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>> Desney Tan: Right. So let's go ahead and get started. It's my pleasure
to introduce Wilson To coming to us from UC Davis. Wilson's got an
interesting background. He's finishing up his Ph.D. in integrative
pathobiology, which I'll let him explain. It's fairly unusual for around
here.
But he has an impressive track record working in technology for healthcare,
engaging in competitions like the Imagine Cup that you guys are well familiar
with, doing the UCIT Ideas Competition and the Harvard Business Competition
getting grants from various agencies including the Gates Foundation to do
much of his work. So without further ado, all yours, Wilson.
>> Wilson To: Thank you. So first and foremost, I wanted to thank you all
for coming out today to listen to my talk.
Before I really begin, I wanted to give you a little bit of insight about my
background, as Desney talked about and a little kind of nontraditional
candidate. But my life can be really summarized in kind of three pictures.
Medicine, business, and technology. Stereotypical images, right. My entire
academic kind of background has been in the healthcare sciences bachelor's
was in biological sciences. Master's was in comparative pathology, and then
my Ph.D. has been in integrative pathobiology. So essentially I study
disease processes across different species whether it's a dog, horse human or
a piglet, for example.
And so it kind of lends itself to be a really, really cool kind of research
program and graduate program and studies in general mainly because I'll be
working on a flamingo one day and then a human the next day. It's really,
really cool in I can see diseases across different expressions of different
species.
On the business side of things, I've worked in a number of different
start-ups, kind of just on my own launching different things. Along with
some of my colleagues, where we're doing kind of creating businesses around
healthcare systems and healthcare technologies in general, how can we
introduce and create technologies from the bench top and really translate it
to the bedside for patients and on the technology side, not much of a coder,
but I do things on a very, very high level and understand technology and the
potential impact of technology in different fields, particularly with regards
to healthcare in general.
So that's a little bit about me. A lot of the research that I've focused on
has been in healthcare in general.
When you look at healthcare and how it's developed over the past decade or
even century, we see a lot of kind of changes that occur. So starting from
1901 birth of medicine, going to the design of basic medical tools to the
revolution in imaging technologies and ever since then there really hasn't
been too much else going on.
The latest real technology revolution, like I said, was an imaging
technologies, the introduction of the PET scan CAT scan, MRIs, things like
that. And it really kind of lends us a question of what's next. Here we
have the introduction of things like the EKG monitor or the stethoscope. And
now we are on the kind of the cusp of mobile technologies. How can we
utilize mobile technology in healthcare? And so I'll be describing a little
bit about the healthcare space in general, primarily how it's focused in the
vascular disease area, because that's one of my areas of focus. So vascular
disease as we all know is one of the major problems globally in terms of
global health.
It affects five out of the eight UN Millennium Development goals whether it's
in poverty or maternal diseases or childhood deaths. It touches the lives of
billions of individuals worldwide.
Secondly, in some countries 40 percent of healthcare resources are used for
treating these diseases. That's a substantial burden in terms of
socioeconomic burden for these countries.
And it's ridiculously high, as you can obviously see. And the thing is costs
are expected to substantially increase year after year after year due to
these diseases.
And even so much that vascular diseases affect more than an eighth of the
world's population. And so there's a huge need to address this problem. And
so we need to introduce new technologies in order to address these problems.
And so one of the big ones that I wanted to focus on was diabetes. Pretty
much everyone in this room knows someone or is friends with someone or
friends of friends of someone who has been affected by diabetes.
When we look at the numbers themselves it's pretty staggering, 25.8 million
Americans are diagnosed with diabetes this year or last year. And that's a
substantial increase from just a decade ago with only 18 million individuals
in the United States.
Imagine how these numbers grow globally. And the directly related results
from these diseases really stem from any sort of cardiovascular complications
which result in 233,000 deaths per year. But when we look at kind of the
disease, it's not just a medical kind of problem. It extends beyond that
scope of medicine, and really touches upon socioeconomic problems.
So when we look at the indirect and direct costs of just diabetes in itself,
billions of dollars. $58 billion in indirect costs, $116 billion in direct
costs. And these problems are only going to start ballooning.
And so we really have the need to introduce new technologies in this field to
really address not only the medical side but also the socioeconomic side of
things.
And so the problem with diabetes isn't that there aren't enough methods or
tests to really test for the disease or in that there are not effective
treatments for the disease. In fact, they're actually pretty pretty good.
And a lot of cases where they're actually treating the patients it works
very, very well.
The problem really lies in that the on set of Type II diabetes may occur as
much as nine to 12 years before clinical diagnosis. And it's something that
I've explained to some of the researchers here where the patient has
developing disease at day zero but up to nine to 12 years it can kind of be
asymptomatic during that entire period. And throughout that course there's
been extensive damage that occurs in the body not only in the eye but extends
further past systemically to all the systems internally.
And so when we look at the end stage complications of diabetes, one of the
ones that really stands out is diabetic retinopathy, really patients are
often diagnosed with diabetes only after the fact that they visited an
ophthalmologist because of vision impairment problems.
They go in for blurry vision, turns out they've had diabetes for nine to 12
years. That's where the problem lies. How do we have an early means of
diagnosing for diseases or screening for diseases in a nonevasive manner such
we can apply it and make it accessible to patients everywhere.
And so one of the projects that I've been working on is computer-assisted
intravital microscopy. I'll explain more about it. But what it entails is
more or less a noninvasive invivo real time imaging platform that allows for
objective assessment of the microcirculation in the vulva conjunctiva. We're
looking at the red blood vessels essentially in the white part of the eye.
And using that as a biomarker and a platform to kind of do much more than
just disease diagnostics, but really give an effective analysis of what's
happening in the body and throughout the body.
So using the system, we're actually able to do visualization of individual
blood cells and blood vessels in the eye. So as you can see here, patients
sitting down, kind of in one of the older models that we have of the system,
but we just have them rest on the chin rest and actually just start taping
and taking pictures of the eye.
As you can see, this is very noninvasive test that allows for a, kind of the
focus to be very, very clear in that we're using a macro lens-based kind of
system to take pictures in an easily accessible environment of the sensitive
micro vessels in the vulva conjunctiva and in turn are able to do an
objective analysis to really show and quantify what's going on in the body.
So just based on the number of changes that have been studied in terms of
microcirculation so far, here's a list of 15 that we generally look for in
terms of when we were doing analysis on the eye. Things like abnormal vessel
diameters or ischemia within the eye or micro aneurysms. These are peer
reviewed kind of tested markers by which a lot of scientists and researchers
in the field look at the microcirculation in order to do a lot of the
analysis on that part.
And so when we think about what's actually going on in the body, a lot of it
is dictated by something called Walstrey [phonetic] array or Walstrey stress.
If you imagine the microcirculation system or circulatory system in general,
you can probably kind of think of a plumbing system, for example. You have a
series of pipes and actual water flowing through those pipes, and that can be
applied to what we see in blood systems and blood circulatory systems. So
everything is dictated by how much stress is the water is exerting on to the
pipes. Similar to what blood is exerting on to the blood vessels. And
because of any changes in any of these kind of factors, we're able to see
changes in the microcirculation as the body adapts to those areas.
And so when we kind of look at it from a more I guess a different profile,
here's a side-view of the parabolic blood flow profile of blood going through
the system, and as you can see, it's only at the center of the lumen that
that's where the highest velocity is. Over across the sides due to the
parabolic nature we actually see blood dragging across the blood vessels
which is in turn causing additional stress on the system.
And so when we look at the results of these changes, we can actually see the
microcirculation actually adapting to a lot of the changes on there.
So here we have the microcirculation of a normal healthy individual.
can see, it's an even orally distribution of arterioles, venules and
capillaries.
As you
These are microvessels enlarged. Anyone who has known someone with diabetes
will see extensive changes. If you think of diabetes as a whole, it's an
increase in blood glucose in the body which is in turn increasing the blood
viscosity and changing the sheer stress and sheer rate that's going across
the blood vessels themselves.
And so when we look at the adaptations that the body has to undergo in order
to address those systemic changes and chronic changes we see changes like
this.
So this is within a patient with diabetes for about nine years. And so as
you can see extensive tortuosity in the system, micro aneurysms and hemo
scission [phonetic] leakage throughout the system.
So when we look at sickle cell disease, for example, we'll see similar
changes. Vascular diseases in themselves because of their nature are
actually easily able to be visualized in the system just by looking at the
conjunctival bed.
And so let me show you a quick video of what we're actually able to see in
the system. So as you can see, we can actually to individual blood cells
flowing through the system using the sort of technology and setup that we
have.
Let's see if this works. Perfect. And so what we're doing in our case is
essentially mapping the eye. A lot of the gold standards in terms of
diagnostic medicine is looking at the retina or the back lining of the eye.
What we want to do is actually image the surface vessels in the white part of
the eye. And so by extracting a lot of the frames from a lot of the video
sequence that we have we can recreate what the surfaces vessels look like and
actually do analyses of these. What we want to do in terms of our next steps
is really how do you use computer vision to actually look at some of the
different changes in the microvasculature in order to have a very effective
model for essentially screening for different diseases.
And so I'll be describing a little bit about one of the studies we worked on
in this case is really how do we position biomarkers that we've discovered as
a means to do early detection of vascular disease?
And so from time zero we have the healthy patient to the point of disease
onset to when symptoms actually emerge and they're clinically diagnosed.
There's this huge asymptomatic period that I described earlier.
And with that developing complications such as retinopathy and neuropathy,
nephropathy that I described earlier.
So what we want to do is actually have that point of diagnosis and treatment
actually shift over to the left where we're able to do that during that
asymptomatic period or previously asymptomatic period and really provide a
sort of means to look at and really detect diseases earlier.
And so here's some of the images from the study we've seen. So A is, again,
one of the normal healthy controls that we have. And then B, C, D, depending
on how long they've had disease, the disease really shows the extent and
severity of the disease, and we can see just based on our numbers that
patients with Type II diabetes has a higher severity index, the list of the
15 I showed you earlier, they have a significantly higher number than those
in the control subjects.
And so when we look at the retina, for example, which is the gold standard by
which a lot of the diagnostic platforms are done nowadays, diagnostic tests,
we actually start seeing very, very similar results. However, they usually
later on during that whole pathogenesis period of disease.
So when we look at kind of retinopathy levels, Type II diabetes patients
generally have around twice as likely to show indications of disease. And
this is the gold standard.
So this is the means by which ophthalmologists actually diagnose diseases.
So when we think about kind of the bigger picture, when we look at healthy
patients, we see a normal conjunctiva, normal retinal fungus. When you look
at the asymptomatic period, the retinal fundus still looks pretty normal but
we start seeing changes in the conjunctival microcirculation. When symptoms
emerge and we're actually able to see different changes in the retina, those
changes in the microcirculatory system and conjunctiva actually get worse,
and when they have full blown peripheral diabetic retinopathy, that's when
you see extensive damage happening in both conjunctival beds.
So when we're comparing the numbers in terms of the SI, severity index,
versus what's going on on the retinopathy levels, healthy, we see pretty
standard 0 and 1.33 on the severity index, and those numbers only get higher.
It's only when symptoms emerge that we start seeing changes in the
retinopathy levels, but we see extensive changes already happening in terms
of what's seen in the conjunctiva. As you can see, there's an upward trend
in terms of the pathogenesis of disease, from disease onset to when they
actually develop clinical symptoms, to when we're actually able to see these
diseases.
And so this sort of model isn't just applied to diabetes. But we can
actually apply it to a lot of different diseases as well. Whether it's
Alzheimer's disease or sickle cell disease or hypertension, we see similar
changes in those areas.
So when we look at the retinal fundus of a patient with hypertension, we see
extensive tortuosity in the system. We see a number of those actually
reflected in the conjunctival bed as well. However, when we look at it a
little closer, the ones in the conjunctival bed actually reflect a more
accurate and more sensitive bed to really predict if a patient has
hypertension in general.
So when we apply these in terms of looking at humans and/or humans or
animals, we can actually see very, very similar changes. So it doesn't
matter -- it doesn't matter what sort of species we're looking at it also
doesn't matter what age we're looking at. We've applied this sort of concept
to children as young as a year and eight months, I believe, to as old as 82.
And so this really has a very, very flexible platform by which we're actually
able to apply new technologies in the healthcare scheme to introduce a
noninvasive real time manner to diagnose and screen for different diseases.
Kind of more on the contact lens side, so this is an area that I've recently
gotten into where we're actually able to measure how contact lenses affect
the microcirculation and microcirculatory bed.
So essentially we're measuring and answering the question of whether
extraocular pressure from contact lenses affect the microcirculation.
Especially in cases where we're using hard contact lenses or corneal
refractive therapy where we're reshaping the corneal surface of the eye.
So as predicted, a lot of what we can actually see is reflected in the
microcirculation as well. So when we look at A and B, for example, we see
signs of tortuosity in healthy individuals, but only those tortuous vessels
can only be seen near the contact lens edge, not really anywhere else on the
surface vessels in the perilimbal region of the eye. So we can actually see
kind of over here a micro aneurysm actually start forming as the contact lens
is actually exerting force into the eye and actually creating different
occlusions.
And so we're actually able to validate whether or not contact lenses in
general are able to become a little better in terms of fit, in terms of
design materials and whether or not they're safe and effective in terms of
its use in patients, especially with the increased adoption of contact lenses
worldwide.
And so with CRT lenses we're actually able to see something very similar.
We're actually always seeing kind of occlusions happening throughout the
entire microcirculatory system within the confines of where the contact lens
edge is. So applying this model when we're looking at how contact lenses are
designed and how we're actually able to kind of predict whether or not this
will increase different risks of conjunctivitis or ceratitis or things like
that, we're actually able to see whether or not damage is actually happening
in the microcirculatory system.
And so that's essentially what computer-assisted intravital microscopy is and
how we've applied it into our lab. A lot of the lessons we've learned has
really a profound impact on what we're able to do in terms of medicine. And
so this is a noninvasive real time invivo tool where we're actually able to
screen patients for different vascular diseases or potentially screen them.
So if you think about the microcirculatory beds, if we look at a patient
today versus seeing them a year from now, we can actually use the patient's
microcirculation as a control within itself.
>>: Just wanted to clarify, it's still images that's taken not video?
>> Wilson To: Both. Yes. Sorry about that. So we're actually able to
compare patients as their own control and actually see how that blood vessel
changes over time. So if we have a patient with diabetes and we give them a
treatment we can actually revisit different sites and actually see whether
those blood vessels are actually getting smaller, thinner, less tortuous,
things like that.
So it really provides a platform where we're actually able to effectively
evaluate whether drugs are working.
>>: So the phenomenon you're observing is blood pressure is high for a long
time period of time and so that makes the vessels larger?
>> Wilson To: One factor is blood pressure. Another is viscosity, for
example, blood flow. We're actually looking also at kind of biochemical
markers. So if you look at how blood vessels are responding to different
vaso dilators in patients with vascular disease, they're essentially not
responding to those different things. And so what happens is the blood
vessel isn't able to dilate at all so you have increase in blood pressure.
So it kind of has this vicious cycle where it feeds on itself. That's why
patients with vascular disease often have a lot of chronic problems that
eventually lead to cardiovascular disease or heart attack just because of
those reasons.
>>: Does the same effect happen elsewhere in the body and you're using the
eye because it's particularly easy or does it only happen in the eye because
of its shape?
>> Wilson To: This can be a representation of everything in the body. So if
you think of the microcirculatory system in general, it goes from I guess
your arteries to your arterials to your capillaries to venules and veins.
This is a micro scale of what's happening.
That can be thought of what's happening systemically in the body. So the
reason why we're using the conjunctival bed in the vulva conjunctiva because
it's an easily accessible area.
The other two areas you can probably look at it is underneath the fingernail
and underneath the tongue as well because they expose a lot of different
blood vessels in those areas as well.
>>: What's the discriminatory power that you have looking at these blood
vessels to tell the difference between different conditions?
>> Wilson To: You know, so a lot of the abnormalities between the different
diseases actually overlap. But varies -- with different diseases, they have
very specific characteristics that can be seen. Sickle cell disease, for
example, you only see common signs in that sort of disease. And we're using
it more as a method for screening patients in order to undergo the test of
actual test rather than actually having them perform like a blood draw and
blood panel to go through pretty much the entire works of a blood glucose
test, for example.
Cool. Awesome. That's a little bit about the computer assisted intravital
microscopy, has a new way of looking at how technology can be applied in the
healthcare system healthcare in general. If you imagine what we're able to
do with this it's pretty substantial. Imagine going to your doctor or your
optometrist, for example, and being screened for different vascular diseases.
More and more optometrists are being positioned as gateway physicians where
they're actually able to look at what sort of changes are happening in the
retina and kind of referring patients to go see their primary care physician.
And the conjunctival bed offers another earlier way of having kind of
patients kind of get tested and screened for in that sort of sense.
And so as you can see, there's been extensive hardware evolution kind of
happening when I first started the project in my graduate program. So
essentially it was more or less prototyping different contraptions and seeing
what would work.
And we eventually moved on to different kind of platforms that are widely
available in kind of the mass commercial market and actually adapting
different slit lengths seen in ophthalmology and optometry clinics to see
whether that would be an applicable route to implement this sort of
technology.
And so another project that we've been working on is whether or not we're
doing this on a cell phone. And so this was the basis of one of the Imagine
Cup projects we worked on in the past where we're able to use a cell phone to
take pictures and video of the eye in order to see whether or not we're
actually able to do onboard analysis of different disease trends and that
sort of aspect.
>>: Are you going to say what you had to do with the hardware to make this?
>> Wilson To: Definitely. The whole full effect. Not a problem. So this
is a Windows 6.5 device. A little old. Back in the day before the Windows
Phone were invented really. And we were using the phone with a lens
attachment to it in order to look -- intra vital lens essentially where we're
actually able to position this over the eye and use the flash on the device
with a green filter to kind of enhance and contrast of the blood vessels and
actually image the kind of blood vessels in that sort of manner. It does
work.
What we're actually trying to do is continue our development of this hardware
evolution and really introducing this on a slate and kind of a tablet kind of
hardware fashion where we're actually combining this sort of technology and
interface with that in slit lengths and really provide a easy-to-use simple
tool for optometrists and ophthalmologists to kind of play around with and
see whether this has clinical applications in the real world.
And so that pretty much sums up what I've been doing in terms of my primary
research during my graduate program. And I'm going to go into a little bit
about another project that I worked on as part of the Imagine Cup.
So I don't know how many of you are familiar with the Imagine Cup, a little
bit? Awesome. Those who aren't familiar with it, the Imagine Cup is a
worldwide technology competition sponsored by Microsoft wherein students are
challenged to create new technologies to solve the world's toughest problems.
And so essentially this is how I sum it up. The Imagine Cup is where
extraordinary happens and change is possible. Chris is a good example of a
competitor, Tristin is another one. And so they've come up with really,
really robust kind of solutions to address poverty in the world or healthcare
in the world, healthcare challenges in the world.
And so one of the projects that I worked on with Tristin here actually was a
project called Life Lens. And essentially what we wanted to do was use the
Windows Phone as a means to do light microscopy. So when pathologists and
laboratory technicians look at blood samples, for example, they use those
really high powered microscopes.
How do we apply that technology and carry it over to a kind of like a
smartphone kind of format? So a lot of research has been done in this area
already. Cell phone-based platform for biomedical device development has
been kind of explored on extensively by a number of different researchers
across the world, where essentially we're using a cell phone with a ball lens
to look at different samples and really image and to samples in that sort of
manner.
So these are the images that we're actually able to see using this sort of
technique. So this is a kind of untouched photo of what's going on. If we
enhance the magnification do different analysis on it, we're actually able to
really create a very, very powerful platform by which we can do a lot of
healthcare analysis of bloodborne diseases.
And so just by taking a blood smear, a little finger prick really of patients
we're actually able to do a lot of visualization and analysis. Can't really
see it on that screen, but of the blood cells in the system and actually look
for diseases like malaria.
So this was kind of the basis and foundation for a lot of the projects that
Tristin and I worked with about a year and a half ago to really showcase the
power of technology when it's applied to healthcare.
And so imagine equipping healthcare workers in third world countries with no
access to hospitals or clinics nearby with point of care devices where
they're actually able to kind of do diagnostics on the spot.
And so one of the cool features about doing this on a mobile device is that
there's so many gismos and gadgets attached to it we can actually put
together a lot of the information to really perform more or less
epidemiological studies.
So using our application and the Windows Phone we're actually able to see
where outbreaks are happening kind of in real time and really provide this
information to teams that do preventive medicine preventive care, such as
things like as simple as mosquito nets, for example. How do we deploy
mosquito nets in areas where they're bound to have it within the next week or
something.
So combining this information with things like weather patterns or predictive
models about where, what sort of direction disease outbreaks are happening,
we can actually provide a new method of preventive care for a lot of
healthcare teams around the world.
And so our project won third place worldwide this past year, or I guess two
years ago. And we actually recently got a big chunk of the three or
$3 million Microsoft Magic Cup Grant to continue developing our project and
actually implementing them in various test studies around the world.
And so we're using this as a means of providing a new sort of inspiration and
product into the market where we're actually helping save lives using common
devices that we have in our pockets.
So one of the new projects we're looking on is something called pathologic
code which is still in its infancy right now. But essentially what we're
doing is protein folding on devices.
So how many of you are familiar with Fold It? Perfect. Awesome. So Fold It
is essentially for those who don't know protein folding as a game more or
less.
And so a lot of it is done on the computer right now, with kind of players
kind of pitching their ideas of how best to fold a protein in order to make a
specific kind of structure. And so we wanted to really create that and
really connect devices to explore that field.
Like I said, this is kind of still in its infancy right now. But software
isn't just meant for -- I guess software in itself isn't just the medium by
which we can address healthcare in technology but also gaming as well. So
that's one area of exploration that we're trying to work on developing right
now.
And so really to summarize what's going on in healthcare and technology in
the space is mobile technology is untethering healthcare and enabling the
practice of healthcare anywhere in the world.
That's pretty much what my whole foundation and belief is in terms of
research really is built upon. If you think about what's happening really
worldwide, people have more access to cell phones than they have fresh water,
fresh, clean water. It's mind blowing sometimes. And so really how do we
introduce mobile technologies in the space of healthcare in order to provide
new methods of diagnosing diseases, preventing diseases, kind of across the
entire spectrum.
And so one of the cool things about mobile phones is they're pretty crazy
nowadays. This is something that's really true. During one of my talks
before someone actually tweeted this to me. Your phone, your mobile phone
has more computing power than all of NASA in 1969. NASA launched a man to
the moon. Here we are launching birds into pig structures.
And when you think about that, it kind of says you about the potential of
technology in the mobile space for healthcare. And it's something that we
really wanted to be on the cutting edge of in order to help facilitate a lot
of the growth in that area to make sure it's kind of pointed in the right
direction.
And so when we think about how we're planning our projects, we should really
let our relationships kind of guide our strategy. We have a lot of people
invested in terms of healthcare and kind of the technology involved in
healthcare.
So it's not just research for research sake but we're doing research because
it aligns with our global passions for global health. We're equipping
communities with new tools and techniques to help them manage their whole
healthcare scenarios, working with team members and shareholders and really
kind of providing a means where it's not just a financial return that they're
kind of building towards, but really a deep human sense of return of how
we're actually able to contribute to the communities and really working with
our customers and partners to really think about what's the best approach and
how is it being used in the field.
And so really to summarize what we've been doing is really kind of innovative
screening techniques. I personally feel that in the area of pathology and
medicine, it's kind of the screening that's a problem. It's not the
treatment. It's not the diagnostic process. It's really how do we keep
pushing to the edge of when can we actually screen for these diseases to
provide that early intervention and early diagnostics.
So providing innovative screening tools to bridge connections in science and
technology to really provide a real impact for a better tomorrow. And so I'd
like to thank namely one person in this room here, Tristin Gebeau [phonetic]
who is working on the project with me for the past couple of years.
Dr. Anthony Chung, Peter Chen, Patricia Took, David Telander, UC Davis, and
Jason [inaudible] on Dove White [phonetic] and a few of my interns over at UC
Davis as well. And more than anything to the Bill and Melinda Gates
Foundation to provide the funding to me for my research and academic studies
throughout my bachelor's and master's and Ph.D. program. Thank you for your
time and open to questions.
[applause]
>>: How much expertise or precise positioning does it take to get the right
picture? I mean, you could imagine we're all wearing glasses that are
imaging our eyes all the time or something like that.
But the set up you showed had their heads down and all still.
>> Wilson To: Definitely. A lot of what we've been doing kind of on the
slit lamp side and the device, the reason we're using a chin rest is because
it provides the stability and lack of kind of motion for the patients. And
so it definitely helps to have a very, very calm patient.
But at the same time, we wanted to integrate a lot of video stabilization
algorithms into what we're looking at in order to kind of make sure that
we're seeing the images as they are rather than blurs everywhere. But a lot
of what we're doing is more or less if we follow the protocol that we've
developed, we're actually able to get enough information in two to three
minutes.
So it's pretty robust in that area.
>>: Hold still.
>> Wilson To: Basically try to hold still. We've done it in patients as
young as a year and eight months. And those guys don't sit so well. So
we're actually able to extract a lot of information in just a short amount of
time. Essentially when we're doing analysis we're only looking at a few
frames of the entire sequence all we need is that little bit. Yes?
>>: How do you define if you've got tons of frames, two or three minutes
worth of frames, how do you zero in on the ones that are important.
>> Wilson To: So we actually have a program that kind of sorts out what's a
good frame, what's a bad frame and we actually do analysis on all of them.
We actually have different investigators do analyses on them to make sure we
have a very accurate picture of what's going on in the body.
>>: That analysis offloaded to humans or is that automatic process?
>> Wilson To: Right now it's based off of humans. But the plan is how do we
automate this entire function, in order to use computer vision, which Tristin
is kind of helping developing, to really teach the computer what to look for
and how to identify those different features and markers to really kind of
push this technology such that you don't need training to do any of the
analysis at all. But right now it's completely done by individual
investigators in three separate locations to kind of verify that everyone's
getting the same numbers.
>>: Can you say a little bit more about the cell phone setup, the lens and
how you actually capture the video of the cell phone.
>> Wilson To: Definitely. I have a paper I can actually send to you if you
want more detailed information about it. But essentially we're using a intra
vital lens that's attached to kind of the back of a cell phone. Essentially
the cell phones nowadays have pretty much a very, very self sufficient,
self-contained kind of environment where we're actually able to do all sorts
of things to it. And so by attaching a lens to the back sensor of the image
sensor of the cell phone, we're actually able to see a lot of interesting
things with it.
I think the sensor or the lens that we're using before was actually a pretty
big, like a 3-inch 4-inch kind of lens that we are using to do a lot of intra
vital microscopy work in microscopes. We're adapting it to kind of
miniaturize it to do a more point of care kind of approach to everything.
I can definitely send you some additional information on it.
Yes?
>>: Have you investigated some of the more self-diagnosing technologies like
the blue field [inaudible] phenomenon?
>> Wilson To: I have not. But it's something that I can definitely look
into. Little curious about it, actually.
>>: For self-diagnosing scotomas in the retina.
>> Wilson To: Definitely have not looked into it more. A lot of the
research and the work I've been doing has been more on the conjunctival level
rather than on the retinal level, mainly because a lot of the vascular
diseases kind of are able to show themselves and manifest in the vasculature
a little earlier on in the microcirculation in the conjunctiva rather than
the retina. A lot of the work I've been looking at has been sort of in that
field.
Yes?
>>: Can I have you speculate on a technology wish list?
>> Wilson To:
Technology wish list.
>>: There's some things I think you see in here, things like requiring
stability for two or three minutes that could actually possibly be solved by
better technology.
>> Wilson To:
Definitely.
>>: Better imaging or better extraction techniques. Are there other things
you've seen in any of these projects where if we gave you the magic ball and
said you have three wishes on any technology you wanted what would be on the
top of your list or different sensors? Is the camera an acceptable sensor in
general to use?
>> Wilson To: Probably one of the biggest things that I think would kind of
help this project the most is having a bigger image sensor. The more
information that we can get from these sort of microcirculatory beds, it's
the more data that we can use to see what's going on. Even though we're
not -- we're able to see too much into it, we're actually able to see a lot.
So did you have a question really quick?
>>: I'm sorry. First of all, that I came in late. Stuff was really up my
alley but I got a meeting that got cancelled. So you were using RGB sensor
or [inaudible] sensor.
>> Wilson To: Those are RGB sensor but we're using a -- we had a few filters
set up on the cell phone itself, where we're using kind of a anti red filter
both in terms of what's on the flash system as well as the actual video
capture. We're doing kind of everything in monochrome to actually enhance a
lot of the images that we have.
>>: How would a large sensor have helped?
that appear in the conjunctiva?
I mean, because of the structures
>> Wilson To: So a lot of it, if you look at it from this sort of point of
view, if we have a bigger sensor, we're actually able to look at the
conjunctiva at a much closer level as well.
So depending on what sort of lens elements that we're using, combining with a
larger image sensor, we can actually capture a lot more information, a lot
more detail in that sort of aspect.
Going back to your question, the second wish would probably be I think access
to a lot of ability -- I guess, access to resources to kind of build
different casings and models to really simplify, like rather than having a
chin rest, for example, how do we build that into more of a platform even if
it's a forehead press or something.
Because I feel that a lot of what's happening in terms of developing the
technology and the solution is really limited by what we have in the lab.
And so it's more or less how do we duct tape stuff together and get things
working. And as cool as that may sound, like it provides a lot of
challenges. And so resources in that sort of area definitely help.
And then lastly it's just in terms of the sensors within mobile devices
themselves. Like from the point when we were using this device to even using
the Windows Phone now this substantial increases in our ability to do things
a lot quicker and more efficiently, but to be able to look at I guess
combining a lot of our projects together in terms of we're looking at the
microcirculation here, we're looking at blood here, and I mean there are apps
to really look at what sort of diets people are having or what sort of
lifestyle they have in terms of how much they're running or whatever it might
be, how do we combine that information together.
How do we create it open enough to where we can get a good picture of what an
individual is like in order to really provide very personalized solutions and
recommendations for that individual. And so those would be the three.
>> Wilson To:
Other questions?
>> Wilson To:
Yes.
>>: Could you potentially diagnose from a single frame?
>> Wilson To: Definitely not. Mainly because when we look at the
microcirculatory -- like one frame, for example, is a very, very tiny piece
of what's happening in the entire conjunctiva. So we want to make sure this
isn't just an instance where you just rub your eye in that area, there's
micro aneurysms or there's hemo [indiscernible] in that area because you're
rubbing your eye or because you've had micro trauma in that location. When
we look at the microcirculation as a whole we try to look at the entire white
part of the eye to get a good view of what's going on systemically.
So...generally know.
>>: The frame have the entire eye -- like you need the whole picture but you
don't need it over time, I think is what ->> Wilson To: No. Essentially what -- I don't know if you remember the
slide where it had red boxes of the different frames stitched together. So
that's essentially what we want to do. How do we just map out the entire eye
so we can take a look at the image itself and compare it over time.
If we look at that image in itself over the course of six months or three
months or a year, we can actually see how blood vessels are responding to
different treatments and kind of look at that from that approach.
>>: So there's no significant temporal profile that you're interested in,
movies or high speed photography or [inaudible] or fascia speeds could help
you with.
>> Wilson To: It's definitely something they've worked on in the past. I
know some of the research before the different models that I showed you here,
they actually use a lot of SLR-based systems to try to do a lot of high speed
photography. But it wasn't very, very powerful or robust in that sense
because what they did was take pictures, develop them, kind of measure it
from point to point to point to point and it wasn't a very efficient manner
to do it.
But it definitely has the potential to move into that field. If we're
actually able to create new kind of techniques and approaches using those
sort of systems. But it's definitely doable.
>>: Able to get pulse profile at least pulse velocity.
>> Wilson To:
Definitely.
>>: And filtering, too.
>> Wilson To: Yes. That's why I was very interested in what you were
talking about earlier. It was like wow, that's perfect. What I do.
>>: The feature, the single frame, what's the size of the smallest feature of
the camera?
>> Wilson To: So based on our converted ratio or conversion -- I think it's
12 microns we can look at. So and those are some of the features involved
with the microcirculation itself. We're essentially looking at blood cells.
We can actually see the blood cells going through in the system. We can look
at it from a tinier perspective, but it's essentially around 12 microns.
>>: Ophthalmologists, take tons of pictures, have tons of gadgets, how does
your differ from the types of pictures that they're normally taking?
>> Wilson To: Ophthalmologists look at the retina rather than the
conjunctiva. So they're looking at the blood vessels in the back lining of
the inside of the eye. And so that's where essentially light hits and you're
actually able to process images. And so what we're looking at is the surface
vessels. So it's a little easier to access and to kind of take pictures of
and it doesn't require any sort of injections or Flurosyn [phonetic] or
anything of that sense to see how fast blood is flowing through the system.
When you look at the retina versus the conjunctiva a lot of the symptoms that
appear in the retina are generally advanced stages of different diseases
already. So the conjunctiva serves as a more sensitive bed to the changes
that are happening in the body. You're more likely to detect something in
the conjunctiva than something you see in the retina. That's why we were
talking about a little bit of the study when we're comparing what's on the
fundus versus what's in the conjunctiva to show that timetable of
pathogenesis when someone is developing a disease.
>>: So this results in a new result?
[inaudible]
>> Wilson To: I'm only three years into the program. But, yeah, it's a
relatively new technique. The whole idea has been around in terms of looking
at the microcirculation has been around for about 15 years. And even more so
when we look at some of the other aspects of the bioengineering and
biochemical properties of the microcirculation.
But this is a relatively new field.
>>: So what is the adoption path of something like this?
>> Wilson To: So with this specific solution, we're looking to partner with
a lot of the healthcare providers and insurance companies. So our group
worked with and talked with some folks over at Walgreens in terms of their
executive team to see -- and just imagine having access to this at every
single corner in terms of having a drugstore nearby.
And working with doctors and optometrists to see whether we can get this as
part of your comprehensive examination. I mean, people go see their
optometrist every year. People go see their doctors every year. But what if
we're able to provide as many places for people to access this sort of
technology in order to get a good timetable and establish how well they're
doing in terms of their vasculature internally.
>>: I guess I ask what do you see as the main object to that, besides
politics and money getting to the [inaudible] store, are there technology
obstacles still there? I would guess the fact that you have to have humans
interpret images is a huge obstacle to making that dream a reality at this
point.
>> Wilson To: No, I completely agree. And that's something that we're
actually looking into, how do we introduce new computer vision algorithms to
kind of automate the entire process and specifically look for things. So
that's an area of research we're moving into a little more. So I definitely
agree with you on that. Kind of removing that whole human subjective kind of
analysis will greatly improve the objective nature of what we're doing.
>>: I would say it's not unreasonable to think like a trained
ophthalmologist, skip through video and maybe find some frames that are
indicative of ->>: A device for the doctor [inaudible].
>> Wilson To:
Excuse me.
>>: The device directly sends the video to the doctor, trained
ophthalmologist. So you don't need [inaudible].
>> Wilson To:
That's definitely a plausible scenario.
>>: Seems like the impact is greatly larger if you can [inaudible] doing it,
untrained person doing it versus a trained person.
>> Wilson To: Yes, definitely opens access and the ability to kind of do
instantaneous analysis. It definitely is the path that we want to move
towards. But it's probably a couple of years from now.
>>: How hard in particular would it be to make it completely machine-based
and not depend on [inaudible] these images that this resolution and this
frame rate, how effective is it to ->> Wilson To: It's definitely doable. It's something that we've already
started to look into. And out of those 15 we can probably look at probably
three or four of them with pretty good confidence that those were pretty
accurate numbers.
But we definitely want to take it a step at a time -- we want to maintain its
accuracy while expanding to new areas in terms of the list of 15. It's
definitely doable I feel because we're looking at pictures of pixels. How do
we teach computers what sort of pixels pictures relate to what sort of thing.
So as long as all the features are very consistent in terms of what they look
like and it is in the case for microcirculation, because my body, your body,
anyone's body kind of adapts in the exact same way. Different patterns but
the exact same way. So we can definitely look at it from that sort of view,
and I'm confident that it will continue moving forward in terms of developing
kind of a new way of looking at and automating the analysis of those images.
Great. Thank you.
[applause]