Deep
learning is moving out of the realm of the theoretical and is starting to help
physicians treat everyday conditions that affect millions of patients.
April 09,
2019 - No longer the exclusive provenance of researchers and
academics, artificial intelligence is quickly filtering into the everyday
clinical setting.
From supporting
radiologists to enhancing the impact of the patient’s voice in her own care, AI
is already producing meaningful impacts on the quality and accuracy of
care.
Connecting human
intelligence and clinical expertise with the unparalleled data processing power
of deep learning algorithms and advanced neural networks is opening up a new frontier for
precise, personalized diagnostics and treatment – but not just for rare cancers
or one-in-a-million genetic conditions.
At the 2019 World
Medical Innovation Forum hosted by Partners HealthCare, the focus is very much
on the mundane.
In a series of
rapid-fire presentations, Harvard faculty and practicing clinicians from across
the Partners network showcased how AI can help everyday doctors get better at
treating everyday patients with common conditions such as glaucoma, breast
cancer, stroke, and neurodegenerative disease.
Using AI to expand
the health system’s capacity to conduct effective screenings, reduce pain
points in the care process, and augment the clinical decision-making process can
help the industry save billions of dollars each year – and more importantly,
potentially save an untold number of lives.
Healthcare providers
are still too reactive when it comes to diagnosing patients with high-impact conditions,
stressed presenter after presenter.
“Our current tools to
predict future risks are simply not accurate,” said Constance Lehman, MD, PhD,
chief of the breast imaging division at Massachusetts General Hospital (MGH)
and a professor of radiology at Harvard Medical School.
“For breast cancer,
the emphasis is still on late-stage diagnosis, because we are not screening as
comprehensively or as well as we should be.”
Even patients who do
get mammograms at recommended intervals are not receiving uniformly
high-quality care from radiologists, she said.
“Human interpretation
of images is highly subjective. We don’t have enough people to read these
images, and we don’t have enough people who can do it to the highest
standards,” she stated.
Forty percent of
certified breast imaging radiologists perform outside of recommended ranges for
acceptable specificity, her previously published research has shown. Even
agreement around one of the most important fundamental predictors for breast
cancer, the density of the tissue, can vary wildly.
Some radiologists
designate less than 10 percent of breast tissue as dense, said Lehman, while
others will label more than 80 percent of mammograms in the same way.
Deep learning can help providers do
more with less – and do it more accurately.
“Deep learning can
use full-resolution mammogram images to accurately predict the likelihood of a
woman developing breast cancer. Importantly, it is accurate across all
races. Existing models are worse than chance at predicting breast cancer
in African American women. We need something better than that.”
An algorithm trained
on more than 70,000 images consistently outperformed the commonly used risk
model, even when the tool lacked additional data on the patient and only had
access to the image itself.
With more than two
million new breast cancer diagnoses each year, improving the health system’s ability to
identify individuals at risk and provide early treatment to those with cancer
could have a drastic impact on outcomes for hundreds of thousands of women.
Providers treating
another common condition, glaucoma, can also benefit from some extra help to
get ahead of a challenging situation, said Nazlee Zebardast, MD, an instructor
of ophthalmology at Harvard Medical School.
Early detection and
treatment are also key for this degenerative vision disease, often called the
“silent thief of sight,” she said.
Glaucoma can cause
blindness if caught too late, and contributes to nearly $4 billion in direct
medical spending every year.
“Because of its slow
progress, it often doesn’t come to the attention of clinicians until it’s very
advanced,” Zebardast explained. “Nearly half of glaucoma cases are
undiagnosed. Unfortunately, that’s partly because there is no reliable
risk group except for the aged. Ideally, we would screen everyone over
the age of 40, but that will require a lot of experts and will be very expensive.”
Artificial intelligence can start to
fill in those gaps, she asserted. “It’s clear that we need an efficient
and effective screening tool to make testing more accessible.”
Deep learning is
currently being used in some settings to screen for glaucoma, but it isn’t as
reliable or accurate as it could be, she said. These algorithms rely on
subjective labeling of eye images by experts. And just like with breast
cancer clinicians, opinions and skills among ophthalmologists can vary
significantly.
“No algorithm can do
better than what it uses to learn,” said Zebardast. “We need to use objective
data in addition to clinical opinion to come up with a better reference
standard.”
Zebardast is working
to train deep learning algorithms that can use the data in eye images to
identify standardized reference points for early-stage glaucoma that can create
risk scores for patients.
“Ultimately, we aim
to use disease-predicting image features identified in this study to construct
multi-modal models to improve our detection and prediction rates for glaucoma,”
she said.
Stroke is another
area where AI can shine, added Synho Do, PhD, director of the Laboratory of
Medical Imaging and Computation at MGH. With 5.8 million global deaths
annually, 140,000 of which are in the United States, the urgent need for
speedy, accurate diagnostic technology cannot be overstated.
“Stroke care is
extremely time-sensitive,” said Do. “Not everyone lives right next to a
good hospital, and not every hospital has a stroke expert on staff.”
One of the most
important parts of stroke care is distinguishing between the two different
types of stroke, ischemic and hemorrhagic, and locating the bleed in the brain.
Artificial
intelligence can support radiologists as they identify
the extent of the damage. But an algorithmic companion is only effective
if the reasoning behind the suggestion is plain to see, he added.
“It is very important
to use explainable AI in diagnostics,” Do said. “We need to stay away
from black box tools. If a doctor said he could diagnose you, but couldn’t
explain why they offered the diagnosis, how could you trust what they are
saying? It is the same with artificial intelligence.”
Do and his team have
developed an algorithm that offers visual insights into why the deep learning
tool identified a stroke as ischemic or hemorrhagic.
The output summary
includes a color-coded “attention map” overlaid on slices of the radiology
image, he explained. These images show where the AI was “looking” when
making its determination.
“The explicability of
an algorithm is essential not only for understanding the system’s predictions,
but also for continuing improvement and optimization,” he said.
With the deep
learning tool in hand, all radiologists can achieve expert-level performance on
stroke diagnosis, the team found. In places where resources are scarce,
democratizing access to clinical decision support could help patients achieve
significantly better outcomes.
Imaging analytics can
be similarly effective for improving the safety and accuracy of liver biopsies,
said Tina Kapur, PhD, executive director of image-guided therapy at the Brigham
& Women’s Hospital.
Nearly a million
patients worldwide undergo an image-guided liver biopsy every year, Kapur said,
and the number of biopsies is expected to increase at an annual rate of 4
percent.
Improperly
guided needles can cause significant bleeding and increase the risk of
infection, yet current ultrasound technology offers incredibly poor visual
guidance for clinicians – and more than 90 percent of biopsies are currently conducted
freehand.
Deep learning neural
networks can do a much better job than humans at identifying man-made objects,
such as needle tips, in real-time ultrasound images. Software based on
deep learning algorithms can show clinicians when the tip of the needle enters
the plane of the suspect liver structure, giving providers much-needed insight
into how to maneuver to capture a tissue sample.
Kapur’s team
envisions integrating the deep learning into the existing workflow of biopsy
technicians, keeping the procedure low-cost and preventing additional
complexity.
“On ultrasounds,
clinicians can press a button to turn on doppler information already,” she
explained. “The overlay stays there for as long as the button is
pressed. We want to add a button that says ‘needle,’ which shows the
outline and trajectory of the needle for as long as the physician needs
it. If they don’t want it, they can turn it off.”
“You don’t need extra
hardware, and you don’t need to change anything about the workflow. You can
imagine how quickly we will create better accuracy and safety while reducing
the bleeding and complications that physicians worry about the most when doing
biopsies.”
Artificial
intelligence can also help clinicians learn more about themselves, said Chris
Sidey-Gibbons, PhD, co-director of the PROVE Center at Brigham and
Women’s. By using natural language processing to analyze patient-reported
outcomes data, providers can glean more insight into the impact of their care
delivery strategies.
“There is a great
deal to be achieved from the relatively simple intervention of the
questionnaire,” Sidey-Gibbons said. “But we have been using the same format
since the 1960s. We used to have a piece of paper that we handed to
patients. Now we have an electronic form that just mimics that piece of
paper – not much of an advancement.”
“Machine learning can
improve the response rate to questionnaires and improve the usefulness of the
data through adaptive testing: something that is widely used for academic
assessments, but isn’t being used regularly in healthcare.”
Adaptive testing can
imitate the best practices of intelligent doctor, he said, by only asking the
questions that are most relevant to the patient’s unique and specific
situation.
Collecting more accurate and relevant data can
make patient-reported data more actionable, something that has been a challenge
for providers faced with combing through huge volumes of free-text responses or
nuggets of information hidden in rambling clinical notes.
Shorter, more
tailored questionnaires could save years’ worth of time for patients at scale,
he explained, while natural language processing can extract meaningful
information from numerous free-text sources.
“Patients can respond
to provider questions in a natural, freeform manner, but we can still structure
the information afterwards and use it for performance improvement,” he said.
“The limitations of
patient-reported data tools can be overcome using computerized adaptive testing
and machine learning.”
Ultimately, AI can
help equip providers with the tools they need to provide care that is
significantly safer, more consistent, more accurate, and more
patient-friendly. And these strategies are moving into the real world of
clinical care at a breakneck pace.
Many of the
presenters have validated and tested their algorithms extensively, and are only
steps away from bringing their ideas to market. As more and more
innovative models start to find their commercial footing, millions of patients
will benefit from low-cost, high-quality clinical decision support tools.
These advances are
happening right now and are expected to have an unprecedented impact on care,
said Vesela Kovacheva, MD, PhD, an anesthesiologist at Brigham & Women’s
working on a machine learning tool to improve the delivery of sedatives during
caesarian sections.
“Having access to
real-time machine learning in the operating room would be an amazing superpower
for me,” she said. “I am fortunate to have all the resources of Partners
HealthCare behind me, and it would still be a superpower.”
“Imagine what a
difference AI will make in rural hospitals or developing countries, where one
anesthesiologist could be covering three operating rooms at the same
time. If AI could be game-changing for me, just think about what it could
do for patients receiving care in those places. We can hardly quantify
how much that will change the way we practice medicine.”
No comments:
Post a Comment