Hello, and welcome to the Physics World weekly
podcast.
Our guest in this episode is the chief
scientist of a startup company that's developed a
noninvasive
way to measure the pressure
inside the skull.
If their optical sensor
gets approval for routine clinical use,
it could revolutionize
how brain injuries are diagnosed
and treated.
Here is Physics World's Tammy Freeman
in conversation
with Panikos
Kyriaku.
Traumatic brain injury
caused by a sudden jolt or impact to
the head is a leading cause of death
and disability.
After such an injury, the most important indicator
of how severe the injury is is intracranial
pressure, which is the pressure inside the skull.
But at the moment, the only way to
assess this is by inserting a pressure sensor
into the patient's brain.
Aiming to eliminate the need for such an
invasive procedure,
UK based startup Cranio has developed a noninvasive
way to measure intracranial pressure
using a simple optical probe attached to the
patient's forehead.
I'm joined today by Panikas Kiriakou,
Cranio's chief scientist,
to find out more.
Welcome to the podcast, Panikas.
Oh, thank you. Thank you so much for
having me.
So the first thing, can you can you
explain why traumatic brain injury is such an
important clinical problem?
Oh, goodness me. I mean,
just to give you a little bit of
a background,
can you imagine that every three minutes in
The UK,
someone is admitted to a hospital with a
head injury? So it's a it's a very,
very
common problem, and you can imagine people having
accidents,
whether it's road accidents, whether any impact on
the head to their work environment,
in sports,
contacts.
So it's pretty common people have a blow
on the head. Now how bad it is,
nobody knows until actually they reach the hospital.
So the ambulance will pick them up. It
would drive them to an A and E
naturally,
and there, the patients will start having an
assessment.
And there will be a point that somebody
from the neurosurgical team will be asked to
look at the patient.
Is it a concussion? Is it more serious?
And, at the time of impact
to the time that the patient will reach
the hospital,
and he will receive an assessment by an
expert,
especially in neurocritical
care or in a trauma unit. That is
known as the golden hour, and nobody knows
what's happening to the brain at that time.
And,
it it it could be it it could
be in a in a disaster in a
way because
you don't know how best to manage the
patient, whether this patient has a severe traumatic
injury, so the intracranial pressure is rising in
the head, or it's a mild, or it's
just a concussion, or it's a medium traumatic
brain injury.
So for many, many years, and especially in
the last few decades, people are trying to
find solutions in medicine
where we can assess
disease,
diagnose,
screen,
disease
in
faster,
noninvasively
at the point of injury.
And in the in the context of traumatic
brain injury, it's something that at the moment
cannot be assessed,
at the point of impact.
And in many ways, if a patient has
a severe traumatic brain injury,
the neurosurgeons,
they will have to assess
how bad is their intracranial
pressure.
Is it really above the threshold that classifies
them as severe? And in order to do
that, they have to drill a hole in
the head, literally.
And they will place into the
brain
a sensor probe. It's an electrical probe.
And this is really,
I I I would say, one of the
most
non
the most invasive
non therapeutic procedures.
And,
you can obviously do this,
to every patient that comes in with a
blow on the head.
So it's it's quite invasive. It has its
risks. Firstly, it has to happen within a
clinical unit,
with expertise.
It could be,
there is a risk of hemorrhage.
There is a risk of,
infection.
And, therefore, there is this sort of cry
out to develop technologies that can we measure
intracranial pressure
more effectively,
earlier,
and in a noninvasive
manner? And for some people for many years,
that it was almost like a dream. They
say, no. Impossible to do that. How can
you access the brain and see if the
pressure is rising in the brain, and you
can do this by placing something on the
forehead?
Now
coming to us in Cranio, obviously, Cranio
was born in
02/2022
out of research, came out of City, University
of London, now City, Saint George's University of
London,
and,
within the research center for biomedical engineering.
So this goes back in 02/2016,
where the National Institute of Health Research gave
us our first grant to investigate
the feasibility
of having a noninvasive
intracranial sensor
based on light technologies.
The grant funded by the NHS was very
successful. We managed to develop a prototype technology.
We secured the intellectual property because we felt
this is quite impactful.
It has a commercialization
value.
And through that first study we did at,
at the time, we managed to conduct a
feasibility study on
TBI patients, traumatic brain injury patients
at the Royal London Hospital at the neurocritical
care unit. And the Royal London in the
East Part Of London is the biggest trauma
hospital in U UK.
And that was the time back in 02/2021,
before Cranio was born,
that we found
we we discovered the first sort of positive
news
that the optical signals coming back from the
brain
after we apply an optical sensor. So we
put a sensor on the head,
on the forehead.
We shine light into the brain. And a
lot for many years and decades, people are
studying
the effect of light when it penetrates into
tissue,
including
visible or infrared light, near infrared light. In
in our lab, in my lab, we've done
extensive studies on light tissue interaction
and what happens to the light when it
goes into the brain, passes through the forehead,
through the skull, into the brain
at certain colors of light, like near infrared.
The light will come back, and the information
coming back,
this optical signal, also known the photopletysmogram
or the PBG,
it could really tell us there is information
which within the signal
that it keeps secrets of what's happened to
the physiology
or the hemodynamics
of the brain.
As you can imagine, when the pressure in
the brain is rising,
the pressure keeps
the the the brain is swelling up, but
it cannot go anywhere because the scalp is
like concrete. And, therefore, the arteries and the
vessels, they are compressed in the brain by
that pressure.
And this PPG signal
shows us the changes
due to the compression of the vessels.
And, therefore, the optical signals
change
or components on the optical signal or features
on the optical signal
change
with,
changes in intracranial pressure. And there, people would
develop the algorithms to be able to interrogate
that optical signal
and then develop models,
machine learning models as we discuss in a
minute, how to estimate,
intercranial pressure.
What is it that the the light's actually
measuring? You say that the vessels are compressed.
Is it is it looking at the actual
size of the vessels or the the blood
flowing within? Or
The photoplethysmograph
by its nature and definition, it measures volumetric
changes
of blood during systole and diastole.
So as the blood pulsating through the arteries,
through the cardiac cycle,
the PBG, this optical technique,
is able to show the changes in volume
of blood within the artery.
So you can imagine if you have a
viscoelastic
artery
that is,
opening and closing through systole and diastole, the
volume of blood changes.
And this is mapped, it's captured by the
PVG.
Now if you have an artery that is
compromised,
it is pushed down because of the pressure
in the brain, that viscoelastic
property of the artery
is is impacted,
and that would impact the PPG.
So the changes of the PPG
due to the volumetric changes it's experiencing
due to the compression of the vessels in
the brain
can tell us information about the ICP.
And
following the research,
within the university,
Crennio was created. It brought together a team
of experts, a lot of passionate people
to aid the further development
and commercialization.
What makes this happen?
CTE, the university, licensed the technology to NLC.
NLC
is a health technology venture builder
based,
headquartered in The Netherlands,
And they work very closely with universities,
medical schools,
taking ideas,
innovations
in a way from bench to patient.
So they gave us really that momentum to
form the company. So Cranio is a UK
based company.
And The U and Cranio from the first
initial days was very proactive,
comprising of a small team with, people from
expertise
in medical devices
and optical sensors.
And the team worked tirelessly, really, in the
last few years to generate,
funding,
to be able to progress further with the
development of the technology, the optical sensor, to
bring it to a technology readiness level that
is ready for further clinical trials.
So,
in 2023,
Cranio was very successful with the an Innovate
UK biomedical catalyst grant, which will enable enable
the company to
engage in a clinical feasibility study,
optimize the technology,
to a more,
advanced probe,
both the optical sensor, the optical front end,
and the analog back end, the analog front
end, the electronics,
and develop further the algorithms.
Then later on,
the company was awarded another
National Institute of Health Research grant
to be able to,
move the technology more into a validation study.
So now we reach the stage where Crenio
redeveloped the sensor. It looks amazing.
It looks very commercial when you look at
it if you're in the business of, you
know, medical devices.
The
technology has received,
approval by MHRA,
the UK regulator,
in order to proceed to the clinical studies.
The clinical,
studies now are on route.
Ethical approvals have been secured.
We are again partnering
with the Royal London Hospital.
We also more collaborators came on board. We
have people from Cambridge,
from the traumatic brain injury team are joining
us
to support the team.
And I think we're expecting to enter
clinical trials by the end of this month.
This will be an opportunity
to
work with a new technology, the new pro,
much more advanced probe, much more advanced electronics
that would enable
more detailed acquisition of signals from TBI patients.
So these are patients that are admitted in
urocritical
trauma units.
And all of them, they do have an
invasive intracranial
ICP bolt.
Hence, they will allow us to compare Yeah.
Both standard with our signals.
The signals then, they will be collated, and
they will be analyzed by Kranios data science
team. This is the team that is utilizing
machine learning algorithms,
basically looking at changes on the PPG signal,
extracting
morphological
features from the signal amongst other parameters,
and then building different models in order to
develop the technology further in order to predict
dynamically,
noninvasively,
intracranial pressure
in either absolute measurements
or indicative of,
high ICP, low ICP.
So we're at this stage now.
The
national and international interest in this project, it
has been overwhelming. You can imagine
the interest, first of all, from the neurocritical
care communities.
So the vibrations
of cranios,
you know, successes,
have disseminated
through,
and there is a very positive feedback from
the neurocritical care community.
There is an active interest of the progression
of the technology.
But interestingly, now we we get a lot
of interest, not peripherally,
but from communities where
injury to the brain is significant.
It could be from sports associations
like the rugby association.
People are interested in concussion.
People are interested in other pathologies relating to
intracranial pressure. So even though Cranios' primary focus
is to deliver a technology
for utilization
in your critical care, You can imagine
that this technology could be used in emergency
care, in ambulances,
in helicopters. They transfer patients.
So it can find this place in many
NHS,
places and beyond. So it's a very, very
exciting
journey.
It's going well, and,
we we are we are very positive on
the outcome. Yes. So, I mean, the the
device is portable, so it could be used
at at the site of an accident.
Exactly. The device being, one, noninvasive.
The sensor, it's just a, like, a a
sticky plaster that it goes onto your forehead.
The back end is a little box with
all the electronics.
Obviously,
from the past few years, we work in
this research environment.
So the technology, it was, connected into a
laptop computer. This is what researchers do in
labs. But now we are sort of transferring
the knowledge into,
a graphical interface. So you will have, obviously,
device with a monitor
to be able to,
see the signals,
see the values of ICP in a portable
way. Yes.
Okay. And then you you mentioned that the
signals are analyzed by machine learning algorithms. So
did you have to develop your own proprietary
software? It was this part of the the
process?
That is correct. I mean, machine learning applications
in health care, they are exponentially
and, I would say, cataclysmically
grown over the years.
It allows people to develop models, whether it's
in biosignals
or imaging, using images
to be able to diagnose disease.
So the team
within the university and later on by cranial,
fortify
the data science part of the team. We
brought experts that they have spent years working
with
predominantly photopredismograms
and be able to develop bespoke
machine learning algorithms
of extracting information
from this physiological
optical signal, the PVG,
and built in models to be able to
predict intracranial pressure. So, yes, it's correct. The
algorithms developed by Cranio is almost
like gold dust to the company.
Okay.
And as you say, you've done some preliminary
tests on patients, and you're starting this this
sort of new clinical trial. What do you
what do you hope to achieve with with
the next lot of measurements? What are you
aiming for? Yeah. Absolutely. I mean, very good
question. Of course, the first study, it was
done during,
the time where the project was within the
university.
It was the first feasibility study on a
probe or sensor technology that was built,
within the university.
Since then,
we've learned
more,
how we can you know, lessons learned from
the first clinical trial
on on a group of 50 patients or
so.
And the second round now conducted and let
is is gonna be led by Crenio
with a more optimized probe.
So after the first experiences,
we've learned,
the technical challenges we had, and we're trying
to mitigate them with the new probe design,
whether it's in the optics,
the configuration of the probe, the physical appearance
of the probe, how it's placed on the
forklift.
So those lessons were learned. So, hopefully, this
round,
we will, mitigate some of the challenges we
had before in the technical aspect.
Also, through the first study, we've learned
the challenges or the problems
with the acquisition of signals.
So the type of patients, how long we
should monitor.
So now we can do the study more
rigorously,
longer,
more supervised in order to acquire
high quality signals.
This time around, we will record
more information from the patients.
We will, re we will look at CT
scans
to see how the scalp density, the thickness
of the skull, perhaps
it will have an impact on the light
between, you know, different ages, different sexes.
We will,
monitor and collect data,
from the commercial medical monitors within neurocritical
care in order to see the relation of
ICP with other physiological
data acquired in these patients. So we are
expanding the acquisition of data
and the knowledge pool about
what happens when a patient's intracranial pressure pressure
rises.
What happens to the blood pressures? What happens
to the other physiological measurements of the patient?
So we're enriching the study
with a more advanced technology,
and, hence,
and fingers crossed,
this would lead to more accurate machine learning
models in relation to capturing correctly,
dynamically
the changes in intracranial pressure.
And you and you said in this, in
these patients, they're all they've also got the
invasive probe. So Indeed. Compare the results and
sort of check the accuracy.
It is fundamental
to be able to compare
our results and our experiences
in changes in ICP
with the gold standard. So we will have
all the,
in real time, the traces, the physiological sick
the signal coming from the ICP sensor
into our own acquisition system so we know
exactly
what's happening to the patient's ICP
according to the gold standard in relation to
our own ICP technology.
Okay. And then, you know, presuming all the
the next,
trials are all successful, how far away is
the system from sort of being used clinically
as as a standard tool?
Well, Kranio is very ambitious.
For those being in the medical devices,
game, it's a long game, usually, medical devices.
But the trajectory,
we're hoping that within
the next couple of years,
we will
progress
adequately
in order to enable us to, you know,
jump all the regulatory sort of COPES,
c marking,
all the standards that are necessary to launch
the medical a medical device.
So we are quite optimistic. We think within
the next two, three years, we'll
be in a very good position actually to
get very, very close to very close to
the market,
all all be well, of course.
And then do you predict will this It
sounds like it'd be sort of relatively low
cost
option if it's just based on optics for
for hospitals to use.
Well,
usually, this is questions that speaks to the
CEO.
But,
I mean, you are right. I mean, the
ambition here is to the primary motivation
of Cranio
is to create solutions for health care. I
mean, developing a technology
which we can help clinicians
to diagnose
traumatic brain injury effectively,
faster,
accurately,
earlier.
It can only yield in the
better patient outcomes,
improving
the quality of life of those patients. We
work closely with the charity.
And,
through those discussions,
they're very impactful for us within Cranio to
see how the patients and the carers
are impacted with traumatic brain injury.
So that one that's one of our primary
motivators. This is what gives energy to Cranio,
really. Of course, as a as a company,
we're interested in being successful commercially as well,
but the ambition here is to keep the
cost, first of all, affordable. We live in
a world that take medical technologies, they need
to be
affordable, not only for the western nations, but
the nations that they cannot afford state of
the art technologies.
So, this is, again,
one of the primaries within Crenio to be
able to
create a sensor technology
that it could be used widely. And
used widely because there is a massive need,
but also used widely because it's affordable technology.
In the commercial journey of cranial,
obviously, The UK is a big market.
NHS, for us, it will be a primary
a primary market. Obviously, our ambitions, they are
beyond.
We want to reach, obviously, the European market
and, obviously, The US market, which is very
big.
So on our target is, at some point
after,
you know, the c marking approval,
is to
tap into The
US market.
So we need to obviously pass through the
pathways of the FDA
approval in order to enter The US market.
So in the sort of short, medium, and
long term, Crenio
inspires by 2030
to be able to penetrate
The US market.
Hopefully, the hopefully, The UK market as close
as 2027,
'20 '8.
So we're ambitious. The team is amazing.
All very passionate about the technology.
So, yeah, that's the sort of endgame
with the company.
It sounds like a really promising tool that
could you know, it has a lot to
offer, Hunter.
So I wish the company lots of luck
for the future. That sounds great. Thank you.
Thank you. Excellent.
Well, thank you very much for joining us
today. Thank you. Very welcome. My pleasure. Thank
you so much.
That was Kranios' chief scientist,
Panikos
Kyriakou,
in conversation with Physics World's Tammy Freeman.
I'm afraid that's all the time we have
for this week's podcast.
Thanks to Pankajos
and Tammy for a fascinating conversation.
And a special thanks to our producer Fred
Isles.
We'll be back again next week. But in
the meantime, do check out the latest episode
of the Physics World Stories podcast.
Host Andrew Glester is joined by three
expert guests
to explore the impact of artificial intelligence
on discovery,
research, and the future of physics.
That episode is called
AI and the future of physics,
and you can find it on the Physics
World website
or at your favorite podcast provider.