the study found that the model didn't perform worse than the second
reader and could potentially reduce their workload by 88%.
The researchers then had six U.S. radiologists who didn't make
the original diagnoses look at 500 U.S. mammograms and compared
their responses with the AI system's. The radiologists also
received the patients' history and past mammograms when available,
while the AI system didn't. The AI system outperformed the average
radiologist in determining whether the women would develop breast
cancer.
While the AI system caught cancers that the radiologists missed,
the radiologists in both the U.K. and the U.S. caught cancers that
the AI system missed. Sometimes, all six U.S. readers caught a
cancer that slipped past the AI, and vice versa, said Mozziyar
Etemadi, a research assistant professor in anesthesiology and
biomedical engineering at Northwestern University and a co-author
of the paper.
The cancers that the AI system caught were generally more
invasive than those caught by the radiologists; the researchers
didn't have an explanation for the discrepancies.
"I found it sobering," said Ziad Obermeyer, acting associate
professor of health policy and management at the University of
California, Berkeley who studies machine learning and health and
wasn't involved in the research. "I think this is a testament to
how difficult the task is and how weirdly good humans are at it,
even with some of the best data in the world."
Researchers now want to see how the model would behave in the
clinic.
"The real world is more complicated and potentially more diverse
than the type of controlled research environment reported in this
study," Etta Pisano, chief research officer at the American College
of Radiology, wrote in an editorial in the journal Nature about the
paper.
Google Health said it is talking with health systems and
research groups about how best to incorporate the AI system into
clinical workflow.
Write to Brianna Abbott at brianna.abbott@wsj.com
(END) Dow Jones Newswires
January 01, 2020 13:14 ET (18:14 GMT)
Copyright (c) 2020 Dow Jones & Company, Inc.
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