GOOD AFTERNOON.
I'M CATHERINE LAW, DIRECTOR OF
COMMUNICATIONS AT THE NATIONAL
CENTER FOR COMPLEMENTARY AND
INTEGRATIVE HEALTH, NCCIH, AT
THE NATIONAL INSTITUTES OF
HEALTH.
I WANT TO WELCOME YOU TO THE
LATEST PRESENTATION IN NCCIH'S
INTEGRATIVE MEDICINE RESEARCH
LECTURE SERIES.
TODAY'S LECTURE, "HARNESSING AI
TO EXPLORE HEALTH RESTORATION IN
DIABETES" IS PRESENTED BY DR.
AARON LEE FROM THE UNIVERSITY OF
WASHINGTON.
BEFORE WE INTRODUCE OUR SPEAKER,
A FEW BRIEF REMINDERS ABOUT
LOGISTICS FOR TODAY'S
PRESENTATION.
FIRST, A REMINDER THAT THIS
PRESENTATION IS BEING RECORDED
AND WILL BE ARCHIVED ON THE NIH
VIDEOCAST WEBSITE FOR FUTURE
VIEWING.
IF YOU HAVE QUESTIONS TODAY, YOU
ARE WELCOME TO SUBMIT A QUESTION
AT ANY TIME USING THE VIDEOCAST
FEEDBACK FORM.
THE LINK SHOULD BE LOCATED JUST
BELOW YOUR SCREEN FOR VIEWING
THE LIVE STREAM.
YOU MAY ALSO SEND QUESTIONS VIA
E-MAIL TO
NCCIHEVENTS@MAIL.NIH.GOV.
WE WILL TRY TO ANSWER AS MANY
QUESTIONS FROM THE AUDIENCE AS
WE CAN.
IF WE MISS ANY QUESTIONS, WE
WILL SHARE THEM WITH DR. LEE
AFTER THE EVENT.
WITH THOSE REMINDERS COVERED, I
AM NOW PLEASED TO INVITE DR.
DAVID SHURTLEFF, DEPUTY DIRECTOR
ON NCCIH, TO INTRODUCE OUR
SPEAKER.
DR. SHURTLEFF.
>> THANK YOU, CATHERINE.
WELCOME, EVERYONE.
MY NAME IS DAVID SHURTLEFF AND
I'M THE DEPUTY DIRECTOR OF
NCCIH.
WELCOME TO OUR SUMMER 2024
LECTURE IN NCCIH'S INTEGRATIVE
MEDICINE RESEARCH LECTURE
SERIES OR IMLS.
I'M HAPPY TO TELL YOU THAT THIS
YEAR MARKS 25 YEARS SINCE THE
FOUNDING OF NCCIH, AND TODAY'S
LECTURE IS AN EXAMPLE OF THE
POTENTIAL IMPACT OF A WHOLE
PERSON APPROACH TO RESEARCH AND
THE POTENTIAL IMPACT ON HEALTH.
DR. LEE'S TOPIC TODAY IS
DR. LEE'S TOPIC REFLECTS SEVERAL
AREAS OF RESEARCH INTEREST AT
NCCIH.
THE FIRST IS SALUTOGENESIS, OR
RESTORATION OF HEALTH.
AS MANY OF YOU MAY KNOW, AT
NCCIH, WE LOOK AT
MULTIPLE FACTORS THAT PROMOTE
EITHER HEALTH OR DISEASE AND
SCIENTIFICALLY CONSIDER THE
WHOLE PERSON AS A COMPLEX SYSTEM
THAT CAN EXIST IN A CONTINUUM OF
HEALTH AND DISEASE WITH THE GOAL
OF RESTORING HEALTH OF ALL
PEOPLE.
IT IS GREATLY APPRECIATED.
THROUGH PATHOGENESIS, WE KNOW
ABOUT DISEASES ABOUT ORGANS.
SALUTOGENESIS FOCUSES ON THE
ORIGINS OF HEALTH, RATHER THAN
DISEASE.
AND FACTORS THAT SUPPORT THE
HUMAN BEING WHICH IS LESS
STUDIED AND LESS UNDER.
WHOLE PERSON HEALTH RESEARCH
INVOLVES THE STUDY OF THE WHOLE
PERSON, NOT SEPARATE ORGANS AND
CONSIDERS MULTIPLE SYSTEMS THAT
CAN PROMOTE HEALTH OR DISEASE,
WHICH LEADS TO RESEARCH ON
MULTIMODAL APPROACHES IN
DIFFERENT SYSTEMS AND
DISCIPLINES, PROMOTING HEALTH AS
A WHOLE BODY PHENOMENON.
FINALLY, NCCIH IS INTERESTED IN
APPLYING AND USING ARTIFICIAL
INTELLIGENCE, MACHINE LEARNING,
AND USE OF BIG DATA TO ADDRESS
THE CHALLENGES OF PROMOTING
HUMAN HEALTH, WHICH WE WILL HEAR
MORE ABOUT FROM DR. LEE IN JUST
A MOMENT.
AND NOW IT IS MY PLEASURE TO
INTRODUCE DR. AARON LEE.
WITHIN THE DEPARTMENT OF
OPHTHALMOLOGY
AT THE UNIVERSITY OF WASHINGTON
IN SEATTLE, DR. LEE IS AN
ASSOCIATE PROFESSOR, THE C. DAN
AND IRENE HUNTER ENDOWED
PROFESSOR, AND A VITREORETINAL
SURGEON.
TRANSLATION OF NOVEL COMPUTATION
TECHNIQUES IN MACHINE LEARNING
TO UNCOVER NEW DISEASE
ASSOCIATIONS AND MECHANISMS FROM
ROUTINE CLINICAL DATA.
EXAMPLES INCLUDE ELECTRONIC
HEALTH RECORDS AND IMAGES.
DR. LEE RECEIVED HIS BACHELOR'S
DEGREE FROM HARVARD UNIVERSITY
AND HIS DOCTOR OF MEDICINE AND
MASTER OF SCIENCE IN CLINICAL
INVESTIGATION DEGREES FROM
WASHINGTON UNIVERSITY SCHOOL OF
MEDICINE IN ST. LOUIS, MISSOURI.
HIS POST DOCTORAL WORK WAS AT
WASHINGTON UNIVERSITY IN ST.
LOUIS, MOORFIELDS EYE HOSPITAL,
AND THE UNIVERSITY OF BRITISH
COLUMBIA.
DR. LEE IS AUTHOR OF MORE THAN
175 PUBLISHED, PEER-REVIEWED
MANUSCRIPTS, AN ASSOCIATE EDITOR
FOR BOTH TRANSLATIONAL VISION
SCIENCE AND TECHNOLOGY AND
OPHTHALMOLOGY SCIENCE, AND AN
EDITORIAL BOARD MEMBER FOR THE
AMERICAN JOURNAL OF
OPHTHALMOLOGY AND NATURE
SCIENTIFIC REPORTS.
HE IS ALSO THE CHAIR OF THE
AMERICAN ACADEMY OF
OPHTHALMOLOGY INFORMATION
TECHNOLOGY STEERING COMMITTEE
AND HAS RECEIVED MULTIPLE AWARDS
IN HIS FIELD.
TO DATE.
SUPPORTERS OF HIS RESEARCH
INCLUDE THE NIH COMMON FUND AND
THE NATIONAL EYE INSTITUTE, AND
SUPPORT FROM FOUNDATIONS AND
INDUSTRY.
DR. LEE'S RESEARCH FOCUSES ON
THE TRANSLATION OF NOVEL
COMPUTATIONAL TECHNIQUES AND
MACHINE LEARNING TO UNCOVER NEW
DISEASE ASSOCIATIONS AND
MECHANISMS FROM CLINICAL DATA.
IMPORTANTLY, HE IS INTERESTED IN
THE INTERSECTION OF LARGE
CLINICAL MEDICAL DATASETS AND
USING NONTRADITIONAL
COMPUTATIONAL TECHNIQUES TO
ANALYZE AND VISUALIZE THOSE
RESULTS.
IN THIS REGARD HE HAS CREATED
PROGRAMS TO PROCESS NEXT
GENERATION SEQUENCING DATA IN
SUPERCOMPUTING ENVIRONMENTS AND
ANALYZE NUMEROUS BIG DATA
SOURCES FROM CMS, THE U.S.
CENSUS AND THE NATIONAL LIBRARY
OF MEDICINE, MEDLINE ARCHIVES
JUST TO NAME A FEW.
DR. LEE IS ALSO CO-PRINCIPLE
INVESTIGATOR FOR THE AI-READI
EQUITABLE INSIGHTS FONDLY KNOWN
AS AI-READI SUPPORTED BY THE NIH
COMMON FUND THROUGH THE BRIDGE
TO AI PROGRAM.
COLLECTING DATA FROM A DIVERSE
POPULATION SIMULTANEOUSLY
CREATED A ROAD MAP FOR EQUITABLE
AND EQUITABLE RESEARCH FOCUSING
ON MORE THAN 4,000 RESEARCH
PARTICIPANTS TO BETTER
INVESTIGATE HEALTH OUTOL COMES
FROM TYPE 2 DIABETES.
IN PREVIOUSLY UNDER SERVED
POPULATIONS, MANY OF WHOM HAVE
BEEN IMPACTED BY THIS DISEASE.
IT IS MY PLEASURE TO WELCOME
YOU, AARON.
I WILL TURN THE MIC OVER TO YOU.
>> THANK YOU SO MUCH, DAVID.
I ALMOST FEEL LIKE I DON'T NEETD
TO GIVE MY TALK AFTER THAT LONG
INTRO.
LET ME START SHARING MY SCREEN.
SO IT IS A GREAT HONOR AND A
PLEASURE TO BE GIVEN THIS
OPPORTUNITY TO PRESENT TO YOU
ALL.
ESPECIALLY GIMVEN THAT IT IS TH
25th ANNIVERSARY OF THE FOUNDING
OF THE NCCIH.
I REALLY APPRECIATE THIS
OPPORTUNITY.
COMING FROM A DIFFERENT FIELD,
PLEASE FORGIVE ME IF I MAKE A
MISTAKE OR SAY SOMETHING
INCORRECTLY.
I'M MUCH MORE FAMILIAR WITH THE
FIELD OF OPHTHALMOLOGY THAN I AM
IN COMPLEMENTARY MEDICINE.
SO I JUST WANT TO START BY
SHOWING MY FINANCIAL
DISCLOSURES.
SOME OF THESE COMPANIES ARE
SUPPORTING THE WORK IN AI-READI
SO THERE IS SOME POTENTIAL
CONFLICTS OF INTEREST.
SO ROUGHLY IN MY TALK IS DIVIDED
INTO THESE SORT OF FOUR
SECTIONS.
I'M GOING TO START BY GIVING YOU
A BIT OF AN INTRODUCTION TO AI
AND SPECIFICALLY DEEP LEARNING.
AND THEN WALK YOU THROUGH MY
JOURNEY USING DEEP LEARNING
TECHNIQUES IN THE FIELD OF
OPHTHALMOLOGY.
THEN I'M GOING TO TALK A BIT
ABOUT MANIFOLD LEARNING AND HOW
THAT LED US TO CONCEIVE OF THE
AI-READI PROJECT THAT IS PART OF
THE BRIDGE TO AI PROGRAM.
I JUST WANT TO START BY
GROUNDING EVERYBODY IN WHERE WE
ARE AS A SOCIETY.
YOU KNOW, WE'RE REALLY LIVING IN
A BRAND NEW ERA WHERE DATA IS
BEING CREATED ALL AROUND US AND
OUR ALGORITHMS THAT ARE WATCHING
AND HARNESSING THAT DATA AND
INFLUENCING PRETTY MUCH
EVERYTHING WE SEE AND DO.
EVERY TIME YOU OPEN YOUR PHONE,
TYPE SOMETHING INTO GOOGLE,
COMPOSE AN E-MAIL, ALGORITHMS
ARE WATCHING AND LEARNING FROM
THAT DATA.
ONE OF THE THINGS THAT I REALLY
LIKE TO DO IS DISAMBIGUATE ABOUT
THESE TERMS.
THE NEWS MEDIA AND SOMETIMES THE
MEDICAL INDUSTRY USING THEM
INTERCHANGEABLE, BUT THEY ARE
ACTUALLY SEPARATE FIELDS AND
DEFINITIONS.
THE FIELD OF ARTIFICIAL
INTELLIGENCE IS ACTUALLY QUITE
OLD.
IT HAS EXISTED SINCE THE DAWN OF
MODERN COMPUTING.
THERE IS A SUB FIELD WITHIN
THERE KNOWN AS MACHINE LEARNING.
AND THEN WITHIN THE FIELD OF
MACHINE LEARNING THERE IS A SUB
FIELD THAT IS RELATIVELY NEW
CALLED DEEP LEARNING.
AND TO UNDERSTAND WHAT DEEP
LEARNING IS, YOU HAVE TO
UNDERSTAND WHAT AN ARTIFICIAL
NEURAL NETWORK IS.
THESE WERE VERY POPULAR IN THE
1980s AND 1990s WHERE THEY
THOUGHT THIS WAS GOING TO BE --
THIS WAS GOING TO TRANSFORM
MEDICINE IN EVERYTHING THAT WE
DO.
SO ALL THE TALK WE ARE SEEING
TODAY ABOUT AI ACTUALLY, THERE
WAS ANOTHER AI SUMMER THAT
OCCURRED IN THE EARLY 90s, LATE
80s WHERE THEY THOUGHT THESE
TECHNIQUES WERE GOING TO
TRANSFORM.
WHEN THE HYPE AND EXPECTATIONS
DIDN'T MEET REALITY WE WENT INTO
ANNAA WINTER THAT WE ARE COMING
OUT OF IN RECENT YEARS.
IN MY OPINION, ALL OF THIS IS
BEING DRIVEN BY THE ADVANCES OF
DEEP LEARNING.
AND THERE'S FOUR SORT OF THINGS
THAT HAPPEN AROUND THE SAME TIME
THAT LED TO THE BIRTH OF WHAT WE
CALL DEEP LEARNING TODAY.
THE FIRST WAS THE REALIZATION
THAT GRAPHICS CARDS THAT EXISTED
IN COMPUTERS COULD BE USED FOR
ACCELERATING LINEAR ALGEBRA
OPERATIONS.
THE SECOND IS THERE WERE THESE
KERNELS THAT WERE SPATIALLY
AWARE THAT COULD TAKE ADVANTAGE
OF DATA THAT WERE SPATIALLY
CORRELATED WITH ONE ANOTHER.
WHAT I MEAN BY THAT IS THINGS
LIKE IMAGES OR THINGS LIKE
SEQUENCES OF WORDS.
THERE'S PATTERNS THAT OCCUR WITH
NEIGHBORING TOKENS OR PIXELS
THAT LED TO INFORMATION THAT THE
MODELS COULD EXPLOIT.
THE THIRD WAS THE USE OF
SOMETHING CALLED NONLINEAR
ACTIVATION FUNCTIONS AND THEN
FINALLY, YOU KNOW, OUR COMPUTER
HARDWARE AND TECHNIQUES HAD TO
EVOLVE TO A POINT WHERE WE COULD
COLLECT AND COLATE LARGE AMOUNTS
OF DATA.
PUTTING THESE THINGS TOGETHER WE
LIVE IN A VERY EXCITING TIME AS
WE START TO PUSH THESE ALGOR
ALGORITHMS WITH MORE AND MORE
DATA AND MAKE THE MODELS LARGER
AND LARGER THEIRING PERFORMANCE
SEEMS TO INCREASE AND INCREASE.
THIS LED TO WHAT PEOPLE ARE
CALLED THE FOURTH INDUSTRIAL
REVOLUTION WE ARE LIVING THROUGH
TODAY.
THE CONVERGENCE OF BOTH THE
DATASETS LARGE ENOUGH TO TRAIN
THE MODELS AS WELL AS THE
TECHNIQUES THAT CAN MAKE THEM
HAPPEN IN A REASONABLE TIME.
THAT IS LEADING TO ALL THE
THINGS WE ARE READING ABOUT
TODAY, SELF-DRIVING CARS,
CHATGPT, ALL OF THESE ARE BORN
OUT OF THIS CONVERGENCE NOW.
I WANT TO REWIND THE CLOCK A
LITTLE BIT AND TELL YOU ABOUT
HOW WE GOT STAFF REPORTED IN
THIS.
TO UNDERSTAND THIS YOU SORT OF
HAVE TO UNDERSTAND A LITTLE BIT
ABOUT OUR FIELD.
THERE'S AN IMAGING MODALITY THAT
WE USE CALLED OPTICAL COHERENCE
TOMOLOGY.
THIS IS A GRAY SCALE WHERE IT IS
A CROSS SECTION OF THE RETINA
AND IT SHOWS YOU THE
ARCHITECTURE OF THE RETINA IN A
WAY YOU CAN'T REALLY SEE WITH
THE NAKED EYE.
WE AS CLINICIANS, WE LOOK INSIDE
PEOPLE'S EYES WE ARE ABLE TO SEE
PICTURES THAT LOOK LIKE THIS.
MOST OF THE PEOPLE OUTSIDE OF
THE VISION SCIENCE ASSUME WHEN
WE ARE TALKING ABOUT RETIANL
IMAGES AND PHOTOGRAPHY, WE ARE
TALKING ABOUT THE IMAGES ON THE
RIGHT.
THE DATA THAT IS GENERATED AND
AVAILABLE FOR RESEARCH IS THESE
IMAGES ON THE LEFT.
THIS IMAGING MODALITY ALLOWS US
TO SEE ALMOST A CELLULAR LEVEL
INFORMATION NONINVASIONIVELY.
IT ALLOWS US TO SEE, THIS IS A
HISTOLOGY SLIDE OF THE HUMAN
RETINA.
YOU CAN SEE THE STRIATIONS THAN
YOU CAN ON A PATHOLOGY SLIDE.
WE DIDN'T HAVE TO TAKE A PATH
SLIDE OUT OF SOMEBODY WHO IS
DECEASED TO TAKE THIS IMAGE.
WE CAN TAKE IT IN LIVE PEOPLE.
THE OCT SCAN CAN BE THOUGHT OF
AS A CT SCAN OF THE MACULA.
IT PROVIDES A THREE-DIMENSIONAL
INFORMATION ABOUT EVERY POSITION
IN THE MACULA.
AND WE, BECAUSE IT ALLOWS US TO
SEE THINGS WE CAN'T NORMALLY
SEE, WE HAVE BEEN DOING A LOT OF
THEM.
THIS IS DATA FROM A NUMBER OF
YEARS AGO NOW.
JUST IN THE UNITED STATES ALONE,
WE ARE BREAKING 5 MILLION SCANS
A YEAR.
AND IT IS BY FAR ONE OF THE MOST
COMMON IMAGING MODALITIES
CAPTURED IN MEDICINE OVERALL.
SO SO OUR FIRST FORRAY INTO DEEP
LEARNING CAME FROM THIS DATASET
I CREATED WHEN I STARTED AT THE
UNIVERSITY OF WASHINGTON.
IT HAD ABOUT 5.5 MILLION OF
THESE IMAGES.
BECAUSE WE HAD ACCESS TO THE
EHR.
WE COULD EXTRACT THESE VARIABLES
AND MATCH THEM UP.
THE VERY FIRST QUESTION WE SORT
OF ASKED WAS SORT OF THE, HELLO,
WORLD, EQUIVALENT OF DEEP
LEARNING WHERE IT IS A VERY
SIMPLE, VERY EASY CLINICAL
PROBLEM.
AND THAT'S THIS QUESTION OF
WHETHER, YOU KNOW, A DEEP
LEARNING MODEL COULD DISTINGUISH
BETWEEN A NORMAL SCAN AND AN AMD
SCAN.
AGE RELATED MACULAR
DEGENERATION.
WHICH IS ONE OF THE MOST COMMON
REASONS WE OBTAINED THESE
IMAGES.
WITH OUR DATASET WE HAD ROUGHLY
100,000 IMAGES THAT WE TRAINED.
THIS MODEL STATE OF THE ARD
CALLED VGE16 AND WITHIN A COUPLE
OF DAYS WE WERE ABLE TO SHOW
THIS KIND OF PERFORMANCE.
WE WERE ABLE TO JUST FROM GIVING
THE MODEL THESE IMAGES OF THE
OCT SCANS AS WELL AS THESE
PEOPLE HAD AMD AND THESE PEOPLE
WERE NORMAL.
THE MODEL COULD LEARN TO
DISTINGUISH BETWEEN THESE TWO
WITH ALMOST NEAR PERFECTION.
AND THIS WAS SORT OF ASTONISHING
THAT THE MODEL WAS ABLE TO DO
THIS WITHOUT ANY SORT OF HUMAN
GUIDANCE OF HOW TO ANALYZE THE
IMAGES.
ONE OF THE THINGS WE DID TO
PROVE THAT THIS WAS NOT SOME
FLUKE IS WE PROVIDED SOME POST
DOC VISUALIZATION THAT LOOKED AT
WHERE THE MODEL WAS MOST
DEPENDENT TO MAKE THIS
DETERMINATION AND IT MATCHED
WITH THE AREAS OF PATHOLOGY THAT
WE ARE USED TO SEEING AS
CLINICIANS.
THIS GAVE US SOME CERTAINTY THAT
THE MODEL WAS DOING SOMETHING
REASONABLE.
WE WENT ON AS OUR LAB TO PUBLISH
MANY, MANY PAPERS IN OUR FIELD.
SHOWING THAT DEEP LEARNING
MODELS COULD DO THINGS THAT
HUMAN CLINICIANS COULD DO AND
EVEN SHOW US NEW PATTERNS OF
DISEASE.
AND SO THIS IS, YOU KNOW, REALLY
THE TAKE HOME MESSAGE OF THIS
SECTION.
THAT WE ARE LIVING WITH A VERY
POWERFUL NEW TOOL SET THAT USES
DEEP LEARNING AND IT HAS THE
ABILITY TO LEARN DIRECTLY FROM
THE DATA AND DISCOVER NEW
PATTERNS.
I WANT TO TALK A BIT ABOUT
MANIFOLD LEARNING.
WHICH IS A SIMPLE IDEA.
IT COMES FROM THIS IDEA OF
DIMENSION REDUCTION.
ONE WAY TO THINK ABOUT IMAGE OR
A DATASET, SOMETHING LIKE WHOLE
GENOME SEQUENCING OR A WHOLE
BUNCH OF SURVEY RESULTS IS THAT
THOSE DATA ELEMENTS, WHEN THEY
ARE BROKEN DOWN PER PARTICIPANT,
LIVE IN A VERY HIGH DIMENSIONAL
SPACE.
WHICH IS NOT VERY USEFUL FOR
ANALYSIS.
WE REALLY WANT TO REDUCE IT DOWN
TO SOMETHING AS SIMPLE AS TWO
DIMENSIONS WHERE WE CAN
UNDERSTAND THE PATTERNS AND
RELATIONSHIPS THAT ARE IN THE
DATASET.
SO HERE THIS LATENT SPACE, THIS
LOWER DIMENSIONAL LATENT SPACE.
IS A TWO-DIMENSIONAL GRAPH WHERE
IT OBEYS TWO PRINCIPLES.
TWO POINTS THAT ARE CLOSE
TOGETHER ON THIS SPACE ARE VERY
SIMILAR TO ONE ANOTHER.
AND TWO POINTS THAT ARE FAR
APART ARE VERY DIFFERENT.
AND THAT IS THE ONLY REAL
ORGANIZATIONAL PRINCIPLE.
AND IF WE CAN SOMEHOW DISTILL
THE DATA DOWN IN A WAY THAT
OBEYS THESE TWO PRINCIPLES, WE
CAN START TO ORGANIZE AND MAP
THAT DATA IN A WAY THAT WE CAN
UNDERSTAND IT.
THERE'S A CLASS OF DEEP LEARNING
METHODS KNOWN AS SELF-SUPERVISED
LEARNING AND THIS IS AN
ANIMATION TAKEN FROM FACEBOOK
WHERE THEY DEVELOPED A MODEL
CALLED DINO, USING THIS
FRAMEWORK WHERE THERE'S NO
LABELS HERE.
THERE'S NO INFORMATION ABOUT
WHAT THESE IMAGES ARE, BUT JUST
FROM THE IDEA THAT A DEEP
LEARNING MODEL SHOULD BE
CONSISTENT, IT IS ENOUGH TO
TRAIN THESE MODELS IN A WAY THAT
CAN EXTRACT INFORMATION.
IT IS TO THE POINT WHERE THESE
MODELS ARE ACTUALLY ABLE TO TAKE
A SET OF GENERIC IMAGES WITH NO
LABELS ATTACHED TO THEM AND
ORGANIZE THEM INTO THESE
CLUSTERS, INTO THIS LOWER
DIMENSIONAL SPACE WHERE YOU CAN
SEE DIFFERENT PICTURES OF CATS
CLUSTERED TOGETHER, DIFFERENT
PICTURES OF DOGS CLUSTERED
TOGETHER, BUT CATS AND DOGS ARE
FAR APART ON THIS LOWER
DIMENSIONAL SPACE.
WHAT IS REALLY NEAT ABOUT THIS
FRAMEWORK, IT CAN THEN TAKE EVEN
MOVIES AND PROCESS THEM AND
POINT OUT THE MOST RELEVANT
FOREGROUND OBJECT THAT IS
PRESENT INSIDE OF THESE VIDEOS.
AND, AGAIN, WHAT IS AMAZING HERE
IS THAT THE MODELS LEARN TO DO
THIS ON THEIR OWN.
ONE OF OUR COLLABORATIONS WITH
MICROSOFT WAS TO USE THIS FAMILY
OF TECHNIQUES AND USE THAT
DATASET THAT I HAD SHOWN YOU
BEFORE WHERE INSTEAD OF NORMAL
AMD, WE TOOK THE ENTIRE DATASET
OF THE 5.5 MILLION AND WE
APPLIED THESE TECHNIQUES AND
PROJECTED THEM DOWN INTO A LOWER
DIMENSIONAL SPACE AND THEY
FORMED THESE SORT OF BEAUTIFUL
ONE DIMENSIONAL MANIFOLDS.
YOU CAN SEE WHEN YOU COLOR BY
AGE, THE MANIFOLDS ARE OBEYING
THE PRINCIPLES PEOPLE ON THIS
SIDE ARE MUCH YOUNGER AND THE
PEOPLE ON THE UPPER RIGHT SIDE
ARE MUCH OLDER AND MOST OF THE
DISEASES ARE OCCURRING IN THIS
UPPER RIGHT QUADRANT.
SO THIS IS AN EXAMPLE OF WHERE
WE'VE BEEN ABLE TO ACTUALLY TAKE
A SINGLE IMAGE AND EMBED THEM IN
THIS LOWER DIMENSIONAL SPACE IN
A WAY WE CAN UNDERSTAND THAT
PATTERN.
I WANT TO FOCUS THIS IDEA DOWN
INTO A SINGLE DISEASE.
THIS IS TAKING THE SAME IDEA
EXCEPT WE ARE USING ONE HUMAN
DISEASE KNOWNED A MACULAR -- WE
KNOW THE DYNAMICS OF THIS
DISEASE, THAT IT ONLY REALLY
AFFECTED THE EYE AND IT IS
PROGRESSIVE.
ONCE YOU START HAVING THIS
DISEASE, IT CONTINUES TO
PROGRESS IN A UNIDIRECTIONAL
FASHION.
WHAT WE WERE ABLE TO DO IS TRAIN
THIS MODEL IN A WAY THAT WOULD
CREATE THIS ONE-DIMENSIONAL
MANIFOLD WHERE THE UPPER LEFT IS
VERY HEALTHY AND NORMAL AND THE
LOWER RIGHT IS VERY DISEASED.
THIS ONE-DIMENSIONAL MANIFOLD IS
ABLE TO SORT OF SHOW US A
PROGRESSION OR A SPECTRUM OR A
CONTINUUM OF DISEASE AS IT GOES
FROM VERY HEALTHY TO HAVE
DISEASED.
NOW, WHAT'S REALLY NEAT IS WE
DEVELOPED THIS SORT OF
VISUALIZATION TO KIND OF SHOW,
YOU KNOW, A FLY THROUGH OF THIS
ONE DIMENSIONAL MANIFOLD AND AS
YOU CAN SORT OF SEE IN THE
GRAPHS THAT ARE SHOWING UP, THAT
THERE'S MORE AND MORE DISEASE
BEING PRESENT IN THOSE OCT
IMAGES AS WE GO FROM THE UPPER
LEFT DOWN TO THE LOWER RIGHT.
MY POINT HERE IS REALLY THAT
DEEP LEARNING, THESE TECHNIQUES,
THESE SETS OF TECHNIQUES THAT
ARE BECOMING MORE POPULAR CAN
REALLY SORT OF ORGANIZE
INFORMATION COMPLETELY DE NOVO,
SO WITHOUT ANY GUIDANCE FROM THE
CLINICIANS.
THESE MODELS ARE ABLE TO EXTRACT
OUT RELEVANT INFORMATION AND
ORGANIZE THEM IN A FASHION WHERE
WE CAN START TO UNDERSTAND THE
NEW PATTERNS OF DISEASE
PROGRESSION.
SO THIS LAST PART, EVEN THOUGH
THIS IS THE LAST PART, THIS IS
THE LONGEST PART OF MY TALK
BECAUSE I WANTED THOSE OTHER
SECTIONS TO BE THE BACKDROP TO
TALKING ABOUT WHAT WE'RE DOING
IN THE AI-READI PROJECT.
AND TO UNDERSTAND THIS PROGRAM,
IT'S A LITTLE BIT -- IT IS
IMPORTANT TO TAKE A STEP BACK
AND TALK ABOUT THE COMMON FUND
BRIDGE2AI AM.
THIS IS A PROGRAM HAPPENING NOT
AT A SINGLE INSTITUTE BUT AT THE
COMMON FUND LEVEL TO TRY TO FILL
THIS BIG GAP THAT EXISTS IN THE
FIELD OF MEDICINE.
WE REALLY LACK THE AVAILABILITY
OF WELL-CURATED LARGE DATASETS
THAT ARE UNBIASED AND FAIR TO
TRAIN AND DISCOVER NEW THINGS.
SO WE, AS A FIELD, HAVE ONLY
BEEN ABLE TO USE ROUTINELY
COLLECTED DATA WITH LOTS OF
BIASES AND ONLY CAPTURE CERTAIN
DIMENSIONS OF HEALTH AND NOT
REALLY CONSIDERED THE WHOLE
PERSON PICTURE.
SO THE BRIDGE2AI PROGRAM HAS
THREE PILLARS IT IS ORGANIZED
AROUND.
FIRST IS THE DATA, THE SECOND IS
ETHICS AND THE THIRD IS THE
PEOPLE.
AND THE REMIT OF THE PROGRAM, OF
THE BRIDGE2AI IS REALLY TO FOCUS
ON THIS FIRST SORT OF CIRCLE IN
THE LIFE CYCLE OF, YOU KNOW,
TRAINING MACHINE LEARNING MODELS
OR DOING AI WORK IN MEDICINE.
IT IS TO REALLY, YOU KNOW, BUILD
AND PREPARE A DATASET THAT COULD
THEN GO ON AND BE USED FOR MODEL
DEVELOPMENT AND THEN FINALLY FOR
EVALUATION AND DEPLOYMENT.
AND SO THIS SORT OF SQUARE UP
HERE IN THE UPPER PART OF THIS
LIFE CYCLE IS WHAT THE BRIDGE2AI
CONSORTIUM IS REALLY FOCUSED ON.
THE CONSORTIUM AND THE DATA
GENERATION PROJECTS ORIGINALLY
WHAT WERE BROKEN UP INTO SIX
DIFFERENT MODULES AND HAVE NOW
BEEN REORGANIZED INTO THESE
THREE PILLARS I TALKED ABOUT
BEFORE.
IT IS SUPPOSED TO GIVE US NOT
ONLY THE DATA REPOSITORIES, BUT
ALSO THE BLUEPRINT OF HOW TO
GENERATE THESE TYPES OF DATASETS
IN THE FUTURE.
IN THE BRIDGE2AI PROGRAM, THERE
ARE FOUR DATA GENERATION
PROJECTS.
THE FIRST THAT IS LISTED HERE IS
THE CELL MAPS FOR AI OR THE
FUNCTIONAL GENOMICS GRANT
CHALLENGE.
THIS IS BASIC SCIENCE FOCUSED
DATASET WHERE THEY ARE TRYING TO
BUILD CELL MAPS OF EVERY CELL
TYPE IN THE HUMAN BODY.
THE SECOND IS A VOICE DATA
INFORMATION PROJECT THAT IS
TRYING TO COLLECT THE DATASET TO
STUDY HOW HUMAN SPOKEN SPEECH
COULD BE USED AS A BIOMARKER OF
HEALTH.
IT HAS VERY DIFFERENT DOMAINS
AND DATA TYPES TIED WITH VOICE
RECORDINGS TO SEE IF MODELS
COULD LEARN THE STATE OF
SOMEBODY'S HEALTH BASED ON
SOMEBODY'S VOICE.
THE THIRD IS OUR PROJECT KNOWN
AS SALUTOGENESIS OR AI-READI.
I WILL GO INTO MUCH MORE DETAIL
ABOUT THAT DATASET IN JUST A
SECOND.
THE LAST DATASET HERE IS KNOWN
AS THE CRITICAL CARE OR CHORUS
DATASET, MAINLY FOCUSED ON
PEOPLE IN THE ICU AND THE DATA
ASSOCIATED WITH THOSE PEOPLE TO
SEE IF WE CAN FIGURE OUT HOW TO
PREVENT MORTALITY IN THE ICU OR
READMISSION BACK TO THE
HOSPITAL.
SO THESE ARE THE FOUR DATA
GENERATION PROJECTS PART OF THE
BRIDGE2AI PROGRAM.
SO WHEN I SAY OURS, I'M
REFERRING TO CECILIA, MY WIFE,
THE CO-PI AND ME, AI READY ATLAS
FOR DIABETES INSIGHTS.
WE CAME UP WITH THIS IDEA AND
BLUEPRINT OF STUDYING
SALUTOGENESIS IN TYPE 2
DIABETES.
SO THE FUNDAMENTAL GOAL OF OUR
DATA GENERATION PROJECT IS TO
CREATE A MULTIDIMENSIONAL,
ETHICALLY SOURCED DATASET IN
DIVERSE PEOPLE FOR STUDYING
SALUTOGENESIS IN TYPE 2 DIAB
DIABETES.
HOPEFULLY THIS AUDIENCE IS
FAMILIAR WITH THE TERM
SALUTOGENESIS.
BUT IF YOU ARE NOT, IT CAN BE
THOUGHT OF AS THE REVERSE
PROCESS OF PATHOGENESIS.
WE'VE SPENT -- REALLY THE MAIN
FOCUS OF A LOT OF BIOMEDICAL
RESEARCH FOR THE LAST CENTURY OR
TWO HAS BEEN STUDYING THE
PATHWAYS OF DISEASE.
HOW DOES SOMEONE GO FROM BEING
HEALTHY TO DISEASED?
HOW DO WE SLOW IT DOWN OR
IDEALLY SORT OF STOP IT?
WE SPENT VERY LITTLE TIME AND
ENERGY THINKING ABOUT THE
REVERSE PROCESS OF HOW DO WE
ACTUALLY RETURN SOMEBODY BACK TO
A HEALTHY STATE?
AND TYPE 2 DIABETES IS A DISEASE
WHERE I SORT OF BELIEVE THAT A
SALUTOGENESIS TYPE PROCESS WILL
LIKELY EXIST.
AND THAT'S THE REASON WHY WE
CHOSE IT AS A MODEL DISEASE FOR
THIS PARTICULAR PROJECT.
OUR DATASET IS STRUCTURED IN A
TRIPLE BALANCED FASHION.
AND I'LL EXPLAIN WHY THAT IS IN
JUST A SECOND.
BUT WE REALLY WANTED TO HAVE AN
EVEN REPRESENTATION OF DIFFERENT
RACE ETHNICITY GROUPS AS WELL AS
BIOLOGICAL SEX.
OF THE 4,000 PEOPLE WE ARE
TRYING TO COLLECT PEOPLE ON,
1,000 OF THE PEOPLE ARE SUPPOSED
TO BE WHITE, 1,000 BLACK, 1,000
ASIAN AMERICAN AND 1,000
HISPANIC.
WITHIN THAT WE WANTED TO SAMPLE
EVENLY ALONG THE DISEASE ACCESS
OF TYPE 2 DIABETES.
AND THAT IS VERY IMPORTANT WHEN
YOU ARE DOING DIMENSION
REDUCTION TO MAKE SURE THAT ONE
OF THE GREATEST SOURCES OF
VARIABILITY IS THE DISEASE
ITSELF, THE DISEASE OF INTEREST.
1,000 OF THESE PEOPLE WILL BE
NORMAL, 1,000 LIFESTYLE
CONTROLLED OR PREDIABETIC, 1,000
CONTROLLED WITH ORAL MEDICATIONS
AND 1,000 WILL BE DEPENDENT UPON
INSULIN.
WITHIN EACH ONE OF THESE BINS,
WE ARE HOPING TO ACHIEVE ONE TO
ONE BALANCING OF MALES AND
FEMALES.
THIS IS VERY IMPORTANT BECAUSE
MOST BIOMEDICAL DATASETS ARE
PREDOMINANTLY FEMALE BECAUSE
FEMALES ARE MUCH MORE LIKELY TO
SEEK SORT OF CLINICAL CARE THAN
MALES ARE.
SO IF YOU USE A ROUTINELY
COLLECTED DATASET, OFTEN IT IS
VERY DISEASED ENRICHED, OFTEN IT
IS BIASED TOWARDS FEMALES, AND
OFTEN IT IS BIASED TOWARDS WHITE
PEOPLE.
THIS MAKES IT VERY CONCERNING
BECAUSE WHEN WE TRAIN DEEP
LEARNING MODELS AND WE GO AND
LET'S SAY WE HAVE AN AMAZING
DEEP LEARNING MODEL THAT CAN
DIAGNOSE DIABETES, IF WE TRAINED
IT ON A ROUTINELY COLLECTED
DATASET, THERE IS A CHANCE WHEN
WE GO AND APPLY THIS MODEL TO
PEOPLE WHO ARE NON-WHITE, THAT
THE MODEL WILL MAKE A
CATASTROPHIC FAILURES OR
MISTAKES AND COULD LEAD TO, YOU
KNOW, HARM RATHER THAN GOOD.
WE HAVE THREE DATA COLLECTION
SITES, ONE AFT UNIVERSITY OF
WASHINGTON, ONE AT UNIVERSITY OF
ALABAMA AT BIRMINGHAM AND ONE AT
UNIVERSITY OF CALIFORNIA AT SAN
DIEGO.
I REALLY WANT TO SORT OF
EMPHASIZE THE POINT OF THIS
SLIDE AND THAT IS THAT, YOU
KNOW, IN OUR DATASET, WE ARE
REALLY COLLECTING DATA ELEMENTS
FROM HEAD TO TOE AND EVEN BEYOND
THAT.
AND SO, YOU KNOW, FROM --
BECAUSE WE ARE SORT OF COME FROM
THE OPHTHALMOLOGY FIELD, WE
SELECTED THAT ARE HIGHLY CUTTING
EDGE AND RELEVANT TO TYPE 2
DIABETES.
WE ARE COLLECTING THINGS FROM
PEOPLE'S HEARTS, EKG, MANY FORMS
OF BLOODWORK THAT PROBE
EVERYTHING FROM KIDNEY FUNCTION
TO DIFFERENT MARKERS OF DIABETES
AND HEART HEALTH.
WE SENT PEOPLE HOME WITH THESE
DIFFERENT WEARABLE DEVICES AND
THEN WE ARE BANKING THESE
SAMPLES.
THIS IS WHAT OUR DATA COLLECTION
PROTOCOL LOOKS LIKE.
IT IS A CROSS SECTIONAL DATASET.
OF THE 4,000, WE ARE COLLECTING
ALL THIS INFORMATION ON THEM
ONCE.
AND WHAT WE ARE ASKING PEOPLE TO
DO BEFORE THEY COME IN IS A
WHOLE HOST OF DIFFERENT SURVEYS
THAT INCLUDES THINGS LIKE
DEPRESSION SCREENING, LOOKING AT
DIFFERENT AREAS OF DIABETES
HEALTH, DIET SURVEY, SMOKING
HISTORY, AND SOCIAL DETERMINANTS
OF HEALTH.
AND DIFFERENT ACCESS TO HEALTH
CARE.
AND SO THESE ARE ALL THE
DIFFERENT SURVEYS WE ASK PEOPLE
TO DO AT HOME BEFORE THEY COME
IN.
WHEN THEY ACTUALLY COME IN FOR
THE CLINICAL SITE VISIT IT IS
ABOUT A FOUR-HOUR VISIT WHERE
THEY COME IN AND WE DO A WHOLE
HOST OF DIFFERENT THINGS WITH
THEM.
ONE IS, OF COURSE, RETINA
IMAGING, OCT AND THE PHOTOS I
SHOWED YOU EXAMPLES OF BEFORE.
WE OBVIOUSLY COLLECT THINGS LIKE
HEIGHT, WEIGHT, WAIST AND HIP
CIRCUMFERENCE, BLOOD PRESSURE
AND HEART RATE.
WE DO SOME VISION TESTING.
WE COLLECT ALL THEIR MEDICATION
HISTORY.
WE ALSO ASK THEM FOR PERMISSION
TO PULL THEIR DRIVING RECORD TO
UNDERSTAND HOW THEIR DRIVING
BEHAVIOR IS AND WHETHER THEY
HAVE ANY ACCIDENTS THAT ARE
LISTED IN THEIR NAME.
WE TAKE OFF THEIR SHOES AND WE
DO A MONOFILAMENT TEST TO TEST
FOR PERIPHERAL NEUROTHOPY, WE
PERFORM A TEST CALLED MOCA, A
WHOLE HOST OF BLOOD TESTS AND
TAKE THEIR BIOSPECIMENS AND MAIL
TO THE UNIVERSITY OF ALABAMA,
BIRMINGHAM WHERE THEY ARE CRYO
FROZEN TO BE USED AS ASSAYS IN
THE FUTURE.
FOR ABOUT 10 DAYS AT HOME, THEY
ARE WEARING A DEX CON G6 GLUE
KOES -- GLUCOSE MONITORING
DEVICE.
WE ASK THEM TO WEAR A GARMIN
FINANCE TRACKER.
AND FINALLY WE BUILT AN
ENVIRONMENTAL SENSOR.
THAT CAN MEASURE THINGS LIKE
TEMPERATURE, HUMIDITY, BUT ALSO
PARTICULATE MATTER INSIDE OF
THEIR HOMES AND THINGS LIKE
ORGANIC COMPOUNDS.
IT HAS A SPECTROGRAM THAT CAN
MEASURE LIGHT IN PEOPLE'S HOME
SO WE CAN TELL AS THEY TURN
LIGHTS ON AND OFF AS THE HOUSE
GETS DARK, WE CAN LOOK AT THE
SPECTRUM OF LIGHT AND TELL
WHETHER THEY HAVE FLUORESCENT
LIGHTBULBS.
THE OTHER THING THAT IS REALLY
IMPORTANT IS THERE IS A LOT OF
TALK WHETHER GAS STOVES ARE SAFE
OR NOT.
ONE OF THE BIG PROBLEMS WHEN YOU
ARE LOOKING AT ENVIRONMENTAL
RESEARCH HAS BEEN THAT PEOPLE
HAVE MAINLY BEEN MAPPING FIVE
DIGIT ZIP CODES TO ATMOSPHERIC
DATA AND PRESUMES THAT IS THE
AIR THAT PEOPLE ARE BEING
EXPOSED TO.
MOST PEOPLE ARE SPENDING, YOU
KNOW, THEIR WINTER MONTHS INSIDE
OF THEIR HOMES AND PROBABLY AT
LEAST 50% OF THEIR LIFE WHEN
THEY ARE ASLEEP, YOU KNOW, IS
BEING SPENT INSIDE OF THEIR
HOMES, BREATHING THE AIR INSIDE
OF THEIR HOMES.
SO IT IS REALLY IMPORTANT, IN MY
OPINION, TO DO THIS RESEARCH BY
MEASURING THE AIR SAMPLE INSIDE
OF THEIR HOMES TO UNDERSTAND
WHAT KIND OF AIR THEY ARE
BREATHING.
SO I WANT TO SPEND A LITTLE BIT
OF TIME HERE TALKING ABOUT THIS
DIMENSION REDUCTION TECHNIQUE OR
MANIFOLD LEARNING I WAS TALKING
ABOUT IN THE PREVIOUS SECTION.
SO WE'RE GOING TO GO THROUGH
SORT OF TWO MODEL PATIENTS.
YOU KNOW, LET'S PRETEND THAT WE
HAVE A 41-YEAR-OLD MALE WHO HAS
REALLY NO PAST MEDICAL HISTORY,
BUT HAS HAD POOR DIETARY HABITS
FOR MOST OF HIS LIFE AND, YOU
KNOW, QUITE FRANKLY, THIS MIGHT
BE ME.
AND, YOU KNOW, A COUPLE OF YEARS
LATER THEY END UP HAVING A MORE
SEDENTARY LIFESTYLE AND THEN BY
THE TIME THEY TURN 47, THEY
NEVER REALLY WENT FOR A PHYSICAL
CHECKUP.
AND THEN BY THE AGE OF 49, THEY
START DEVELOPING COMPLICATIONS
FROM UNTREATED DIABETES.
THESE COLOR DOTS SORT OF GO FROM
HEALTHY DOWN TO DISEASE AND
REPRESENTS SORT OF THE BODY
STATE, THE WHOLE PERSON HEALTH
STATE OF THIS PARTICULAR MODEL
PATIENT.
AND WHAT'S BEING PLOTTED HERE ON
THE X AXIS IS TIME, SINCE SORT
OF THE FIRST CLINICAL VISIT OF
THIS PERSON.
SO IF YOU TAKE A DIFFERENT
PERSON AND LET'S SAY THIS OTHER
MODEL PARTICIPANT IS 28 YEARS
OLD, MALE, NO PAST MEDICAL
HISTORY, AROUND 31 THEY STOPPED
EXERCISING REGULARLY, BECAME
MORE SEDENTARY, PICKED UP SOME
BAD DIETARY HABITS BUT FINALLY
GOES TO GET A PHYSICAL AND GETS
DIAGNOSED WITH EARLY DIABETES
AND REALLY THAT ENDS UP BEING A
WAKEUP CALL.
HE STARTS SEEING A HEALTH COACH,
EXERCISING REGULARLY, WATCHING
WHAT HE HEATS AND HE HOPEFULLY
STARTS REVERSING HIS HEALTH
STATE BACK TO A HEALTHY STATE.
ONE OF THE POINTS I WANT TO MAKE
HERE IS THAT THE BODY STATE IS
VERY UNLIKELY THAT THEY ARE
GOING TO BE IDENTICAL WHEN THEY
ARE SORT OF GOING DOWN THIS WAY
ON THIS GRAPH VERSUS STARTING TO
TRY TO TURN THE TIDE AND LIVE A
MORE HEALTHY LIFESTYLE AFTER
BEING AT A LOWER POINT ON THIS
GRAPH.
SO IT IS VERY UNLIKELY THAT
THESE TWO STATES ARE EXACTLY THE
SAME.
SO IF YOU TAKE THESE TWO MODEL
PARTICIPANTS AND THEN YOU PUT
THEM ON THE SAME GRAPH AND YOU
USE AGE, ONE WAS YOUNGER, ONE
WAS MUCH OLDER, THEY WILL END UP
SORT OF LOOKING LIKE THIS.
WHERE YOU CAN'T REALLY MAKE
HEADS OR TAILS OUT OF THEM.
BUT INSTEAD, WHAT WE TRY TO DO
IS WE TRY TO ALIGN THE BODY
STATES TOGETHER.
WE CAN SORT OF COMBINE THESE
GRAPHS TOGETHER AND IF WE SAMPLE
LOTS AND LOTS OF PEOPLE, WE WILL
HOPEFULLY FIND SORT OF THIS
GRAPH WHERE THERE ARE PEOPLE
SORT OF GOING THROUGH SIMILAR
BODY STATES AS THEY TRANSITION
FROM HEALTHY TO DISEASED AND
SIMILAR BODY STATES AS THEY GO
FROM DISEASED BACK TO HEALTHY.
AND THESE -- THIS X AXIS WENT
FROM TIME TO AGE TO NOW PSEUDO
TIME, WHICH PSEUDO TIME IS NOT A
REAL TIME INTERVAL, BUT IT IS
BEING COLLAPSED BY SIMILAR
POSITIONS OF END STATES OF
HEALTH.
AND SO THAT'S SORT OF THE GRAND
IDEA BEHIND OUR DATASET.
WHAT WE ARE HOPING TO DO IS
COLLECT THIS VERY LARGE
MULTIMODAL DATASET EXPANDS FROM
HEAD TO TOE AND EXAMINES THE
ENVIRONMENT AROUND THEM AND
BUILD A DATASET TO BE USED FOR
DIMENSIONAL REDUCTION TO BUILD
ONE DIMENSIONAL MANIFOLDS IN
THIS LOWER DIMENSIONAL SPACE
WHERE ONE AXIS IS PSEUDOTIME AND
THE OTHER AXIS IS HEALTHY AND
DISEASED.
BY DOING SO WE CAN STUDY
DIFFERENT THINGS LIKE
PATHOGENESIS AND SALUTOGENESIS.
THAT IS THE FUNDAMENTAL IDEA
BEHIND OUR DATASET AND THE GRAND
CHALLENGE THAT WE ARE BUILDING.
NOW ONE THING THAT I'M SORT OF
EXCITED TO SHOW YOU IS THAT IN
DECEMBER OF LAST YEAR, WE
COMPLETED OUR PILOT AND WE HAD
ABOUT 204 PARTICIPANTS IN THERE.
AND THIS DATASET HAD ABOUT 202,
EKGs, 28,000 LAB MEASUREMENTS.
FROM THE WEARABLE DEVICES WE
HAVE MILLIONS OF DATA POINTS OF
WHAT THEIR GLUCOSE WAS AND HEART
RATE AND OXYGEN SATURATION,
STRESS LEVEL AND HOW MANY HOURS
OF SLEEP THEY WERE GETTING.
WE HAVE 33 MILLION SAMPLES,
RECORDINGS OF DIFFERENT AIR
POLLUTION LEVELS AS WELL AS
LIGHT SPECTROMETRY MEETINGS AND
RETINAL IMAGING.
THIS IS 21,000 FILES TAKING UP
311 GIGABYTES OF DATA, 2500
BIOSPECIMENS FROM THIS COHORT
HAVE BEEN BANKED.
WHAT I'M REALLY EXCITED TO SHARE
IS A COUPLE OF MONTHS AGO WE
PUBLISHED THIS DATASET.
IT IS OPENLY AVAILABLE FOR
PEOPLE TO GO TO OUR WEBSITE AND
REGISTER AND DOWNLOAD THIS
DATASET TO YOUR COMPUTERS TO DO
ANALYSIS WITH.
WE HAVE A WHOLE HOST OF
DIFFERENT MECHANISMS THAT WE PUT
IN PLACE TO SHARE IT IN THE MOST
SAFE AND RESPONSIBILITY WAY
POSSIBLE.
BUT WE ALSO WANTED TO GIVE THE
ABILITY FOR PEOPLE TO ACTUALLY
DOWNLOAD THE DATASET TO THEIR
COMPUTERS, USE THE TOOLS AND
ANALYSIS PACKAGES THEY ARE
FAMILIAR WITH AND DISCOVER NEW
THINGS.
THIS IS JUST FROM OUR PILOT.
IT HAS 204 PARTICIPANTS, BUT WE
ARE AIMING FOR 4,000 PEOPLE.
SO THE FULL DATASET SHOULD BE
ABOUT 20 TIMES.
ALL THESE NUMBERS SHOULD BE
MULTIPLIED BY 20 BY THE TIME WE
GET TO THE END OF IT.
WE DO HOPE TO SHARE IT IN THE
SAME WAY.
EVERY YEAR WE PLAN TO RELEASE
ALL THE DATA THAT WE HAVE.
OUR NEXT DATA RELEASE IS SLATED
FOR NOVEMBER.
WE ARE ALREADY PAST 800 SOME
PARTICIPANTS IN OUR DATASET NOW
AND SO BY THE TIME WE GET TO
NOVEMBER WE ARE HOPING TO
RELEASE 1,000 PEOPLE WORTH OF
DATA FOR PEOPLE TO USE FOR
ANALYSIS.
WE HAVE A NUMBER OF DIFFERENT
PARTNERS.
MANY OF THESE COMPANIES WERE
GRACIOUS ENOUGH TO EITHER LEND
US THEIR DEVICE OR PROVIDE
DISCOUNT PRICING.
ONE PARTNER IN PARTICULAR,
MICROSOFT WAS ABLE TO KIND OF
GIVE US FREE CLOUD CREDITS TO
USE TO BUILD AND SHARE THIS
DATASET WITH THE WORLD.
I JUST WANT TO END BY SORT OF
ACKNOWLEDGING THE MANY, MANY
PEOPLE THAT ARE BEING SUPPORTED
BY THIS PROJECT.
THERE'S MANY ELEMENTS OF THIS
THAT -- OF THIS PROGRAM, OUR
PROJECT IN PARTICULAR, THAT I
DIDN'T HAVE TIME TO SORT OF GO
INTO.
FOR EXAMPLE, SALLY BAXTER AT
UCSD IS LEADING AN INTERNSHIP
PROGRAM WHERE WE ARE TAKING
PEOPLE FROM DIVERSE BACKGROUNDS
AND BRINGING THEM INTO THE
DIFFERENT INVESTIGATORS' LABS IN
THIS PROJECT AND HELPING THEM
LEARN HOW TO USE AI AND TRAIN
DEEP LEARNING MODELS AND LEARN
MACHINE LEARNING.
WE HAVE A WHOLE GROUP OF PEOPLE
THAT IS TRYING TO STANDARDIZE
DATA SO IT IS VERY EASY TO USE
DATA ONCE YOU DOWNLOADED IT AND
YOU DON'T NEED TO SPEND AN INORD
NANT AMOUNT OF TIME ORGANIZING
THE DATA BEFORE YOU START USING
STATISTICAL MODELS OR TRAINING
DEEP LEARNING MODELS.
WE HAVE, YOU KNOW, A GROUP AT
JOE -- AND NATIVE BIODATA IS
ENGAGING WITH AN AMERICAN INDIAN
TRIBE TO SEE IF THEY MIGHT BE
INTERESTED IN REPLICATING OUR
PROTOCOL TO COLLECT DATA INSIDE
OF THEIR TRIBE.
SO THEY MAY, IF THEY, SORT OF
THE IDEA BEHIND THAT IS IF THEY
FOLLOW OUR COLLECTION PROTOCOL
AND USE THE EXACT SAME STEPS
THEN THAT DATA WOULD BECOME
HARMONIZABLE WITH THE LARGER
DATASET.
THERE IS ANOTHER GROUP OF FOLKS
LIKE SARAH SINGER AT STANFORD
THAT IS TRYING TO UNDERSTAND HOW
DIFFERENT MULTIDISCIPLINARY
TEAMS CAN INTERACT TOGETHER IN
TEAM SCIENCE.
THAT IS JUST A FLAVOR OF THE
VERY LARGE GROUP OF PEOPLE THAT
ARE BEHIND THIS PROJECT TRYING
TO MAKE IT A SUCCESS.
THIS IS THE QR CODE TO OUR
WEBSITE WHERE YOU CAN, YOU KNOW,
VIEW THE DOCUMENTATION AND
ACCESS THE DATASET.
WHEN YOU GO TO ACCESS THE
DATASET IT WILL TALK YOU THROUGH
MANY DIFFERENT STEPS BEFORE YOU
CAN DOWNLOAD THIS DATASET.
ONE OF THOSE ACTUALLY INVOLVES
SORT OF WATERMARKING THE DATASET
SO THAT DATASET YOU DOWNLOAD IS
DIGITALLY TRACEABLE BACK TO YOU
SO IF YOU SORT OF VIOLATE OUR
TERMS AND YOU RESHARE THAT
DATASET, WE WOULD KNOW SORT OF
WHO YOU ARE AND BE ABLE TO TRACK
YOU DOWN.
SO THERE ARE SORT OF ALL SORTS
OF THINGS THAT WE'VE DONE IN
THIS PROJECT TO SORT OF TRY AND
BUILD A BLUEPRINT FOR HOW TO
COLLECT SUCH A DATASET IN THE
FUTURE AND RELEASE IT SAFELY TO
THE WORLD.
SO I'M HOPING AND GUESSING THAT
THERE WILL BE LOTS OF QUESTIONS
THAT WE CAN SPEND SOME TIME
GOING OVER AND I GUESS I'LL STOP
SHARING NOW AND BE HAPPY TO TAKE
THOSE QUESTIONS.
>> WELL, THANK YOU SO MUCH, DR.
LEE, THAT WAS JUST RICH WITH
MANY THINGS.
A LOT OF DATA AND A LOT OF
REALLY INTERESTING FUTURE
DIRECTIONS.
SO IT SOUNDS LIKE THINGS ARE
GOING TO GO.
I KNOW THAT DAVID, I THINK YOU
HAVE SOME QUESTIONS TO KICK US
OFF WITH.
SO DAVID, WHAT IS YOUR FIRST
QUESTION FOR DR. LEE?
>> FIRST OF ALL, AARON, THANK
YOU SO MUCH.
YOU HAVE SHOWN US THE FUTURE OF
HEALTH CARE RESEARCH AND HEALTH
CARE NOW.
YOU ARE SHOWING US THE WAY HOW
WE CAN REALLY USE RESEARCH TO
BENEFIT PEOPLE, RESTORE THEIR
HEALTH, MAINTAIN THEIR HEALTH
AND CURE DISEASE.
SO I THANK YOU FOR WHAT YOU'RE
DOING.
IT ALSO POINTS TO THIS IDEA,
THIS LEARNING HEALTH CARE SYSTEM
IT IS INTERTWINED PATIENT CARE
OF RESEARCH THAT FIT TOGETHER IN
WAYS OF PROMOTING HEALTH AND
HEALTH CARE.
SO THANK YOU FOR ALL YOU ARE
DOING.
I GUESS JUST A COUPLE OF
QUESTIONS AROUND BIG DATA AND
THE FACT YOU RELEASED YOUR FIRST
WAVE OF DATA.
CAN YOU SPEAK TO THE ISSUES
AROUND FAIR, FINDABLE,
ACCESSIBLE, INTEROPERABLE AND
REUSABLE?
WE HAVE ALL OF US PROGRAM AT NIH
THE UK DATA BANK, THE WORK YOU
ARE DOING AND OTHERS THROUGH
BRIDGE2AI.
ARE WE ALL WORKING -- WE ARE ALL
WORKING TOWARDS THE SAME GOAL.
ARE THERE WAYS TO HARNESS ALL
THESE DATA, MAKE THEM
INTEROPERABLE, SHAREABLE?
YOUR THOUGHTS ON WHERE WE ARE
GOING WITH BIG DATA IN GENERAL
AROUND PRINCIPLES OF FAIR?
>> YEAH.
SO WE, AND WHEN I SAY WE, I MEAN
THE FIELD OF MEDICINE WRIT
LARGE.
I THINK IT IS REALLY IMPORTANT
THAT SORT OF THE ROADS OF
PROGRESS LEAD TO A PLACE WHERE
DATA IS ULTIMATELY HARMONIZABLE
ACROSS ALL OF THESE DIFFERENT
EFFORTS.
AND SO, YOU KNOW, WITHIN THE
BRIDGE2AI PROGRAM, THERE IS A
LOT OF WORK THAT IS BEING DONE
AT THE CENTER LEVEL AS WELL AS
BETWEEN DIFFERENT DATA
GENERATION PROJECTS TO MAKE SURE
WHEN, LIKE, ONE PROJECT IS USING
A PARTICULAR DATA TYPE THAT IT
IS FORMATTED IN THE SAME WAY AS
WHEN ANOTHER DATA GENERATION
PROJECT IS USING THAT DATA TYPE.
SO THAT ULTIMATELY IN THE END,
YOU COULD EVEN JUST COMBINE ALL
THE FILES TOGETHER AND ANALYZE
THEM IN THE SAME WAY.
THAT ALSO SORT OF LEADS TO SOME
OF THE EFFORTS THAT WE HAVE,
THAT WE ARE A PART OF AT THE
COMMON FUND LEVEL.
THERE IS SOMETHING KNOWN AS THE
COMMON FUND DATA ECOSYSTEM.
WE ARE, YOU KNOW, WE'VE SPENT
THE FIRST COUPLE OF YEARS OF THE
PROJECT GETTING OUR DATASET
READY AND STANDARDIZED AND
HARMONIZED AND NOW IT IS SORT OF
THE NEXT STEP TO BUILD THAT
HARMONY AT A HIGHER LEVEL.
SO THAT INVOLVES WORKING WITH
THE CFDE TO TRY AND MAKE SURE
THAT THE DATA ELEMENTS THAT WE
HAVE MATCH THE META DATA THEY
HAVE IN THEIR SORT OF ECOSYSTEM.
AND THEN THERE ARE TALKS WITH
THE ALL OF US PROGRAM AS WELL TO
MAKE SURE THAT OUR DATA IS
INTERCHANGEABLE WITH, YOU KNOW,
SOME OF THE DATA THAT IS BEING
COLLECTED IN THE ALL OF US
PROGRAM.
SO I HOPE THAT PART OF THIS BLUE
PRINT THAT PEOPLE TAKE HOME WHEN
THEY ARE BUILDING DATASETS OR
THINKING ABOUT SHARING DATASETS
INCLUDES THIS IDEA THAT IT NEEDS
TO BE, THE DATA NEEDS TO BE IN
SOME SORT OF STANDARD THAT OTHER
PEOPLE ARE VERY FAMILIAR WITH
THAT IS EASY TO USE AND IS OPEN.
SO THAT IT CAN SORT OF BE
HARMONIZED AT A MUCH, MUCH
HIGHER LEVEL THAN A SINGLE
DATASET.
>> I'M GOING TO JUMP IN HERE
JUST FOR A SECOND BEFORE WE COME
BACK TO YOU DAVID FOR A FOLLOW
UP QUESTION.
I'M GOING TO REMIND OUR
VIDEOCAST AUDIENCE THAT THE
FEEDBACK FORM LINK IS RIGHT
BELOW THE SCREEN YOU ARE
VIEWING.
YOU ARE WELCOME TO SEND IN
QUESTIONS.
AND I'M HOPING, YOU KNOW, SOME
FOLKS ARE FURIOUSLY TYPING AND
THE NCCIH EVENTS ON E-MAIL IS
AVAILABLE TO SEND E-MAILS THERE
SEPARATELY.
MAKE SURE TO AVAIL YOURSELF OF
THAT.
SO DAVID, DID YOU HAVE A FOLLOW
UP QUESTION?
>> I HAVE SOME OTHER QUESTIONS,
BUT I WANT TO OPEN TO THE
AUDIENCE.
I WILL COME BACK TO AARON IF
TIME ALLOWS.
THANK YOU SO MUCH FOR ENGAGING
ABOUT A FUTURISTIC TALK ABOUT
WHERE WE CAN GO WITH BIG DATA
AND MACHINE LEARNING.
>> HELENE, WHAT QUESTIONS DO YOU
HAVE?
>> YOU GAVE US A KIND OF AN
OPENING VIEW OF WHAT IT IS LIKE
TO ANALYZE THESE BIG DATASETS,
VERY, VERY EXCITING.
I HAVE A QUESTION ABOUT THE
EXAMPLE THAT YOU USED AT THE
BEGINNING ABOUT HOW YOU WERE
ABLE TO GET THIS VERY HIGHLY
ACCURATE ESSENTIALLY DIAGNOSIS
OF MACULAR DEGENERATION BASED ON
A SIMPLE SET OF TOMS THERE --
TOMOGRAPHY.
BASED ON HUMANS.
>> RIGHT.
>> PRESUMABLY SOME OTHER TYPES
OF DATA.
SO MY QUESTION THEN IS WHAT IF
YOUR DEEP LEARNING MODEL
DISCOVERS SOMETHING COMPLETELY
NEW THAT WE DON'T KNOW?
WHAT IS THE PROCESS BY WHICH YOU
WOULD BE ABLE TO BRING THIS BACK
TO ESSENTIALLY TO TEST IT?
AND THIS WOULD APPLY, I SUPPOSE,
TO THIS SALUTOGENESIS BECAUSE WE
DON'T HAVE OTHER WAYS, RIGHT, TO
UNDERSTAND THE MECHANISMS OF
SALUTOGENESIS.
SO SAYING YOU FIND SOME VERY,
VERY INTERESTING NEW MANIFOLD
THAT SEEMS TO DESCRIBE
INDIVIDUALS WHO ARE GOING BACK
TO HEALTH, WHAT WOULD BE YOUR
NEXT STEP TO VERIFY THAT THAT IS
ACTUALLY THE CASE?
>> YEAH.
SO THAT IS A REALLY GREAT
QUESTION AND AN INSIGHTFUL ONE.
SO LET ME SORT OF BOIL DOWN THAT
QUESTION INTO A COUPLE OF
DIFFERENT CONCEPTS.
ONE IS THAT TO YOUR POINT, IF WE
TRAIN A DEEP LEARNING MODEL
USING HUMAN LABELS, SO LIKE IN
THIS PARTICULAR EXAMPLE IT WAS
NORMAL VERSUS MACULAR
DEGENERATION.
THE MODEL IS GOING TO
RECAPITULATE OUR CLINICAL
UNDERSTANDING, RIGHT, BECAUSE
THAT IS LITERALLY WHAT IT IS
BEING TRAINED TO DO.
IT IS GIVEN THE SAME LABELS
DERIVED FROM A HUMAN
UNDERSTANDING OF DISEASE.
SO THERE IS A ELEMENT TO WHAT
THOSE MODELS WILL DISCOVER.
AND THE WAY THAT WE VERIFIED
THAT IN THAT PARTICULAR EXAMPLE
IS USING THIS SORT OF
OCCLUSION-BASED TECHNIQUE WHERE
WE HID DIFFERENT PARTS OF THE
IMAGE AND LOOKED TO SEE WHAT
HAPPENED TO THE MODEL CONFIDENCE
IN CALLING THAT MACULAR
DEGENERATION AND WE COULD BUILD
SORT OF THAT HEAT MAP.
THAT TECHNIQUE AND THAT
VERIFICATION METHOD WORKS WHEN
YOU ARE WITHIN THE CONTEXT OF
SORT OF KNOWN DISEASES AND KNOWN
MODEL BEHAVIORS.
SO THERE IS A WHOLE CLASS OF
DEEP LEARNING WHERE THE GOAL
THERE IS TO REALLY TRY AND DO
WHAT HUMAN BEINGS DO, BUT
AUTOMATE IT.
SO THAT IS SORT OF THE WAY THAT
YOU WOULD VERIFY THAT, IS BY
CHECKING IT AGAINST HUMAN
BEHAVIOR.
NOW, THERE IS ANOTHER WAY TO USE
DEEP LEARNING METHODS AND THAT
IS FOR DISCOVERY.
THAT IS WHERE WE ARE TRYING TO
PUSH THE BOUNDS OF HUMAN
KNOWLEDGE.
ONE OF THE WAYS WE CAN DO THAT
IS BY USING LABELS OR WHAT WE
USE TO TRAIN THESE MODELS, THAT
INFORMATION NEEDS TO COME FROM
SOME SORT OF DATA ELEMENTS THAT
IS SORT OF IRREFUTABLE AND IS
MUCH MORE TRUE THAN SORT OF
HUMAN UNDERSTANDING.
AND SO LET ME SORT OF GIVE YOU
AN EXAMPLE OF THAT.
SO IMAGINE YOU GOT AN EKG
TRACING OF SOMEBODY AND YOU
KNEW, YOU HAD THE DATA THAT
WITHIN 24 HOURS THAT PERSON HAD
A CARDIAC EVENT.
YOU -- HUMAN BEINGS MIGHT NOT BE
ABLE TO LOOK AT AN EKG AND BE
ABLE TO PREDICT WHETHER THAT
PERSON IS GOING TO HAVE A
CARDIAC EVENT IN 24 TO 48 HOURS.
THERE MIGHT BE SOME SUBTLE
CLUES.
THERE MAY NOT BE A FUNDAMENTAL
SORT OF WAY TO ANALYZE THE EKGs
WHERE WE CAN ACHIEVE A 98%
SENSITIVITY AND SPECIFICITY
AROUND THAT END POINT.
YOU CAN USE THAT AS THE GROUND
TRUTH TO TRAIN THESE MODELS.
AND THE MODELS WILL LEARN
DIRECTLY FROM THE DATA AND TRY
TO LEARN TO DO THAT TASK.
IT MAY BE ABLE TO DO THINGS THAT
HUMAN BEINGS ARE NOT ABLE TO DO
AND THEN LATER GO BACK AND TEACH
YOU THIS PART OF THE EKG IS WHAT
I USED TO BE ABLE TO MAKE THIS
DIAGNOSIS.
AND SO THAT'S A CLASS OF SORT OF
DEEP LEARNING METHODS AND
STRATEGIES YOU CAN USE TO
DISCOVER NEW THINGS ABOUT HEALTH
AND DISEASE PROCESSES.
IF WE WERE -- IF THIS DATASET IS
INDEED SUCCESSFUL AND IT DOES
LEAD TO SOME SORT OF BIOMARKER
THAT IS PREDICTIVE OF
SALUTOGENESIS, THE WAY YOU WOULD
GO AN VERIFY THOSE SORT OF NEW
NOVEL BIOMARKERS WHERE THEY GO
BEYOND HUMAN UNDERSTANDING IS TO
DO SORT OF A PROSPECTIVE
EVALUATION.
WHERE YOU TAKE THIS MODEL AND
YOU DEPLOY IT.
ALL THE PARTICIPANTS THAT ARE
COMING IN THROUGH THE DOOR
EITHER AT A HEALTH CLINIC OR
HOSPITAL SYSTEM AND IN THE
BACKGROUND YOU ARE RUNNING THIS
MODEL TO SEE IF, INDEED, THERE
ARE PEOPLE THAT HAVE THIS
BIOMARKER THAT IS PREDICTIVE OF
SOMEBODY TURNING BACK TO HEALTHY
STATE.
THEN YOU FOLLOW THEM INTO THE
FUTURE AND YOU SEE WHAT HAPPENS
TO THEM OR YOU CALL THEM BACK
AND IDENTIFY THEM AND DO A
CLINICAL STUDY WHERE YOU COLLECT
RELEVANT VARIABLES.
THEN YOU DO A VALIDATION STUDY
TO SHOW, INDEED, IN THIS GROUP
OF 50% OF THE COHORT POSITIVE
FOR THIS BIOMARKER, THIS WAS THE
SPECIFICITY OF THOSE PEOPLE
GOING ON TO A HEALTHY STATE AND
THOSE PEOPLE BIOMARKER NEGATIVE
WENT ON TO CONTINUE DOWN THE
PATHOGENESIS PATHWAY.
YOU WOULD HAVE TO DO A STUDY
LIKE THAT AT THE END OF THE DAY
TO CONFIRM THAT THESE BIOMARKERS
ARE, INDEED, USEFUL AND
CORRELATE WITH A MEANINGFUL
CLINICAL OUTCOME.
>> THANK YOU.
IN THIS CASE, THE GROUND TRUTH
WOULD END UP BEING TIME.
>> YES.
>> AS OPPOSED TO PSEUDOTIME.
>> RIGHT, RIGHT, RIGHT.
ABSOLUTELY.
SO ONE OF THE THINGS THAT I WANT
TO SORT OF EMPHASIZE IS THAT
BECAUSE WE ARE WORKING IN THE
SPACE WHERE WE DON'T KNOW WHAT
WE ARE LOOKING FOR, IT IS
IMPORTANT THAT THIS DATASET BE
DESIGNED FOR DISCOVERY AND FOR
UNDERSTANDING DISEASE MECHANISMS
AND UNDERSTANDING NEW WAYS OF
SALUTOGENESIS AND PATHOGENESIS.
ABSOLUTELY, IF YOU ARE PLANNING
TO VALIDATE ANYTHING YOU FIND
USING THIS DATASET YOU WILL NEED
TO DO A SEPARATE STUDY THAT
LOOKS AT THIS IDEALLY IN A
PROSPECTIVE FASHION.
>> THANK YOU.
THAT'S A VERY NICE WAY TO
EXPLAIN IT.
>> THAT'S EXCELLENT.
THANK YOU, DR. LEE.
DR. EDWARDS, DID YOU HAVE A
QUESTION FOR DR. LEE?
THANK YOU.
THE DIRECTOR OF OUR DIVISION OF
EXTERNAL RESEARCH FOR OUR
VIEWERS.
>> THANK YOU SO MUCH, DR. LEE,
FOR A WONDERFUL PRESENTATION.
OF COURSE, IT LOOKS SO SIMPLE
FROM YOUR PRESENTATION, BUT I
KNOW THAT IS NOT THE CASE.
SO MY QUESTION TO YOU IS WHAT
HAS BEEN YOUR GREATEST CHALLENGE
TO PUT THIS WONDERFUL DATASET
TOGETHER?
I KNOW YOU HAVE A WONDERFUL
TEAM, BUT COULD YOU SHARE, I
MEAN, FOR PEOPLE THAT MIGHT BE
INTERESTED IN FOLLOWING THAT
PATH, WHAT ARE SOME OF THE
CHALLENGES THAT YOU'VE
ENCOUNTERED THAT YOU ARE WILLING
TO SHARE?
>> YEAH.
SO ONE OF THE THINGS THAT WE ARE
TRYING VERY HARD TO DO IS TO DO
THIS THE QUOTE-UNQUOTE RIGHT
WAY.
AND WHAT I MEAN BY THAT IS, YOU
KNOW, WE ARE NOT USING
CONVENIENCE SAMPLING WHEN WE ARE
TRYING TO GET THIS 4,000 PEOPLE.
WE ARE NOT INVITING EVERYBODY
WHO STEPS THROUGH THE DOOR AND
ASKING THEM TO COME.
INSTEAD, WHAT WE'VE DONE IS
WE'VE PROBED THESE THREE MEDICAL
HEALTH CARE SYSTEMS AND PULLED
DOWN EVERY PATIENT THAT HAS BEEN
SEEN IN THE LAST FIVE YEARS.
AND WE LOOK AT THEIR DEMOGRAPHIC
PROFILE AND WE SEND THEM SORT OF
AN INVITATION LETTER THROUGH THE
MAIL OR E-MAIL TO INVITE THEM TO
BE PART OF THE STUDY.
THE REASON WE ARE DOING THIS IS
BECAUSE WE WANT TO TRY TO GET
RID OF THE HEALTHY VOLUNTEER
BIAS AS MUCH AS POSSIBLE.
BUT WE ARE SETTING OURSELVES UP
FOR AN UPHILL BATTLE FOR SURE.
THAT IS DEFINITELY THE HARDEST
THING THAT WE, AS A PROJECT,
HAVE BEEN DEALING WITH.
IS TRYING TO GET PEOPLE TO SAY
YES.
NOW, YOU KNOW, WHAT HAS BEEN
ENCOURAGING IS THAT WHEN WE LOOK
AT THE PERCENTAGES OF THE NUMBER
OF MALES, LETTERS WE HAVE SENT
THROUGH THE MALE AND THE NUMBER
OF PEOPLE RESPONDED YES AND
COMPLETED THIS 10-DAY PROTOCOL
OF WEARING THE SENSORS AND
HAVING THAT ENVIRONMENTAL SENSOR
AND ALL THIS BLOOD DRAWN.
THEY ULTIMATELY ENDS UP BEING
THAT THE PARTICIPANT RATE IS
AROUND, I THINK, 6% OR 7% OR SO.
AND EVEN THOUGH THAT SOUNDS LOW,
GIVEN THE NUMBER OF PEOPLE WE
HAVE IN OUR THREE COMBINED
HEALTH CARE SYSTEMS, WE ARE
ACCOUNT -- CONFIDENT WE WILL GET
TO THE 4,000 WE NEED.
TO ANSWER YOUR QUESTION THE
HARDEST THING WE ARE FACING IS
FINDING THE PEOPLE WHO ARE
WILLING TO GO THROUGH THIS
REALLY BURDENSOME PROTOCOL AND
BE GRACIOUS ENOUGH TO SHARE
THEIR DATA FOR THE SCIENTIFIC
WORLD TO LEARN FROM.
>> THANK YOU.
>> WELL, WE DO HAVE A COUPLE OF
QUESTIONS FROM THE AUDIENCE.
WE HAVE THE FIRST ONE HERE, DR.
LEE.
DO YOU EVER ENVISION USING
DATASETS COMPRISED OF HEALTHY
PEOPLE?
CAN AI BE USED TO STUDY HEALTH
INSTEAD OF ONLY STUDYING
DISEASE?
FOR EXAMPLE, OBVIOUSLY, YOU ARE
STUDYING DIABETES OR MACULAR
DEGENERATION.
THE PERSON IS ASKING CAN IT BE
USED TO STUDY WHAT MOTIVATES
PEOPLE TO MAKE HEALTHY LIFESTYLE
BEHAVIOR CHANGES?
>> YEAH.
SO ONE THING THAT I WANT TO
STRESS IS THAT WE ARE IN OUR
DATASET COLLECTING EVEN NUMBERS
OF PEOPLE WHO ARE HEALTHY VERSUS
DISEASED.
SO ONE OF THE BIG PROBLEMS WITH
DATASETS IN GENERAL IS THAT THEY
TEND TO BE EITHER ENRICHED FOR
VERY HEAVILY ENRICHED FOR
DISEASE OR VERY HEAVILY ENRICHED
FOR NORMAL.
I WILL KIND OF GIVE A COUPLE OF
EXAMPLES FOR THAT.
A DATASET THAT YOU MAY BE
FAMILIAR WITH IS THE UK BIOBANK.
IT IS A VERY LARGE DATASET
COLLECTED IN THE UNITED KINGDOM.
IT IS VERY SUCCESSFUL AND THEY
HAVE COLLECTED ABOUT HALF A
MILLION PEOPLE IN THAT DATASET.
NOW THE PROBLEM IS THAT THEY ARE
ALL VERY, VERY HEALTHY.
AND SO, IN FACT, LIKE COMMON
DISEASES ARE ACTUALLY FAIRLY
UNCOMMON IN THE UK BIOBANK.
SO THAT MAKES IT VERY
CHALLENGING TO ACTUALLY
UNDERSTAND HOW, IF SOMEBODY
STARTS OUT BEING EXTRAORDINARILY
HEALTHY, THERE IS NOT ROOM FOR
THEM TO BECOME HEALTHIER.
THERE IS SORT OF A CEILING
EFFECT.
YOU WANT A SPREAD, A SPECTRUM OF
PEOPLE, ALONG, IDEALLY ALONG
THAT DISEASE AX IS.
THAT IS WHY IN OUR DATASET WE
HAVE 1,000 PEOPLE THAT HAVE NO
DIABETES.
THEY ARE NOT EVEN PREDIABETES.
THERE IS ABSOLUTELY, THEY
BELIEVE THEY ARE LIVING A
HEALTHY LIFESTYLE AND THEY DO
NOT HAVE ANY DIABETES.
AND THEN THERE'S 1,000 OF THE
PEOPLE, INDEED, PREDIABETIC AND
ARE NOT STARTED ON MEDICATIONS
YET.
A THOUSAND OF THE FOLKS ARE
PEOPLE ON DRUGS TO TRY AND
CONTROL THEIR DIABETES AND THE
LAST GROUP IS PEOPLE WHO REQUIRE
INSULIN TO SORT OF STAY ALIVE
WITH THEIR TYPE 2 DIABETES.
SO WE ARE TRYING TO SAMPLE EVEN
NUMBERS OF THOSE FOUR DIFFERENT
GROUPS AND TO CREATE THIS SPREAD
OF PEOPLE SO THAT YOU CAN STUDY
THINGS LIKE WHAT KINDS OF
LIFESTYLE CHANGES MIGHT MOTIVATE
PEOPLE TO BECOME HEALTHIER.
>> THANK YOU SO MUCH.
AND THE SECOND QUESTION WE
RECEIVED FROM AN AUDIENCE
MEMBER, THE AUDIENCE MEMBER SAYS
I THOUGHT THE MACHINE LEARNING
MODELS ARE LIKE BLACK BOXES AND
CANNOT REVEAL HOW THE DATA IS
ANALYZED.
CAN YOU PLEASE NAME SOME MODELS
YOU ARE ABLE TO SHARE REGARDING
HOW THE DATA WAS ANALYZED TO
FINDINGS.
ECG BIOMARKERS AND THE PERSON
SAYS THANK YOU FOR A WONDERFUL
TALK.
>> YEAH.
I'M REGRETTING CUTTING -- THERE
IS AN EARLIER VERSION OF THIS
SLIDE FIVE HOURS AGO WHERE I HAD
FIVE SLIDES SHOWING THIS
VISUALIZATION TECHNIQUE.
I CUT THAT OUT.
I ASSUMED THIS AUDIENCE MAY NOT
BE INTERESTED IN IMAGE ANALYSIS
TECHNIQUES.
I'M REGRETTING THAT IN THIS
MOMENT.
IT WOULD HAVE BEEN REALLY
HELPFUL TO ELUCIDATE HOW THIS
WORKS.
THE PERSON ASKING THIS QUESTION
IS ABSOLUTELY CORRECT.
DEEP LEARNING MODELS AND COMPLEX
MACHINE LEARNING MODELS HAVE A
PROBLEM WITH INTERPRETABILITY OR
EXPLAINABILITY.
YOU CAN'T FULLY UNDERSTAND HOW A
MODEL IS ABLE TO MAKE ITS
DECISION OR COME TO ITS FINAL
CONCLUSIONS AND YOU CAN'T REALLY
TAKE IT APART AND FOLLOW EVERY
PART OF THAT LOGIC PATTERN
BECAUSE IT IS SO COMPLEX.
IT IS THE REASON WHY THEY ARE SO
POWERFUL AND ABLE TO DO COMPLEX
THINGS COME FROM THAT HIGH --
SPACE THEY WORK IN.
THERE IS AN INHERENT PROBLEM
WITH UNDERSTANDING 100% OF HOW A
MODEL IS ABLE TO DO ANYTHING.
NOW THERE ARE A CLASS OF
POSTTALK METHODS THAT CAN BE
USED TO PROBE HOW THE MODEL IS
MAKING ITS DECISION.
IN THE SPACE OF IMAGE ANALYSIS,
ONE OF THE EXAMPLES WE USED IN
THE FIRST PART I GAVE IS
COVERING UP A SMALL PART OF THAT
IMAGE AND PASSING IT THROUGH THE
MODEL AND SEE HOW MUCH COVERING
UP THAT PART OF THE IMAGE
INFLUENCED THE MODEL'S ABILITY
TO MAKE ITS DETERMINATION.
IF YOU COVER UP A CRITICAL PART
OF THAT IMAGE THE MODEL WILL
FAIL IN BEING ABLE TO TELL IF IT
IS NORMAL VERSUS MACULAR
DEGENERATION.
AND THAT IS HOW YOU CAN TELL
THAT PART OF THE IMAGE IS
REALLY, REALLY IMPORTANT FOR THE
MODEL TO DO THAT.
THE OTHER EXAMPLE THAT I CAN
GIVE YOU IS THAT A BEAUTIFUL
VIDEO THAT I STOLE FROM FACEBOOK
THAT HAD THE RUNNING DOG AND THE
HORSE.
AND THE MODEL WAS SORT OF
SHOWING WHERE IT WAS LOOKING.
SO THAT FAMILY OF DEEP LEARNING
MODELS ARE USED WITH SOMETHING
CALLED ATTENTION AND THEY HAVE
THESE DIFFERENT ATTENTION HEADS
THAT YOU CAN PROBE DIRECTLY TO
UNDERSTAND WHAT PART OF THE
IMAGE IS BEING USED.
AND THOSE VIDEOS ARE ACTUALLY
BEING COLORED DIRECTLY FROM THE
ATTENTION HEADS THAT ARE PART OF
THAT MODEL THAT WAS TRAINED IN
THE DINO FASHION.
SO, YES, IT IS ABSOLUTELY TRUE
THAT WE CAN'T UNDERSTAND HOW
THESE MODELS WORK AND WE CAN'T
UNDERSTAND HOW IT IS MAKING A
PARTICULAR DECISION, BUT THERE
IS SOME HOPE OF SOME LEVEL OF
EXPLAINABILITY WHERE WE CAN GET
HINTS OUT FOR HOW THE MODEL IS
ABLE TO DO ITS TASK.
IF YOU SCALE THAT UP TO A LARGE
NUMBER OF EXAMPLES THAT ARE
CONSISTENT AND BEHAVES IN A
CONSISTENT FASHION, YOU CAN
START TO DISCOVER NEW BIOMARKERS
FROM THESE MODELS.
>> THANK YOU SO MUCH.
DAVID, DID YOU HAVE ANY
FOLLOW-UP QUESTION FROM EARLIER,
ANYTHING THAT YOU WANTED TO
CIRCLE BACK TO BEFORE WE START
TO CONCLUDE?
IS
>> YEAH.
YOU HAVE TOUCHED A LOT OF ISSUES
I HAVE QUESTIONS ABOUT.
ONE ISSUE IS YOU SORT OF
MENTIONED AND HE HELENE MENTION.
I'M WONDERING IF YOU CAN GIVE US
A SENSE OF HOW YOU SEE THE
FUTURE IN TERMS OF -- I ALWAYS
FEEL LIKE WHAT WE ARE DOING WHEN
WE TREAT DOES IT IS REACTIVE
RATHER THAN PROACTIVE.
I THINK THE CONTINUUM YOU SHOWED
FROM HEALTH TO DISEASE AND THE
WAY MACHINE LEARNING CAN GET
DEEPER IN FINDING PATTERNS THAT
MAY BE LESS -- HUMANS ARE LESS
ABLE TO PIECE TOGETHER.
ARE THERE WAYS YOU SEE HOW THIS
REALLY COULD REVOLUTIONIZE
HEALTH CARE?
I MEAN, CAN YOU TALK ABOUT MAYBE
HOW WE SHIFT THE MODEL, A REAL
PARADIGM SHIFT IN HEALTH CARE,
HOW THIS DATA MIGHT ADDRESS SOME
OF THE REAL NEEDS IN HEALTH
CARE?
COULD YOU JUST SPEAK TO THAT?
>> YEAH.
SO I THINK THESE FAMILY OF
ANALYSIS TOOLS CAN REALLY BE
USED TO SORT OF REENGINEER THE
WAY THAT PEOPLE HAVE DONE
SCIENTIFIC DISCOVERY.
WHAT I MEAN BY THAT IS USUALLY
THERE IS SOME SORT OF
OBSERVATION AND PEOPLE
DEVELOPMENT HIYPOTHESIS AND PROE
THE HYPOTHESIS AND THAT IS THE
DISCOVERY.
SOMETIMES THOSE OBSERVATIONS
REQUIRE HUMAN BEINGS BEING
LOCKED UP IN A ROOM WITH EXPERTS
ALL COMING TO A CONSENSUS ABOUT
SOMETHING OR BREAKING DOWN THE
DISEASE INTO DIFFERENT STAGES.
THAT IS ANOTHER EXAMPLE OF THAT.
AND I KIND OF VIEW THAT THERE
MIGHT BE A HOPE WHERE THESE
KINDS OF DATASETS AND THESE
TYPES OF METHODS COULD BE USED
TO GIVE US A NEW WAY TO DISCOVER
ASSOCIATIONS THAT ARE NOT BEING
SO BIASED BY HUMAN
UNDERSTANDING, RIGHT?
IF YOU LOOK IN PUB MED THERE ARE
CERTAIN PATHWAYS PROBED OVER AND
OVER AND OVER AGAIN.
THERE IS A WHOLE BODY OF
LITERATURE.
IN ACTUALITY THERE IS MANY, MANY
MORE BIOCHEMICAL PATHWAYS THAT
EXIST IN THE BODY AND THE
INTERACTION WITH THE ENVIRONMENT
IS MORE COMPLEX THAN HOW DOES
THIS ONE PROTEIN INTERACT WITH
THIS OTHER PROTEIN IN SOME SORT
OF BIOCHEMICAL PATHWAY.
THESE METHODS GIVE US A WAY TO
APPROACH DATA IN THE UNBIASED
FASHION, WITHOUT THE LENS OF THE
HUMANS DOING EXPERIMENTS AND
TRYING TO LOOK AT ASSOCIATIONS
AND PATH WAYS.
IT GIVES US A DE NOVO WAY TO
LOOK AT THIS DATASET WITHOUT
THOSE BIASES BUILT IN.
THAT CAN BE VERY POWERFUL AND
LEAD TO NEW DISCOVERIES THAT WE
WERE, AS A SPECIES, JUST NOT
THINKING ABOUT IN THAT
PARTICULAR MOMENT.
>> GREAT.
THANKS SO MUCH.
THANK YOU SO MUCH, DR. LEE.
I THINK AT THIS POINT IN TIME WE
ARE GOING TO BRING THESE TO
CONCLUSION.
I WANT TO THANK THE FOLKS WHO
SENT IN SOME QUESTIONS, AND
DAVID, HELENE AND EMMALINE.
DR. LEE, I APPRECIATE THE TALK
AND BREAKING DOWN THE WAYS
PEOPLE CAN START TO UNDERSTAND
AI AND MACHINE LEARNING AND DEEP
LEARNING AND I REALLY APPRECIATE
YOU TAKING THE TIME TO DO THAT.
I THINK OUR AUDIENCE WILL
DEFINITELY APPRECIATE IT AND
THOSE WHO COME BACK AND WATCH AT
A LATER DATE.
WITH THAT SAID, I WANT TO REMIND
EVERYBODY THIS WAS RECORDED.
IT WILL BE IN THE NIH VIDEOCAST
PAST EVENTS ARCHIVES WHERE YOU
CAN JOIN AGAIN.
I WANT TO THANK EVERYONE FOR
JOINING ON VIDEOCAST TO SEE THIS
GREAT PRESENTATION.
AND, OF COURSE, THANK YOU SO
MUCH DR. LEE FOR YOUR TIME
TODAY.
WITH THAT, I THINK WE CAN BRING
OUR DAY TO A CONCLUSION.
DAVID AND HELENE, ANY FINAL
THOUGHTS AS WE WRAP UP?
>> DAVID, ANY FINAL THOUGHTS?
>> I THINK YOU SAID IT ALL,
CATHERINE.
THANK YOU, DR. LEE.
AGAIN, I THINK YOU ARE SHOWING
US WHERE THE FUTURE IS AND I
APPRECIATE WHAT YOU ARE DOING
FOR THE PUBLIC HEALTH.
SO THANK YOU.
>> I THANK ALL OF YOU FOR GIVING
ME THE OPPORTUNITY TO SPEAK
TODAY.
IT IS A GREAT HONOR.
ANY FINAL THOUGHTS?
>> JUST TO SAY THANK YOU FOR A
WONDERFUL TALK AND I LOOK
FORWARD TO TALKING TO YOU A
LITTLE LATER.
>> SOUNDS GOOD.
>> I WANT TO CONGRATULATE YOU ON
WHAT REALLY IS I'M SURE A VERY
CHALLENGING PROJECT YOU HAVE
TAKEN ON FEARLESSLY.
WE WISH YOU THE BEST OF LUCK
WITH THE NEXT PHASE OF IT.
>> THANK YOU SO MUCH.
>> WELL, THANK YOU.
AND THANK YOU TO ALL OF OUR
VIEWERS AND WITH THAT I'M NOW
GOING TO CONCLUDE TODAY'S
PRESENTATION.
BYE-BYE, EVERYBODY.
HAVE A GREAT AFTERNOON.