Get the latest news on how deep learning is revolutionizing radiology in preparation for RSNA 2017.

1. The Road to RSNA 2017
REVOLUTIONIZING RADIOLOGY WITH
DEEP LEARNING

2. Medical image analysis is one of the world’s fastest
growing markets, with annual revenue in
healthcare alone increasing to $1.523 billion
worldwide in 2025 from less than $100,000 last
year, according to Tractica.
Source: https://blogs.nvidia.com/blog/2017/09/11/medical-imaging-at-miccai/

3. This month, the largest gathering of radiologists and
medical physicists takes place in Chicago, hosted by
the Radiological Society of North America (RSNA).
More than 55,000 will attend.

4. NVIDIA and its AI computing platform are driving
advancements and breakthroughs across medical
imaging with healthcare industry partners. We look
at some of them as we head to RSNA.

5. THE HEADLINER OF RSNA: MACHINE LEARNING
Brand new to RSNA this year is the Machine
Learning Pavilion, featuring AI experts and
state-of-the-art technology. Front and center
at the show will be the Deep Learning Institute
(DLI) which will:
“Give attendees a range of hands-on courses
to engage with ML tools, write algorithms and
improve their understanding of ML
technology.”
Source: http://www.rsna.org/News.aspx?id=22957
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6. 16BIT.AI WINS RSNA MEDICAL IMAGING CONTEST
The largest AI medical imaging competition in the world takes
place at RSNA each year. 16bit.ai, whose state of the art
machine learning algorithms are powered by GPUs, won the
Pediatric Bone Age Challenge this year, and will be honored on
November 27th at RSNA conference.
Assisting physicians’ diagnostic capability is 16bit.ai’s mission
is:
“To utilize modern developments in machine
intelligence to improve the accuracy, reliability, and
speed of medical image interpretation while
decreasing cost and barriers to healthcare.”
Source: http://www.16bit.ai/
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7. HEALTHCARE STARTUPS BOOMING
The number of AI and deep learning healthcare
startups has grown more than 160 percent in the last
five years, analysts estimate. Startup Arterys,
exhibiting at RSNA in the Machine Learning Pavilion,
taps into cloud computation and deep learning to
help physicians to measure blood flow through the
heart’s ventricles. It’s a process that usually takes 45
minutes. Arterys does it in 15 seconds.
“Deep learning is unleashing ideas so futuristic they
seem inspired by science fiction. One paper, for
example, explores how deep learning can analyze
images to help robots perform minimally invasive
surgery.”
Source: https://blogs.nvidia.com/blog/2017/09/11/medical-imaging-at-miccai/
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8. CENTER FOR CLINICAL DATA SCIENCE TO USE DEEP LEARNING
The Center For Clinical Data Science is pursuing deep
learning to help find breakthroughs in medical
imaging, where Dr. Keith Dreyer states:
“We’ve had CAD for a couple of decades, but
deep learning is a much better technology. It
will provide much higher sensitivity and
specificity than we have today, and
radiologists will trust it. Integrating it with
clinical practice offers many potential
benefits.”
Source: https://www.forbes.com/sites/tomdavenport/2017/11/05/revolutionizing-radiology-with-deep-learning-at-partners-healthcare-and-many-others/#4cedf4fd5e13
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9. FROM DATA CENTER LAB TO CLINIC
To differentiate themselves from the booming
number of healthcare startups, the Center of
Clinical Data Science (CCDS) is utilizing the NVIDIA
DGX-1, an AI supercomputer, to power their
research in medical imaging.
The findings are having an immediate impact,
where CCDS Executive Director Dr. Mark Michalski
states:
“As we speak, CCDS is taking our breakthroughs
straight from the data science lab into doctors’
clinics.”
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Source: https://blogs.nvidia.com/blog/2017/09/06/ai-assisted-radiology/

10. HOW AI COULD SPOT LUNG CANCER SOONER – AND SAVE LIVES
Lung cancer is the most common cancer
worldwide. It’s also one of the most deadly.
More than 80 percent of people with lung cancer
die within five years of being diagnosed, and
half die within a year. H. Michael Park, co-
founder of startup Innovation DX, is working to
improve those odds.
In December, his St. Louis-based medical
analytics company plans to release its first
product — a GPU-accelerated AI system that
detects lung cancer in its early stages from a
simple chest X-ray.
“Lung cancer is so deadly today because
it’s diagnosed so late. We wanted to see if
we could help people survive by detecting
it early.”
Source: https://blogs.nvidia.com/blog/2017/10/30/detecting-lung-cancer/
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11. AI HELPS GUIDE DECISIONS IN INTENSIVE CARE
After her mother suddenly developed a hot pepper
allergy, MIT doctoral student Harini Suresh sparked
an interest in medical research. Her latest paper:
“Shows how GPU-accelerated deep
learning predicts whether patients will need
certain ICU treatments. The model uses
hourly measurements of vital signs — such as
blood pressure, heart rate and glucose
levels plus patient information like age and
gender, to forecast needed treatments.”
Source: https://blogs.nvidia.com/blog/2017/10/02/the-ai-will-icu-now-deep-learning-helps-guide-decisions-in-intensive-care/
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12. Learn more about how deep learning is
advancing radiology
Visit the NVIDIA booth #8543
Machine Learning Pavilion
RSNA 2017