Behind the Scenes Episode 345: NetApp and Digital Pathology with Kim Garriott

Welcome to the Episode 345, part of the continuing series called “Behind the Scenes of the NetApp Tech ONTAP Podcast.”

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This week, Kim Garriott (kim.garriott@netapp.com) drops by to teach us all about digital pathology and how it is the next wave of change in the healthcare IT industry.

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Transcription

The following transcript was generated using Adobe Premiere’s speech to text service and then further edited. As it is AI generated, YMMV.

Episode 345: NetApp and Digital Pathology

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This week on the Tech ONTAP Podcast.

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Kim Garriott joins us to talk to us all
about digital pathology.

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[Tech ONTAP podcast intro]

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Hello
and welcome to the Tech ONTAP Podcast.

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My name is Justin Parisi.

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I’m here in the basement of my house

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and with me today – on the phone –
we have Kim Garriott.

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So Kim, what do you do here at NetApp?
How do we reach you?

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Hi, Justin.

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I am the Chief Innovation
Officer of our NetApp healthcare

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and life sciences practice and also
the general manager for medical imaging.

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And you can reach me
at kim.garriott@netapp.com.

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All right. Excellent.

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So Kim is here today.

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She’s been on the podcast
before to talk about

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healthcare and medical imaging
and that sort of thing.

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Today,
we’re going to talk about something new,

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something that’s
kind of cutting edge in the industry,

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and that is a concept known as digital
pathology.

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So, Kim,
could you please tell me what that is?

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Because I don’t know what that is.

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And I’m sure a lot of people
haven’t heard of this before

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other than, you know, what the words
mean, right?

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Yeah. No, exactly.

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It’s a really exciting field
that that is coming to life

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in healthcare and life sciences
and have some interaction with pathology.

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So any kind of tissue
removed from your body,

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say you had a gallbladder removed
or you went to the dermatologist

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because there’s a, you know,
a little mole or a bump on your arm.

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And they do a little biopsy sample
of that.

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That tissue or fluid from your body

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is set on a glass slide.

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And then that glass slide
is put under a microscope.

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And a medical professional – called
the pathologist – looks at the cells

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and the tissue on that slide
and makes a diagnosis.

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Most commonly that we can relate to
is whether that skin tissue

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is benign or malignant,
whether you have cancer

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or not, or the type of
cancer that you may have. So

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digital biology is

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really the next digital frontier
in clinical medicine.

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It may be the last major clinical workflow
to go through a digital transformation,

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and that’s when we take this glass slide

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and we digitize the slide

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so that the cells become an image,

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and then the pathologists can look at that
through their computer

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versus looking at the glass slide
through a microscope.

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So digital pathology,

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by definition, is a dynamic,

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image-based environment
that enables the acquisition,

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the management
and interpretation of pathology

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information that’s generated

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by digitizing that glass slide.

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So it seems like such a simple concept,
right?

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Like taking a glass slide
and turning it into a picture.

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So I can’t imagine it’s that simple.

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What is involved with digitally creating
an image of these slides?

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Is it as simple as scanning in a machine
or what else is involved?

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Is there any AI/ML impact there?

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It is a complex process.

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However, there are huge advantages over

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how we look at those glass
slides on a microscope today.

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So we would have the same process
of actually

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putting the tissue sample on the slide
and staining the slide or applying

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a chemical to the slide to make certain
cells be more visible than others.

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But then we will take that slide
and put them in trays.

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And these trays can hold anywhere
from one slide to hundreds of slides.

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And then you place them in a device
or a whole slide scanner,

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and then each slide is
then individually scanned.

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And there’s different ways
to scan the slides, depending

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on the type of sample that is on the slide
that you’re trying to digitize.

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So you can do tiling
where the scanner takes

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little tiny squares of images in a row,
or like they’ll do tile strips

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all at once,
where’s this one linear read of data.

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Or we can actually
for some types of tissue samples,

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we want to be able to have that X
and that Y plane scanned.

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But we also want to have the view
through the cells,

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as you can imagine, on a glass slide,
when you put the tissue on there,

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it’s not all flat.

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It’s not the cells
don’t line up all in one plane.

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Right.

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So there’s multiple planes or cells

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that can be stacked on each other
as you look down through a sample.

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So that type of Z stack scanning
and the intense scanning process

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gives us a really good depth
and allows us to see through that tissue.

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So once the slide is scanned,
then those images

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will then present on a view
or for a pathologist to look at.

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Now that’s the simplest workflow.

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Where the dynamics really come in
and where we can really enhance

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our ability to provide precision medicine
to patients, meaning individualized

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diagnosis based on their condition
and their sample alone

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is when we apply AI to this,

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we have a lot of improvements
that will come in this field

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because of the ability
to help the pathologist do their job.

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That’s kind of the first side of it.

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So what do I mean by that?

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Today when a pathologist has a glass slide

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and they put that slide
into the microscope, they have to

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first identify what’s the area of interest
that I want to look at.

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What is it on this slide sample
where the cells that I’m interested in

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are clustered
on? So first I have to identify that.

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And oftentimes they take just a marker
and they literally write on the glass

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and think about how small
those glass slides are. Right.

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You remember when you were in biology
or chemistry, how small those slides are.

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So a pathologist has to take a marker
and mark around that

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and then identify the cells of interest
and then count

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individual cells, which is very time
consuming for a pathologist.

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So at the front of the workflow,
we are working to develop AI models

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to mark the area or the region of interest
for the pathologist

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and began to do the cell segmentation –
or the cell counting.

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So the pathologists can really focus
on identifying the types of cells

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and the behavior of the cells

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versus just simply prepping the data,
if you will, for them to look at.

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And then beyond that, AI
and we have lots of AI developers,

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as you can imagine,
that are working on solutions or AI algos

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or algorithms to be able to help

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look at the cells, identify
the cells that are interesting

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and then compare those cells to databases

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of millions of other similar samples

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and be able to improve
that precision medicine diagnosis.

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For instance, today
we only have one approved AI algorithm

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in the field of pathology for disease
detection, and that is comparing

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tissue patterns from a needle
for biopsy used in prostate testing.

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And we’re able to take that needle
for a biopsy sample,

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compare it to a large database
of known cancers.

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And in the clinical trials for this

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AI model, they reduced the occurrence
of false negatives.

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That means I don’t
think I see any disease here.

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We have
reduced the false negatives by 70%.

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So that means 70% of the test cases

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that were used in these clinical trials
actually had cancer present.

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But the human pathologists by themselves
did not diagnose that.

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So 70% of the time
a patient had something questionable

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or wrong with them that we did not detect.

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So by applying AI,
we can be much smarter about our diagnosis

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because we’re comparing it
against millions of other tissue samples

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that may be similar.

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And in this example, we can see
how that really can impact lives.

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Now, I’m not saying
that 70% of false negatives

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that those people
had a critical condition.

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Right.

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But that could be

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someone that an early intervention
could keep them from becoming sick.

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Or maybe there’s other things
that we can do for that patient

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to help them have a great quality of life
and not actually be negatively impacted

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by the disease state
that may be occurring in their body.

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And that’s the beauty
of precision medicine.

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That’s the beauty of how we can apply
AI and make a difference in health care

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and in patient diagnosis.

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Yeah, another benefit of doing
this is – so that these slides I know

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if you’ve ever worked with any glass
slides and I remember in high school

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doing the biology stuff,
those things are real fragile, right?

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So they break, they break pretty easily.

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And then you have to think about
also the organic material itself.

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Over time it breaks down.

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So having images
taken of that really reduces

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the amount of worry about losing
this valuable data that you’ve had.

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You no longer are storing it
as a hard piece of evidence.

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You know, you’re using digital imaging,
which can last essentially forever.

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Yeah, that’s a great point, Justin.

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And that’s another huge benefit

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of this digitization of these whole slide
images.

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We use glass slides in a manner of ways.

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One is for primary diagnosis.

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The second is gaining a second opinion.

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Right.

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I want another pathologist
to look at this image or to look at this

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tissue to see if the diagnosis matches
the first pathologist that looked at it.

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And then we also have research
and education

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where these samples are very important.

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So as you can imagine, a lot of slides
get broken like you just alluded to.

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And by digitizing these images,
we are able to easily share the image

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where in the past, if we needed a second
opinion, we would have to box

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that last slide up and send it by courier
to another pathologist,

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which can take days,
which is a delay in diagnosis.

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And what if that slide gets broken?

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What if it gets misplaced?

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So not only the time consideration
of transferring that physical content

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to another provider, it’s
also the time to diagnosis.

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And if you are waiting on a cancer
diagnosis, days matter, right?

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We know that.

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So by being able to communicate
with a digital form of the tissue,

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then we are able to reduce the time
to diagnosis, be able to keep that sample.

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And then also if you look at use cases
with research and education

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as students are sitting in a classroom,
you want them all to be able

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to see the same interesting case.

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And today you have that pass
this glass slide around to students

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or hope that you had enough tissue samples

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that you could make a number of glass
slides out of it.

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But I think it’s pretty easy to see
in this way that if you have that tissue

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sample digitized,
then every student can look at that

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same sample at the same time and be able
to really improve the education process.

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And we don’t have to again
worry about breakage

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because once it breaks, then the tissue
samples to store it and no one can use it.

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The other thing about the glass slides
is that they are very heavy,

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if you can imagine that, and today,
organizations have to retain

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those physical glass slides for years
and years and years and that’s

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a lot of real estate to have to be able
to maintain these slides.

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And I heard a statistic
that Iron Mountain has kind of well

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over a billion of these glass slides
that organizations are having to retain,

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but they never go back and look at them
or very seldom, for

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primary diagnosis
do you go back and look at them.

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But they have to retain them
and it takes a lot of space.

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I’ll give you a couple of
interesting statistics.

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So there’s one large academic
medical center here in the U.S.

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that started scanning their glass
slides a few years ago.

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And they have scanned
almost 3 million slides.

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And just to put that in perspective
for you, a medium sized hospital

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probably has about 100,000

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pathology studies on their patients.

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So that would be 100,000 glass slides.

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This organization has scanned 3 million,
almost 3 million slides.

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That turned out to be around
three petabytes of data.

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But to the point of the weight of
the glass is 17

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tons of glass slides that were scanned.

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And they estimated that
if you were to stack those slides

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on top of each other, it would be 1155

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stories of slides, approximately.

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00:13:37,216 –> 00:13:42,288
So that kind of just paints a picture
of the size of these glass slides.

219
00:13:42,288 –> 00:13:46,725
And if we’re able to digitize that,
that really takes a burden off

220
00:13:46,725 –> 00:13:50,229
of the health care organization
or off the life sciences organizations

221
00:13:50,229 –> 00:13:52,832
that may have to retain
that very heavy medium.

222
00:13:53,232 –> 00:13:56,702
Yeah, sounds like slides are kind of
like the tape of the health care

223
00:13:56,702 –> 00:13:57,303
industry, right?

224
00:13:57,303 –> 00:14:00,105
Like, trying to get away from that. Right.

225
00:14:00,105 –> 00:14:03,275
So with slides,
I guess this would kind of spawn

226
00:14:03,275 –> 00:14:06,478
some innovation as well
because you start to think, okay,

227
00:14:06,545 –> 00:14:09,181
why am I putting all this stuff
on individual slides?

228
00:14:09,181 –> 00:14:15,387
Where we’re going ultimately is lots of
smart people are working on technology

229
00:14:15,387 –> 00:14:18,824
so that the glass slides go away
eventually all together

230
00:14:18,824 –> 00:14:22,561
and that we’re actually able
to use the technology.

231
00:14:22,561 –> 00:14:25,331
And there’s several different ideas
that people are pursuing.

232
00:14:25,331 –> 00:14:29,101
But one idea is with something
called a micro CAT scan.

233
00:14:29,335 –> 00:14:33,072
So today we have big CAT scans
or CT scanners that you need

234
00:14:33,072 –> 00:14:36,775
to have a certain part of your body
scanned for better imaging.

235
00:14:36,909 –> 00:14:40,379
We’re all familiar with that
or my family members have one of those.

236
00:14:40,813 –> 00:14:45,217
We have small CT scanners
now that can sit on a countertop

237
00:14:45,751 –> 00:14:49,121
and that tissue sample itself
without the slide

238
00:14:49,121 –> 00:14:53,058
can be scanned by these micro CT scanners.

239
00:14:53,325 –> 00:14:57,363
And so from the original acquisition

240
00:14:57,696 –> 00:15:01,200
of the data, it’s in a digital format
and this is very similar

241
00:15:01,200 –> 00:15:06,105
to the early days of radiology,
where we would shoot a chest X-ray

242
00:15:06,238 –> 00:15:11,343
and then we would still have film
and we would put that film in a cartridge

243
00:15:11,677 –> 00:15:13,746
and then we would load that cartridge

244
00:15:14,113 –> 00:15:18,317
into a digitizer
and then it would digitally be scanned.

245
00:15:18,317 –> 00:15:21,420
And out comes this digital chest X-ray.

246
00:15:21,420 –> 00:15:25,724
So I liken the cartridge to the glass
slide today.

247
00:15:25,958 –> 00:15:31,363
And now when you have a chest X-ray, you
simply go into a digital radiology room.

248
00:15:31,597 –> 00:15:33,632
You still stand in front of a sensor.

249
00:15:33,632 –> 00:15:36,101
They shoot that X-ray image of you,

250
00:15:36,869 –> 00:15:39,538
but it’s completely digital in format.

251
00:15:39,538 –> 00:15:41,473
Now, from the point of acquisition.

252
00:15:41,473 –> 00:15:45,744
So from the point of acquisition
for pathology samples, in the future

253
00:15:45,878 –> 00:15:49,882
they will be digitized
versus having to have this interim

254
00:15:49,882 –> 00:15:53,585
medium of glass
for the tissue to be adhered to.

255
00:15:53,585 –> 00:15:55,654
So that’s where we’re ultimately going.

256
00:15:55,888 –> 00:15:57,756
I figured we wouldn’t
just keep doing the same thing

257
00:15:57,756 –> 00:16:00,859
over and over again because that does seem
very time consuming, very wasteful,

258
00:16:00,859 –> 00:16:03,796
and there are probably better ways
of doing this sort of thing

259
00:16:03,829 –> 00:16:05,097
that people are going to come up with.

260
00:16:05,097 –> 00:16:07,833
I’m not someone who’s going to come up
with that idea, obviously, but,

261
00:16:09,001 –> 00:16:09,268
you know,

262
00:16:09,268 –> 00:16:11,870
with my with my wipe off the slide idea
right

263
00:16:13,072 –> 00:16:16,442
away, no Clorox wipes, it’s fine.

264
00:16:17,843 –> 00:16:20,312
It’s like,
why can’t we repurpose that type of thing?

265
00:16:20,312 –> 00:16:23,315
And it is we don’t understand process
so why wouldn’t yeah.

266
00:16:23,382 –> 00:16:23,849
I mean it’s.

267
00:16:23,849 –> 00:16:27,186
I do think it’s really important
you know that evolution

268
00:16:27,486 –> 00:16:30,522
that will get us to
this digital age is really important.

269
00:16:30,522 –> 00:16:34,626
It is some super cool technology
to see what individuals and organizations

270
00:16:34,626 –> 00:16:36,595
are working on to move this field forward.

271
00:16:36,595 –> 00:16:41,533
When you say things like petabytes of data
and, you know, needing a place

272
00:16:41,533 –> 00:16:44,536
to put all that, I immediately think,
okay, well, where does NetApp fit in?

273
00:16:44,536 –> 00:16:44,670
Right.

274
00:16:44,670 –> 00:16:50,109
I mean, this sounds like a very lucrative
way for NetApp to get involved because

275
00:16:50,109 –> 00:16:53,612
we, of course, have multiple petabytes
to put things right.

276
00:16:53,912 –> 00:16:55,881
So where does not fit into all this?

277
00:16:55,881 –> 00:16:57,716
Are we looking at on-prem storage?

278
00:16:57,716 –> 00:16:59,251
Are we looking at cloud storage?

279
00:16:59,251 –> 00:16:59,885
A little of both?

280
00:16:59,885 –> 00:17:02,388
How is the industry moving towards this?

281
00:17:02,588 –> 00:17:06,358
Before I answer that question,
let me put this in perspective of what

282
00:17:06,358 –> 00:17:12,364
the impact to health care is from a health
IT perspective and the data management

283
00:17:12,364 –> 00:17:15,868
that’s going to be required
for this whole slide imaging.

284
00:17:15,868 –> 00:17:18,904
So we talked about the chest X-ray
a second ago

285
00:17:18,904 –> 00:17:21,907
and it’s a really great example
to compare.

286
00:17:21,907 –> 00:17:26,378
So a chest X-ray, normally
you get one or two views of that image.

287
00:17:26,378 –> 00:17:30,182
It’s very straightforward, single image
or maybe two images.

288
00:17:30,416 –> 00:17:34,353
Unlike an MRI or CT
where you put up hundreds of images

289
00:17:34,353 –> 00:17:37,022
that make up a single radiology exam.

290
00:17:37,456 –> 00:17:40,225
So a chest X-ray, one single image.

291
00:17:40,325 –> 00:17:43,929
It’s the highest resolution
image that we have in radiology.

292
00:17:44,296 –> 00:17:47,433
And it is approximately 10 to 15 megabytes

293
00:17:47,433 –> 00:17:50,736
for one radiology chest X-ray image.

294
00:17:51,003 –> 00:17:56,442
When we look at digital pathology
and whole slide imaging, a single image –

295
00:17:56,442 –> 00:18:02,147
and there will be multiple images
within a single case or exam –

296
00:18:02,514 –> 00:18:06,752
one full slide image is approximately one

297
00:18:06,752 –> 00:18:10,756
and a half to three gigabytes in size.

298
00:18:10,956 –> 00:18:15,461
So we can see that comparison
of the highest resolution, densest

299
00:18:15,461 –> 00:18:19,798
pixel image in radiology
today is 10 to 15 megabytes.

300
00:18:19,798 –> 00:18:25,304
We’lll jump to one and a half gigabytes
for a single pathology image.

301
00:18:25,304 –> 00:18:30,542
So CIOs know that they’re organized
and they’re starting to think about

302
00:18:30,843 –> 00:18:35,747
how do they begin to take this digital
transformation journey in pathology?

303
00:18:36,215 –> 00:18:38,717
And from a CIO perspective,

304
00:18:38,917 –> 00:18:41,987
how are they going to manage
all of this massive data?

305
00:18:41,987 –> 00:18:45,090
Because today it’s widely accepted that

306
00:18:45,090 –> 00:18:48,961
medical imaging comprises 80 – eight-zero

307
00:18:48,961 –> 00:18:52,931
percent – of all clinical data
generated worldwide.

308
00:18:53,332 –> 00:18:58,103
Well, that number, with the addition
of digital pathology, is set to explode.

309
00:18:58,737 –> 00:19:03,509
So if a medium sized community hospital
that’s generating

310
00:19:04,009 –> 00:19:08,680
100,000 pathology cases
a year is just average, right?

311
00:19:08,881 –> 00:19:14,253
That organization is looking at
probably 500 terabytes of data

312
00:19:14,253 –> 00:19:17,256
that’s going to be generated annually,
and that’s net-new data.

313
00:19:17,256 –> 00:19:21,960
Helping organizations understand
how to prepare for this

314
00:19:22,327 –> 00:19:25,230
new massive volume of data
that’s coming their way,

315
00:19:25,230 –> 00:19:28,467
as well as be able
to think about strategies

316
00:19:28,467 –> 00:19:33,572
to leverage the value of that data
in the use of AI model development

317
00:19:33,572 –> 00:19:39,077
or in the use of research and education
is a really great place for NetApp to be.

318
00:19:39,411 –> 00:19:43,949
And whether an organization
is looking to manage its data

319
00:19:44,283 –> 00:19:49,021
completely on-premises,
because we do still have some hesitations

320
00:19:49,021 –> 00:19:52,291
of putting masses of data in the cloud
from a health care perspective,

321
00:19:52,558 –> 00:19:55,727
whether that sort of a privacy
and security perspective

322
00:19:55,961 –> 00:19:57,296
or maybe there’s concerns

323
00:19:57,296 –> 00:20:00,832
about the egress fees if we do need
to bring that data down from the cloud.

324
00:20:01,133 –> 00:20:04,403
So whatever the reason,
if an organization wants to stay on-prem

325
00:20:04,736 –> 00:20:09,308
we certainly have great technologies
to help manage that data,

326
00:20:09,942 –> 00:20:14,546
whether it’s in that file format
as its initial type of data retention,

327
00:20:14,546 –> 00:20:18,884
or you want to promote that data
to a more cost effective object storage,

328
00:20:19,184 –> 00:20:20,552
perhaps using storage grade

329
00:20:20,552 –> 00:20:24,756
and using that fabric pools
to manage the tiering on-prem

330
00:20:25,123 –> 00:20:29,361
or if you are more cloud comfortable
or technologies can help

331
00:20:29,361 –> 00:20:34,800
you have that best hybrid experience
by being able to leverage our NetApp

332
00:20:34,866 –> 00:20:39,871
ONTAP operating system across
any of the public cloud providers.

333
00:20:39,871 –> 00:20:44,076
And it’s going to be really advantageous
and beneficial

334
00:20:44,476 –> 00:20:48,480
to the patient population
for organizations to be able

335
00:20:48,480 –> 00:20:55,454
to have their clinical data as part of the
AI algorithm development process

336
00:20:55,520 –> 00:20:59,524
or that AI model training or machine
learning model development.

337
00:20:59,691 –> 00:21:03,462
So organizations
that are wanting to leverage

338
00:21:03,462 –> 00:21:08,267
the value of their data
in that way, in a totally anonymized way

339
00:21:08,267 –> 00:21:11,370
that protects patient identity
and patient privacy.

340
00:21:11,370 –> 00:21:15,707
Of course, by leveraging NetApp
technologies, we can help organizations

341
00:21:15,707 –> 00:21:19,578
to replicate that information to the cloud
in a secure way.

342
00:21:19,578 –> 00:21:22,381
We’ve talked
about how NetApp fits in here and

343
00:21:22,981 –> 00:21:26,151
what sort of things digital pathology
brings to the industry.

344
00:21:26,785 –> 00:21:29,688
What about if I want to learn
more about this, like, where would I go

345
00:21:29,688 –> 00:21:32,891
to find more information
about digital pathology?

346
00:21:33,191 –> 00:21:34,393
NetApp Solutions?

347
00:21:34,393 –> 00:21:36,428
Yeah,
we have a great amount of information

348
00:21:36,428 –> 00:21:37,329
out there, Justin,

349
00:21:37,329 –> 00:21:38,563
that we’ll include the link

350
00:21:38,563 –> 00:21:42,401
in this podcast and then you can always
find this information at netapp.com.

351
00:21:42,601 –> 00:21:45,570
One thing that I did want to add
and one thing

352
00:21:45,570 –> 00:21:49,341
that we have information about that
I think is exciting and really timely for

353
00:21:49,341 –> 00:21:53,612
this field is a partnership
with Google Cloud that was announced

354
00:21:53,612 –> 00:21:57,115
at the beginning of October,
and this is the Google Cloud Medical

355
00:21:57,115 –> 00:22:00,185
Imaging suite of which NetApp
is a core component of.

356
00:22:00,185 –> 00:22:04,790
And with the Google Cloud Medical
Imaging Suite, we’re able to replicate

357
00:22:05,123 –> 00:22:09,061
data volumes from on-premises,
whether that’s an entire dataset

358
00:22:09,361 –> 00:22:12,831
or perhaps a data cohort,
replicate that data

359
00:22:12,831 –> 00:22:16,501
into the Google Cloud,
into a cloud volumes, ONTAP

360
00:22:17,102 –> 00:22:21,707
instance or Cloud Volumes Service instance
and leveraging technologies

361
00:22:21,707 –> 00:22:25,243
like Cloud Data Sense,
we’re able to help customers

362
00:22:25,510 –> 00:22:29,147
very quickly
be able to categorize and map the data

363
00:22:29,147 –> 00:22:34,453
that’s contained within the medical image
or this in this case, a whole slide image

364
00:22:34,886 –> 00:22:38,557
so that they can easily be able
to audit the data.

365
00:22:38,724 –> 00:22:43,662
But also in this use case, most
importantly, be able to query the data

366
00:22:44,129 –> 00:22:47,866
and create specific AI data cohorts

367
00:22:48,133 –> 00:22:51,503
that then feed into this greater

368
00:22:51,770 –> 00:22:54,873
AI model development environment
within the Google Cloud

369
00:22:55,073 –> 00:22:58,910
that provides a robust set of tools
to help customers

370
00:22:58,910 –> 00:23:03,648
be able to partner with third party
AI developers, in secure ways,

371
00:23:03,882 –> 00:23:08,353
so that those developers do not have to
actually be accessing the data on site.

372
00:23:08,820 –> 00:23:12,457
So by leveraging this
Google Medical Imaging suite,

373
00:23:12,457 –> 00:23:17,129
we are able to bring the power of NetApp
technologies, partner with Google,

374
00:23:17,129 –> 00:23:22,968
to provide a rich set of AI development
tools and services for customers

375
00:23:22,968 –> 00:23:25,904
to be able to take their pathology data
to the next level.

376
00:23:26,104 –> 00:23:28,940
Sounds like we got a good start
on this burgeoning industry, right?

377
00:23:29,441 –> 00:23:33,078
It’s not quite at the level of a
medical imaging, but it is getting there.

378
00:23:33,078 –> 00:23:35,313
Right. It’s the future of pathology there.

379
00:23:35,313 –> 00:23:38,750
So, Kim, again, if we wanted to reach you,
how do we do that?

380
00:23:38,884 –> 00:23:42,954
Yeah, you can simply reach me
at kim.garriott@netapp.com.

381
00:23:43,188 –> 00:23:45,857
All right.
That music tells me it’s time to go.

382
00:23:45,857 –> 00:23:47,492
If you’d like to get in touch with us,
send us an email

383
00:23:47,492 –> 00:23:51,296
to podcast@netapp.com,
or send us a tweet @NetApp.

384
00:23:51,730 –> 00:23:55,500
As always, if you’d like to subscribe
find us on iTunes, Spotify,

385
00:23:55,567 –> 00:24:00,605
Google Play, iHeartRadio, SoundCloud,
Stitcher or via techontappodcast.com.

386
00:24:01,072 –> 00:24:03,108
If you liked the show
today, leave us a review.

387
00:24:03,108 –> 00:24:06,711
On behalf of the entire Tech ONTAP podcast
team, I’d like to thank Kim Garriott

388
00:24:06,812 –> 00:24:07,813
for joining us today.

389
00:24:07,813 –> 00:24:14,386
As always, thanks for listening.

390
00:24:27,365 –> 00:24:30,802
[Tech

391
00:24:30,802 –> 00:24:34,239
ONTAP

392
00:24:34,239 –> 00:24:37,676
Podcast

393
00:24:37,676 –> 00:24:41,112
Outro

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00:24:41,112 –> 00:24:44,549
theme]

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