Presentation used to present for GirlCodeAI

1. Renee Yao, Global Healthcare AI Startups Lead, NVIDIA | Sep 2019
CODE FOR GOOD

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HTTPS://WWW.YOUTUBE.COM/WATCH?V=GFQBBRSGVAQ&LIST=P
LZHNYVH1QTOBSJC0KHFHXLCFIFBE7LQCR&INDEX=14

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NVIDIA IN HEALTHCARE
BREAKTHROUGHS INSTRUMENTSECOSYSTEM
RESEARCHIMAGING DRUG DISCOVERY

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AI-ACCELERATED DRUG DISCOVERY
SENSORS
PERCEIVE REASON
PLAN
DATA
DATA
ANALYTICS
MACHINE
LEARNING
ACTUATORSAI MODEL
11B 6 yrs $100B X0%

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AI DETECTS
GROWTH PROBLEMS
IN CHILDREN
Detecting growth-related problems in children
requires calculating their bone age. But it’s an
antiquated process that requires radiologists to
match X-rays with images in a 1950s textbook.
Massachusetts General Hospital, which conducts
the largest hospital-based research program in
the United States, developed an automated
bone-age analyzer built on the NVIDIA DGX-1
with CUDA. The system is 99% accurate and
delivers test results in seconds versus days.
1950s 75yrs 1+day
2017 99% Seconds

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In the healthcare industry, there exists mountains
of underutilized data. 16 Bit is using AI to unlock
the value hidden in this data to augment
diagnostics and improve the quality and
accessibility of healthcare.
16 Bit is using GPU-accelerated deep learning and
big data to assist radiologists in detecting breast
cancer, analyze CT scans of the brain to exclude
acute diseases, and accurately measure pediatric
bone age. Their Bone Age analyzer has an accuracy
rate of +/- 4 months and returns results in
milliseconds―earning 16 Bit 1st place in the 2017
RSNA Machine Learning Challenge.
16 BIT.AI
RSNA BONE AGE
CHALLENGE WINNER
Try the 16 Bit algorithm: 16bit.ai/bone-age

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AI AUGMENTS
IMAGING WORKFLOWS
Medical imaging analysis is a time sink for radiologists,
but crucial for diagnosing many ailments. Arterys’ FDA-
cleared cloud-based clinical oncology solutions use
deep learning to accelerate the interpretation
of CT and MRI scans.
With algorithm training done on NVIDIA Tesla GPU
accelerators in the Google Cloud Platform and
inference occurring on Tesla GPUs running
in the Amazon cloud, the automated
intelligent imaging network helps
clinicians better manage
their busy workflows.
3
mins
10
mins
30m
ppl
15-90
seconds
170
physicians
100
hospitals
30,000
cases
A few
radiologists
BEFORE
AFTER

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Diagnosing Retinopathy of prematurity (ROP) ―a condition
that can cause blindness in preterm babies― is subjective.
Doctors compare the infant’s retina with decade’s old
photos to determine severity of ROP.
Using a dataset of 6,000 images matched with expert
ROP diagnoses and an NVIDIA DGX-1, researchers
at the Martinos Center trained a deep neural
network to differentiate ROP severity. Still
work in progress, this method could one
day help prevent blindness in
infants worldwide.
AUTOMATING THE
DIAGNOSIS OF
INFANT BLINDNESS
14k-16k 400-600 10k images
<31wks <2 ¾ lbs 3.9m 28,000

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AI ADVANCES THE
FIGHT AGAINST
BREAST CANCER
Breast cancer is the second leading cause of cancer
death for women worldwide. Genomic tests help
doctors determine a cancer’s aggressiveness so
they can prescribe appropriate treatment. But
testing is expensive, tissue-destructive, and
takes 10-14 days. Case Western Reserve is
using GPU-based deep learning with
CUDA to develop an automated
assessment of cancer risk at
1/20th the cost of current
genomic tests.
10-14 days $4000
Auto $200
BEFORE
AFTER

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Heart disease is the world’s biggest killer. Until recently,
the best test to diagnose heart disease was an invasive and
costly angiogram. HeartFlow applies GPU-accelerated deep
learning to deliver non-invasive diagnostics.
Trained on CT scans and computational fluid dynamics,
HeartFlow creates a personalized 3D model of a
patient’s coronary arteries and analyzes the
impact of blockages. Heartflow reduces the
need for an angiogram by 61% and
healthcare system costs
by 26%.
HEART SMART: AI THAT
DETECTS HEART DISEASE
Image is not representative of actual product. Courtesy of HeartFlow, Inc.
9 million
biggest
61%
26%

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AI ENABLED
HEALTHCARE AT SCALE
The human retina contains diagnostic markers for many
diseases. SocialEyes hopes to transform healthcare
systems worldwide with its low-cost AI enabled Mobile
Autonomous Retinal Evaluation (MARVIN).
MARVIN –which runs on GPU-powered Android
tablets- could help tens of millions of community
healthcare workers and physicians immediately
diagnose a wide range of conditions for
timely and effective intervention.
Marginal image quality with low
resource settings
Improved visibility of retinal features
with MARVIN
MARVIN’s AI detects high-risk cases
Diabetic retinopathy images courtesy of SocialEyes
415 million
diabetes
50%
25 yrs
2.5
million/yr

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ACCELERATING
TREATMENT FOR
ALZHEIMER’S DISEASE
Neurodegenerative diseases like Alzheimer’s affect
nearly 50 million people worldwide but still lack a
cure. Researchers at the Université de Reims created
GPU-powered software that analyzes the effects
of potential drug treatments. The interactive
interface processes hundreds of petabytes
of brain scan data at ~50 frames/second
enabling scientists to more efficiently
annotate key cells for a much
faster analysis workflow.
Hundreds of
slices
120GB each
slices
100s PB of
data
hrs vs. days
50m ppl
9.9 million
every year
1 new case
every 3.2
seconds
6th leading
cause

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BUILDING A COMMUNITY
Bioinformatics for Next Gen Sequencing

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LATEST HEALTH
TECH INNOVATION

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MIXED REALITY FOR RADIOLOGISTS

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SOLVING THE MOST
DIFFICULT PROBLEMS

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