Non-invasive pressure sensor could revolutionize how brain injuries are diagnosed

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Hello, and welcome to the Physics World weekly

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podcast.

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Our guest in this episode is the chief

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scientist of a startup company that's developed a

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noninvasive

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way to measure the pressure

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inside the skull.

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If their optical sensor

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gets approval for routine clinical use,

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it could revolutionize

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how brain injuries are diagnosed

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and treated.

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Here is Physics World's Tammy Freeman

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in conversation

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with Panikos

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Kyriaku.

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Traumatic brain injury

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caused by a sudden jolt or impact to

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the head is a leading cause of death

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and disability.

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After such an injury, the most important indicator

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of how severe the injury is is intracranial

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pressure, which is the pressure inside the skull.

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But at the moment, the only way to

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assess this is by inserting a pressure sensor

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into the patient's brain.

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Aiming to eliminate the need for such an

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invasive procedure,

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UK based startup Cranio has developed a noninvasive

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way to measure intracranial pressure

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using a simple optical probe attached to the

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patient's forehead.

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I'm joined today by Panikas Kiriakou,

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Cranio's chief scientist,

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to find out more.

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Welcome to the podcast, Panikas.

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Oh, thank you. Thank you so much for

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having me.

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So the first thing, can you can you

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explain why traumatic brain injury is such an

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important clinical problem?

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Oh, goodness me. I mean,

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just to give you a little bit of

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a background,

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can you imagine that every three minutes in

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The UK,

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someone is admitted to a hospital with a

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head injury? So it's a it's a very,

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very

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common problem, and you can imagine people having

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accidents,

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whether it's road accidents, whether any impact on

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the head to their work environment,

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in sports,

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contacts.

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So it's pretty common people have a blow

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on the head. Now how bad it is,

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nobody knows until actually they reach the hospital.

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So the ambulance will pick them up. It

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would drive them to an A and E

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naturally,

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and there, the patients will start having an

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assessment.

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And there will be a point that somebody

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from the neurosurgical team will be asked to

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look at the patient.

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Is it a concussion? Is it more serious?

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And, at the time of impact

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to the time that the patient will reach

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the hospital,

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and he will receive an assessment by an

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expert,

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especially in neurocritical

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care or in a trauma unit. That is

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known as the golden hour, and nobody knows

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what's happening to the brain at that time.

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And,

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it it it could be it it could

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be in a in a disaster in a

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way because

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you don't know how best to manage the

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patient, whether this patient has a severe traumatic

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injury, so the intracranial pressure is rising in

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the head, or it's a mild, or it's

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just a concussion, or it's a medium traumatic

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brain injury.

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So for many, many years, and especially in

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the last few decades, people are trying to

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find solutions in medicine

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where we can assess

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disease,

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diagnose,

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screen,

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disease

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in

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faster,

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noninvasively

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at the point of injury.

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And in the in the context of traumatic

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brain injury, it's something that at the moment

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cannot be assessed,

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at the point of impact.

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And in many ways, if a patient has

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a severe traumatic brain injury,

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the neurosurgeons,

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they will have to assess

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how bad is their intracranial

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pressure.

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Is it really above the threshold that classifies

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them as severe? And in order to do

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that, they have to drill a hole in

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the head, literally.

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And they will place into the

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brain

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a sensor probe. It's an electrical probe.

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And this is really,

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I I I would say, one of the

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most

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non

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the most invasive

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non therapeutic procedures.

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And,

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you can obviously do this,

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to every patient that comes in with a

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blow on the head.

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So it's it's quite invasive. It has its

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risks. Firstly, it has to happen within a

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clinical unit,

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with expertise.

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It could be,

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there is a risk of hemorrhage.

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There is a risk of,

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infection.

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And, therefore, there is this sort of cry

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out to develop technologies that can we measure

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intracranial pressure

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more effectively,

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earlier,

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and in a noninvasive

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manner? And for some people for many years,

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that it was almost like a dream. They

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say, no. Impossible to do that. How can

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you access the brain and see if the

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pressure is rising in the brain, and you

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can do this by placing something on the

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forehead?

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Now

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coming to us in Cranio, obviously, Cranio

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was born in

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02/2022

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out of research, came out of City, University

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of London, now City, Saint George's University of

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London,

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and,

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within the research center for biomedical engineering.

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So this goes back in 02/2016,

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where the National Institute of Health Research gave

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us our first grant to investigate

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the feasibility

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of having a noninvasive

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intracranial sensor

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based on light technologies.

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The grant funded by the NHS was very

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successful. We managed to develop a prototype technology.

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We secured the intellectual property because we felt

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this is quite impactful.

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It has a commercialization

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value.

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And through that first study we did at,

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at the time, we managed to conduct a

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feasibility study on

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TBI patients, traumatic brain injury patients

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at the Royal London Hospital at the neurocritical

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care unit. And the Royal London in the

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East Part Of London is the biggest trauma

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hospital in U UK.

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And that was the time back in 02/2021,

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before Cranio was born,

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that we found

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we we discovered the first sort of positive

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news

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that the optical signals coming back from the

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brain

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after we apply an optical sensor. So we

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put a sensor on the head,

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on the forehead.

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We shine light into the brain. And a

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lot for many years and decades, people are

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studying

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the effect of light when it penetrates into

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tissue,

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including

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visible or infrared light, near infrared light. In

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in our lab, in my lab, we've done

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extensive studies on light tissue interaction

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and what happens to the light when it

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goes into the brain, passes through the forehead,

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through the skull, into the brain

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at certain colors of light, like near infrared.

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The light will come back, and the information

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coming back,

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this optical signal, also known the photopletysmogram

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or the PBG,

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it could really tell us there is information

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which within the signal

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that it keeps secrets of what's happened to

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the physiology

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or the hemodynamics

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of the brain.

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As you can imagine, when the pressure in

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the brain is rising,

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the pressure keeps

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the the the brain is swelling up, but

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it cannot go anywhere because the scalp is

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like concrete. And, therefore, the arteries and the

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vessels, they are compressed in the brain by

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that pressure.

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And this PPG signal

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shows us the changes

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due to the compression of the vessels.

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And, therefore, the optical signals

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change

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or components on the optical signal or features

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on the optical signal

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change

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with,

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changes in intracranial pressure. And there, people would

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develop the algorithms to be able to interrogate

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that optical signal

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and then develop models,

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machine learning models as we discuss in a

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minute, how to estimate,

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intercranial pressure.

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What is it that the the light's actually

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measuring? You say that the vessels are compressed.

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Is it is it looking at the actual

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size of the vessels or the the blood

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flowing within? Or

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The photoplethysmograph

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by its nature and definition, it measures volumetric

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changes

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of blood during systole and diastole.

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So as the blood pulsating through the arteries,

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through the cardiac cycle,

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the PBG, this optical technique,

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is able to show the changes in volume

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of blood within the artery.

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So you can imagine if you have a

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viscoelastic

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artery

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that is,

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opening and closing through systole and diastole, the

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volume of blood changes.

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And this is mapped, it's captured by the

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PVG.

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Now if you have an artery that is

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compromised,

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it is pushed down because of the pressure

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in the brain, that viscoelastic

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property of the artery

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is is impacted,

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and that would impact the PPG.

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So the changes of the PPG

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due to the volumetric changes it's experiencing

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due to the compression of the vessels in

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the brain

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can tell us information about the ICP.

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And

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following the research,

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within the university,

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Crennio was created. It brought together a team

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of experts, a lot of passionate people

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to aid the further development

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and commercialization.

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What makes this happen?

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CTE, the university, licensed the technology to NLC.

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NLC

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is a health technology venture builder

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based,

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headquartered in The Netherlands,

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And they work very closely with universities,

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medical schools,

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taking ideas,

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innovations

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in a way from bench to patient.

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So they gave us really that momentum to

287
00:10:26,384 --> 00:10:28,909
form the company. So Cranio is a UK

288
00:10:28,909 --> 00:10:29,730
based company.

289
00:10:30,829 --> 00:10:33,409
And The U and Cranio from the first

290
00:10:33,470 --> 00:10:35,889
initial days was very proactive,

291
00:10:36,589 --> 00:10:39,389
comprising of a small team with, people from

292
00:10:39,389 --> 00:10:39,889
expertise

293
00:10:40,269 --> 00:10:41,409
in medical devices

294
00:10:43,725 --> 00:10:44,705
and optical sensors.

295
00:10:45,245 --> 00:10:47,565
And the team worked tirelessly, really, in the

296
00:10:47,565 --> 00:10:49,664
last few years to generate,

297
00:10:50,284 --> 00:10:50,784
funding,

298
00:10:51,164 --> 00:10:53,325
to be able to progress further with the

299
00:10:53,325 --> 00:10:56,299
development of the technology, the optical sensor, to

300
00:10:56,299 --> 00:10:58,699
bring it to a technology readiness level that

301
00:10:58,699 --> 00:11:01,199
is ready for further clinical trials.

302
00:11:01,740 --> 00:11:02,240
So,

303
00:11:03,019 --> 00:11:04,559
in 2023,

304
00:11:04,620 --> 00:11:07,579
Cranio was very successful with the an Innovate

305
00:11:07,579 --> 00:11:11,735
UK biomedical catalyst grant, which will enable enable

306
00:11:11,735 --> 00:11:13,034
the company to

307
00:11:13,654 --> 00:11:16,074
engage in a clinical feasibility study,

308
00:11:16,694 --> 00:11:17,834
optimize the technology,

309
00:11:18,934 --> 00:11:19,834
to a more,

310
00:11:20,375 --> 00:11:21,434
advanced probe,

311
00:11:21,815 --> 00:11:24,750
both the optical sensor, the optical front end,

312
00:11:24,830 --> 00:11:27,230
and the analog back end, the analog front

313
00:11:27,230 --> 00:11:28,129
end, the electronics,

314
00:11:29,070 --> 00:11:30,929
and develop further the algorithms.

315
00:11:31,950 --> 00:11:33,409
Then later on,

316
00:11:34,269 --> 00:11:36,210
the company was awarded another

317
00:11:36,990 --> 00:11:39,169
National Institute of Health Research grant

318
00:11:39,584 --> 00:11:40,804
to be able to,

319
00:11:41,904 --> 00:11:44,564
move the technology more into a validation study.

320
00:11:45,024 --> 00:11:47,444
So now we reach the stage where Crenio

321
00:11:47,745 --> 00:11:50,485
redeveloped the sensor. It looks amazing.

322
00:11:51,345 --> 00:11:53,504
It looks very commercial when you look at

323
00:11:53,504 --> 00:11:55,209
it if you're in the business of, you

324
00:11:55,209 --> 00:11:56,750
know, medical devices.

325
00:11:57,169 --> 00:11:57,669
The

326
00:11:58,089 --> 00:11:59,629
technology has received,

327
00:12:00,490 --> 00:12:01,950
approval by MHRA,

328
00:12:02,330 --> 00:12:03,389
the UK regulator,

329
00:12:04,089 --> 00:12:07,309
in order to proceed to the clinical studies.

330
00:12:07,769 --> 00:12:08,429
The clinical,

331
00:12:09,209 --> 00:12:10,830
studies now are on route.

332
00:12:11,465 --> 00:12:13,245
Ethical approvals have been secured.

333
00:12:13,705 --> 00:12:15,085
We are again partnering

334
00:12:15,465 --> 00:12:17,725
with the Royal London Hospital.

335
00:12:18,425 --> 00:12:21,465
We also more collaborators came on board. We

336
00:12:21,465 --> 00:12:22,845
have people from Cambridge,

337
00:12:23,225 --> 00:12:26,585
from the traumatic brain injury team are joining

338
00:12:26,585 --> 00:12:26,879
us

339
00:12:28,320 --> 00:12:29,700
to support the team.

340
00:12:30,480 --> 00:12:32,580
And I think we're expecting to enter

341
00:12:32,960 --> 00:12:36,580
clinical trials by the end of this month.

342
00:12:36,960 --> 00:12:38,259
This will be an opportunity

343
00:12:38,720 --> 00:12:39,220
to

344
00:12:40,160 --> 00:12:42,580
work with a new technology, the new pro,

345
00:12:42,884 --> 00:12:46,024
much more advanced probe, much more advanced electronics

346
00:12:46,644 --> 00:12:47,625
that would enable

347
00:12:48,245 --> 00:12:52,425
more detailed acquisition of signals from TBI patients.

348
00:12:52,725 --> 00:12:54,804
So these are patients that are admitted in

349
00:12:54,804 --> 00:12:55,304
urocritical

350
00:12:55,684 --> 00:12:56,519
trauma units.

351
00:12:57,080 --> 00:12:59,080
And all of them, they do have an

352
00:12:59,080 --> 00:13:00,379
invasive intracranial

353
00:13:01,160 --> 00:13:02,300
ICP bolt.

354
00:13:02,679 --> 00:13:05,240
Hence, they will allow us to compare Yeah.

355
00:13:05,320 --> 00:13:07,179
Both standard with our signals.

356
00:13:07,800 --> 00:13:10,040
The signals then, they will be collated, and

357
00:13:10,040 --> 00:13:13,524
they will be analyzed by Kranios data science

358
00:13:13,524 --> 00:13:16,264
team. This is the team that is utilizing

359
00:13:17,044 --> 00:13:18,345
machine learning algorithms,

360
00:13:19,125 --> 00:13:22,105
basically looking at changes on the PPG signal,

361
00:13:22,565 --> 00:13:23,065
extracting

362
00:13:23,764 --> 00:13:24,264
morphological

363
00:13:24,804 --> 00:13:27,865
features from the signal amongst other parameters,

364
00:13:28,320 --> 00:13:31,299
and then building different models in order to

365
00:13:32,160 --> 00:13:34,980
develop the technology further in order to predict

366
00:13:35,200 --> 00:13:35,700
dynamically,

367
00:13:36,480 --> 00:13:36,980
noninvasively,

368
00:13:38,720 --> 00:13:40,019
intracranial pressure

369
00:13:40,480 --> 00:13:42,420
in either absolute measurements

370
00:13:43,184 --> 00:13:44,884
or indicative of,

371
00:13:45,904 --> 00:13:47,764
high ICP, low ICP.

372
00:13:48,384 --> 00:13:50,245
So we're at this stage now.

373
00:13:50,625 --> 00:13:51,125
The

374
00:13:51,504 --> 00:13:54,545
national and international interest in this project, it

375
00:13:54,545 --> 00:13:56,644
has been overwhelming. You can imagine

376
00:13:58,490 --> 00:14:00,669
the interest, first of all, from the neurocritical

377
00:14:00,970 --> 00:14:01,709
care communities.

378
00:14:02,889 --> 00:14:03,949
So the vibrations

379
00:14:04,250 --> 00:14:05,069
of cranios,

380
00:14:06,250 --> 00:14:07,629
you know, successes,

381
00:14:08,970 --> 00:14:09,709
have disseminated

382
00:14:10,089 --> 00:14:10,589
through,

383
00:14:11,065 --> 00:14:13,464
and there is a very positive feedback from

384
00:14:13,464 --> 00:14:15,245
the neurocritical care community.

385
00:14:15,945 --> 00:14:18,365
There is an active interest of the progression

386
00:14:18,424 --> 00:14:19,164
of the technology.

387
00:14:19,945 --> 00:14:22,424
But interestingly, now we we get a lot

388
00:14:22,424 --> 00:14:24,019
of interest, not peripherally,

389
00:14:24,480 --> 00:14:26,179
but from communities where

390
00:14:27,279 --> 00:14:29,059
injury to the brain is significant.

391
00:14:29,440 --> 00:14:31,539
It could be from sports associations

392
00:14:32,000 --> 00:14:33,299
like the rugby association.

393
00:14:35,199 --> 00:14:36,980
People are interested in concussion.

394
00:14:37,664 --> 00:14:40,644
People are interested in other pathologies relating to

395
00:14:40,784 --> 00:14:45,205
intracranial pressure. So even though Cranios' primary focus

396
00:14:45,424 --> 00:14:46,964
is to deliver a technology

397
00:14:47,745 --> 00:14:48,725
for utilization

398
00:14:49,184 --> 00:14:51,799
in your critical care, You can imagine

399
00:14:52,179 --> 00:14:54,919
that this technology could be used in emergency

400
00:14:54,980 --> 00:14:56,519
care, in ambulances,

401
00:14:57,059 --> 00:14:59,240
in helicopters. They transfer patients.

402
00:15:00,340 --> 00:15:02,759
So it can find this place in many

403
00:15:02,980 --> 00:15:03,480
NHS,

404
00:15:04,774 --> 00:15:07,654
places and beyond. So it's a very, very

405
00:15:07,654 --> 00:15:08,154
exciting

406
00:15:08,855 --> 00:15:09,355
journey.

407
00:15:09,894 --> 00:15:11,495
It's going well, and,

408
00:15:12,054 --> 00:15:14,134
we we are we are very positive on

409
00:15:14,134 --> 00:15:16,214
the outcome. Yes. So, I mean, the the

410
00:15:16,214 --> 00:15:18,454
device is portable, so it could be used

411
00:15:18,454 --> 00:15:20,250
at at the site of an accident.

412
00:15:21,029 --> 00:15:23,850
Exactly. The device being, one, noninvasive.

413
00:15:24,549 --> 00:15:26,789
The sensor, it's just a, like, a a

414
00:15:26,789 --> 00:15:29,769
sticky plaster that it goes onto your forehead.

415
00:15:30,149 --> 00:15:31,830
The back end is a little box with

416
00:15:31,830 --> 00:15:32,730
all the electronics.

417
00:15:33,284 --> 00:15:33,784
Obviously,

418
00:15:34,325 --> 00:15:36,325
from the past few years, we work in

419
00:15:36,325 --> 00:15:37,784
this research environment.

420
00:15:38,164 --> 00:15:40,884
So the technology, it was, connected into a

421
00:15:40,884 --> 00:15:43,524
laptop computer. This is what researchers do in

422
00:15:43,524 --> 00:15:45,865
labs. But now we are sort of transferring

423
00:15:46,004 --> 00:15:47,225
the knowledge into,

424
00:15:48,070 --> 00:15:51,289
a graphical interface. So you will have, obviously,

425
00:15:51,350 --> 00:15:52,570
device with a monitor

426
00:15:52,950 --> 00:15:54,169
to be able to,

427
00:15:54,950 --> 00:15:56,409
see the signals,

428
00:15:57,029 --> 00:15:59,610
see the values of ICP in a portable

429
00:15:59,750 --> 00:16:00,554
way. Yes.

430
00:16:01,195 --> 00:16:03,274
Okay. And then you you mentioned that the

431
00:16:03,274 --> 00:16:06,794
signals are analyzed by machine learning algorithms. So

432
00:16:06,794 --> 00:16:08,815
did you have to develop your own proprietary

433
00:16:08,955 --> 00:16:10,955
software? It was this part of the the

434
00:16:10,955 --> 00:16:11,455
process?

435
00:16:12,154 --> 00:16:14,894
That is correct. I mean, machine learning applications

436
00:16:15,034 --> 00:16:16,879
in health care, they are exponentially

437
00:16:17,259 --> 00:16:18,879
and, I would say, cataclysmically

438
00:16:19,340 --> 00:16:20,800
grown over the years.

439
00:16:21,660 --> 00:16:25,180
It allows people to develop models, whether it's

440
00:16:25,180 --> 00:16:26,160
in biosignals

441
00:16:26,540 --> 00:16:28,480
or imaging, using images

442
00:16:28,794 --> 00:16:30,735
to be able to diagnose disease.

443
00:16:31,514 --> 00:16:32,735
So the team

444
00:16:33,035 --> 00:16:35,855
within the university and later on by cranial,

445
00:16:35,995 --> 00:16:36,495
fortify

446
00:16:37,035 --> 00:16:39,274
the data science part of the team. We

447
00:16:39,274 --> 00:16:41,850
brought experts that they have spent years working

448
00:16:41,850 --> 00:16:42,350
with

449
00:16:43,210 --> 00:16:44,350
predominantly photopredismograms

450
00:16:45,610 --> 00:16:47,710
and be able to develop bespoke

451
00:16:48,490 --> 00:16:49,870
machine learning algorithms

452
00:16:50,170 --> 00:16:51,550
of extracting information

453
00:16:52,250 --> 00:16:53,470
from this physiological

454
00:16:53,850 --> 00:16:55,389
optical signal, the PVG,

455
00:16:56,154 --> 00:16:57,754
and built in models to be able to

456
00:16:57,754 --> 00:17:01,035
predict intracranial pressure. So, yes, it's correct. The

457
00:17:01,035 --> 00:17:03,294
algorithms developed by Cranio is almost

458
00:17:03,595 --> 00:17:05,134
like gold dust to the company.

459
00:17:05,755 --> 00:17:06,255
Okay.

460
00:17:06,875 --> 00:17:08,575
And as you say, you've done some preliminary

461
00:17:08,954 --> 00:17:11,609
tests on patients, and you're starting this this

462
00:17:11,609 --> 00:17:14,250
sort of new clinical trial. What do you

463
00:17:14,250 --> 00:17:16,169
what do you hope to achieve with with

464
00:17:16,169 --> 00:17:18,169
the next lot of measurements? What are you

465
00:17:18,169 --> 00:17:20,569
aiming for? Yeah. Absolutely. I mean, very good

466
00:17:20,569 --> 00:17:22,809
question. Of course, the first study, it was

467
00:17:22,809 --> 00:17:23,630
done during,

468
00:17:24,275 --> 00:17:26,755
the time where the project was within the

469
00:17:26,755 --> 00:17:27,255
university.

470
00:17:27,875 --> 00:17:30,914
It was the first feasibility study on a

471
00:17:30,914 --> 00:17:33,494
probe or sensor technology that was built,

472
00:17:33,954 --> 00:17:35,015
within the university.

473
00:17:35,869 --> 00:17:36,769
Since then,

474
00:17:37,230 --> 00:17:38,130
we've learned

475
00:17:38,750 --> 00:17:39,250
more,

476
00:17:40,269 --> 00:17:42,910
how we can you know, lessons learned from

477
00:17:42,910 --> 00:17:44,450
the first clinical trial

478
00:17:44,910 --> 00:17:47,309
on on a group of 50 patients or

479
00:17:47,309 --> 00:17:47,805
so.

480
00:17:49,565 --> 00:17:52,285
And the second round now conducted and let

481
00:17:52,365 --> 00:17:54,065
is is gonna be led by Crenio

482
00:17:54,365 --> 00:17:56,144
with a more optimized probe.

483
00:17:57,325 --> 00:17:59,025
So after the first experiences,

484
00:17:59,644 --> 00:18:00,465
we've learned,

485
00:18:01,485 --> 00:18:04,070
the technical challenges we had, and we're trying

486
00:18:04,070 --> 00:18:06,330
to mitigate them with the new probe design,

487
00:18:06,710 --> 00:18:08,089
whether it's in the optics,

488
00:18:08,549 --> 00:18:11,589
the configuration of the probe, the physical appearance

489
00:18:11,589 --> 00:18:13,190
of the probe, how it's placed on the

490
00:18:13,190 --> 00:18:13,690
forklift.

491
00:18:14,309 --> 00:18:17,190
So those lessons were learned. So, hopefully, this

492
00:18:17,190 --> 00:18:17,690
round,

493
00:18:18,055 --> 00:18:21,015
we will, mitigate some of the challenges we

494
00:18:21,015 --> 00:18:23,275
had before in the technical aspect.

495
00:18:23,815 --> 00:18:26,474
Also, through the first study, we've learned

496
00:18:26,855 --> 00:18:28,875
the challenges or the problems

497
00:18:29,335 --> 00:18:31,035
with the acquisition of signals.

498
00:18:31,480 --> 00:18:33,640
So the type of patients, how long we

499
00:18:33,640 --> 00:18:34,380
should monitor.

500
00:18:34,839 --> 00:18:36,919
So now we can do the study more

501
00:18:36,919 --> 00:18:37,419
rigorously,

502
00:18:38,039 --> 00:18:38,539
longer,

503
00:18:39,000 --> 00:18:41,259
more supervised in order to acquire

504
00:18:41,720 --> 00:18:42,940
high quality signals.

505
00:18:43,414 --> 00:18:45,434
This time around, we will record

506
00:18:45,815 --> 00:18:47,515
more information from the patients.

507
00:18:47,894 --> 00:18:50,294
We will, re we will look at CT

508
00:18:50,294 --> 00:18:50,794
scans

509
00:18:51,095 --> 00:18:54,294
to see how the scalp density, the thickness

510
00:18:54,294 --> 00:18:55,755
of the skull, perhaps

511
00:18:56,109 --> 00:18:58,029
it will have an impact on the light

512
00:18:58,029 --> 00:19:00,929
between, you know, different ages, different sexes.

513
00:19:01,470 --> 00:19:02,190
We will,

514
00:19:02,909 --> 00:19:04,769
monitor and collect data,

515
00:19:05,549 --> 00:19:08,929
from the commercial medical monitors within neurocritical

516
00:19:09,230 --> 00:19:11,255
care in order to see the relation of

517
00:19:11,255 --> 00:19:13,355
ICP with other physiological

518
00:19:13,654 --> 00:19:16,535
data acquired in these patients. So we are

519
00:19:16,535 --> 00:19:18,714
expanding the acquisition of data

520
00:19:19,015 --> 00:19:21,035
and the knowledge pool about

521
00:19:21,414 --> 00:19:25,480
what happens when a patient's intracranial pressure pressure

522
00:19:25,480 --> 00:19:25,980
rises.

523
00:19:26,359 --> 00:19:28,599
What happens to the blood pressures? What happens

524
00:19:28,599 --> 00:19:31,240
to the other physiological measurements of the patient?

525
00:19:31,240 --> 00:19:33,259
So we're enriching the study

526
00:19:33,720 --> 00:19:35,339
with a more advanced technology,

527
00:19:35,880 --> 00:19:36,539
and, hence,

528
00:19:37,480 --> 00:19:38,539
and fingers crossed,

529
00:19:39,674 --> 00:19:43,035
this would lead to more accurate machine learning

530
00:19:43,035 --> 00:19:46,095
models in relation to capturing correctly,

531
00:19:46,634 --> 00:19:47,134
dynamically

532
00:19:47,914 --> 00:19:50,255
the changes in intracranial pressure.

533
00:19:51,035 --> 00:19:52,880
And you and you said in this, in

534
00:19:52,880 --> 00:19:54,880
these patients, they're all they've also got the

535
00:19:54,880 --> 00:19:58,480
invasive probe. So Indeed. Compare the results and

536
00:19:58,480 --> 00:19:59,859
sort of check the accuracy.

537
00:20:00,240 --> 00:20:01,059
It is fundamental

538
00:20:01,599 --> 00:20:03,299
to be able to compare

539
00:20:03,679 --> 00:20:05,460
our results and our experiences

540
00:20:06,095 --> 00:20:07,714
in changes in ICP

541
00:20:08,494 --> 00:20:11,474
with the gold standard. So we will have

542
00:20:11,535 --> 00:20:12,355
all the,

543
00:20:13,214 --> 00:20:16,414
in real time, the traces, the physiological sick

544
00:20:16,575 --> 00:20:18,755
the signal coming from the ICP sensor

545
00:20:19,170 --> 00:20:22,210
into our own acquisition system so we know

546
00:20:22,210 --> 00:20:22,710
exactly

547
00:20:23,250 --> 00:20:25,750
what's happening to the patient's ICP

548
00:20:26,529 --> 00:20:29,170
according to the gold standard in relation to

549
00:20:29,170 --> 00:20:30,710
our own ICP technology.

550
00:20:31,970 --> 00:20:34,210
Okay. And then, you know, presuming all the

551
00:20:34,210 --> 00:20:34,724
the next,

552
00:20:35,605 --> 00:20:38,244
trials are all successful, how far away is

553
00:20:38,244 --> 00:20:40,884
the system from sort of being used clinically

554
00:20:40,884 --> 00:20:42,424
as as a standard tool?

555
00:20:43,044 --> 00:20:44,744
Well, Kranio is very ambitious.

556
00:20:46,085 --> 00:20:47,944
For those being in the medical devices,

557
00:20:48,644 --> 00:20:52,160
game, it's a long game, usually, medical devices.

558
00:20:54,059 --> 00:20:55,119
But the trajectory,

559
00:20:55,500 --> 00:20:57,759
we're hoping that within

560
00:20:58,299 --> 00:21:00,000
the next couple of years,

561
00:21:00,460 --> 00:21:01,200
we will

562
00:21:01,500 --> 00:21:02,000
progress

563
00:21:03,284 --> 00:21:03,784
adequately

564
00:21:04,404 --> 00:21:07,125
in order to enable us to, you know,

565
00:21:07,125 --> 00:21:09,704
jump all the regulatory sort of COPES,

566
00:21:11,125 --> 00:21:11,944
c marking,

567
00:21:13,444 --> 00:21:16,005
all the standards that are necessary to launch

568
00:21:16,005 --> 00:21:17,784
the medical a medical device.

569
00:21:18,250 --> 00:21:20,409
So we are quite optimistic. We think within

570
00:21:20,409 --> 00:21:21,950
the next two, three years, we'll

571
00:21:22,250 --> 00:21:24,089
be in a very good position actually to

572
00:21:24,089 --> 00:21:26,490
get very, very close to very close to

573
00:21:26,490 --> 00:21:27,150
the market,

574
00:21:27,769 --> 00:21:30,029
all all be well, of course.

575
00:21:30,890 --> 00:21:32,654
And then do you predict will this It

576
00:21:32,654 --> 00:21:34,494
sounds like it'd be sort of relatively low

577
00:21:34,494 --> 00:21:34,994
cost

578
00:21:35,295 --> 00:21:38,015
option if it's just based on optics for

579
00:21:38,015 --> 00:21:39,394
for hospitals to use.

580
00:21:40,255 --> 00:21:40,755
Well,

581
00:21:41,215 --> 00:21:43,295
usually, this is questions that speaks to the

582
00:21:43,295 --> 00:21:43,795
CEO.

583
00:21:46,440 --> 00:21:46,940
But,

584
00:21:48,279 --> 00:21:50,039
I mean, you are right. I mean, the

585
00:21:50,039 --> 00:21:52,619
ambition here is to the primary motivation

586
00:21:52,920 --> 00:21:53,579
of Cranio

587
00:21:54,200 --> 00:21:56,359
is to create solutions for health care. I

588
00:21:56,359 --> 00:21:58,220
mean, developing a technology

589
00:21:58,679 --> 00:22:00,299
which we can help clinicians

590
00:22:01,615 --> 00:22:02,275
to diagnose

591
00:22:03,214 --> 00:22:05,154
traumatic brain injury effectively,

592
00:22:05,934 --> 00:22:06,434
faster,

593
00:22:06,974 --> 00:22:07,474
accurately,

594
00:22:08,575 --> 00:22:09,075
earlier.

595
00:22:09,775 --> 00:22:11,875
It can only yield in the

596
00:22:12,255 --> 00:22:13,634
better patient outcomes,

597
00:22:17,210 --> 00:22:17,710
improving

598
00:22:18,009 --> 00:22:20,890
the quality of life of those patients. We

599
00:22:20,890 --> 00:22:22,589
work closely with the charity.

600
00:22:23,130 --> 00:22:23,529
And,

601
00:22:24,089 --> 00:22:25,150
through those discussions,

602
00:22:25,690 --> 00:22:28,170
they're very impactful for us within Cranio to

603
00:22:28,170 --> 00:22:31,034
see how the patients and the carers

604
00:22:31,414 --> 00:22:33,994
are impacted with traumatic brain injury.

605
00:22:34,615 --> 00:22:37,115
So that one that's one of our primary

606
00:22:37,335 --> 00:22:40,075
motivators. This is what gives energy to Cranio,

607
00:22:40,214 --> 00:22:42,349
really. Of course, as a as a company,

608
00:22:42,349 --> 00:22:45,549
we're interested in being successful commercially as well,

609
00:22:45,549 --> 00:22:48,190
but the ambition here is to keep the

610
00:22:48,190 --> 00:22:50,909
cost, first of all, affordable. We live in

611
00:22:50,909 --> 00:22:53,069
a world that take medical technologies, they need

612
00:22:53,069 --> 00:22:53,595
to be

613
00:22:54,154 --> 00:22:56,234
affordable, not only for the western nations, but

614
00:22:56,234 --> 00:22:58,315
the nations that they cannot afford state of

615
00:22:58,315 --> 00:22:59,214
the art technologies.

616
00:22:59,994 --> 00:23:02,015
So, this is, again,

617
00:23:02,394 --> 00:23:05,115
one of the primaries within Crenio to be

618
00:23:05,115 --> 00:23:05,855
able to

619
00:23:06,154 --> 00:23:07,934
create a sensor technology

620
00:23:08,549 --> 00:23:10,730
that it could be used widely. And

621
00:23:11,269 --> 00:23:14,090
used widely because there is a massive need,

622
00:23:14,230 --> 00:23:17,130
but also used widely because it's affordable technology.

623
00:23:17,590 --> 00:23:19,690
In the commercial journey of cranial,

624
00:23:20,954 --> 00:23:23,134
obviously, The UK is a big market.

625
00:23:23,755 --> 00:23:25,375
NHS, for us, it will be a primary

626
00:23:25,674 --> 00:23:28,315
a primary market. Obviously, our ambitions, they are

627
00:23:28,315 --> 00:23:28,815
beyond.

628
00:23:29,194 --> 00:23:32,015
We want to reach, obviously, the European market

629
00:23:32,315 --> 00:23:34,794
and, obviously, The US market, which is very

630
00:23:34,794 --> 00:23:35,294
big.

631
00:23:35,890 --> 00:23:38,210
So on our target is, at some point

632
00:23:38,210 --> 00:23:38,710
after,

633
00:23:39,089 --> 00:23:41,029
you know, the c marking approval,

634
00:23:42,529 --> 00:23:43,269
is to

635
00:23:43,970 --> 00:23:45,029
tap into The

636
00:23:45,490 --> 00:23:46,230
US market.

637
00:23:47,490 --> 00:23:49,329
So we need to obviously pass through the

638
00:23:49,329 --> 00:23:50,710
pathways of the FDA

639
00:23:51,595 --> 00:23:54,095
approval in order to enter The US market.

640
00:23:54,154 --> 00:23:56,394
So in the sort of short, medium, and

641
00:23:56,394 --> 00:23:57,615
long term, Crenio

642
00:23:58,234 --> 00:24:00,315
inspires by 2030

643
00:24:00,315 --> 00:24:01,534
to be able to penetrate

644
00:24:01,914 --> 00:24:03,054
The US market.

645
00:24:03,514 --> 00:24:06,869
Hopefully, the hopefully, The UK market as close

646
00:24:06,869 --> 00:24:08,150
as 2027,

647
00:24:08,150 --> 00:24:08,970
'20 '8.

648
00:24:09,990 --> 00:24:12,490
So we're ambitious. The team is amazing.

649
00:24:13,670 --> 00:24:16,170
All very passionate about the technology.

650
00:24:18,684 --> 00:24:21,825
So, yeah, that's the sort of endgame

651
00:24:22,205 --> 00:24:23,105
with the company.

652
00:24:23,644 --> 00:24:26,285
It sounds like a really promising tool that

653
00:24:26,285 --> 00:24:27,884
could you know, it has a lot to

654
00:24:27,884 --> 00:24:28,705
offer, Hunter.

655
00:24:29,164 --> 00:24:30,924
So I wish the company lots of luck

656
00:24:30,924 --> 00:24:33,164
for the future. That sounds great. Thank you.

657
00:24:33,164 --> 00:24:34,700
Thank you. Excellent.

658
00:24:35,399 --> 00:24:37,159
Well, thank you very much for joining us

659
00:24:37,159 --> 00:24:39,559
today. Thank you. Very welcome. My pleasure. Thank

660
00:24:39,559 --> 00:24:40,460
you so much.

661
00:24:48,585 --> 00:24:50,924
That was Kranios' chief scientist,

662
00:24:51,384 --> 00:24:51,884
Panikos

663
00:24:52,265 --> 00:24:52,765
Kyriakou,

664
00:24:53,384 --> 00:24:56,605
in conversation with Physics World's Tammy Freeman.

665
00:24:57,224 --> 00:24:59,144
I'm afraid that's all the time we have

666
00:24:59,144 --> 00:25:00,525
for this week's podcast.

667
00:25:01,069 --> 00:25:02,049
Thanks to Pankajos

668
00:25:02,509 --> 00:25:05,250
and Tammy for a fascinating conversation.

669
00:25:05,789 --> 00:25:08,349
And a special thanks to our producer Fred

670
00:25:08,349 --> 00:25:08,849
Isles.

671
00:25:09,470 --> 00:25:11,549
We'll be back again next week. But in

672
00:25:11,549 --> 00:25:14,625
the meantime, do check out the latest episode

673
00:25:14,845 --> 00:25:17,345
of the Physics World Stories podcast.

674
00:25:17,805 --> 00:25:20,704
Host Andrew Glester is joined by three

675
00:25:21,005 --> 00:25:22,065
expert guests

676
00:25:22,365 --> 00:25:25,744
to explore the impact of artificial intelligence

677
00:25:26,309 --> 00:25:27,049
on discovery,

678
00:25:27,750 --> 00:25:30,329
research, and the future of physics.

679
00:25:31,029 --> 00:25:32,569
That episode is called

680
00:25:33,029 --> 00:25:35,130
AI and the future of physics,

681
00:25:35,509 --> 00:25:37,509
and you can find it on the Physics

682
00:25:37,509 --> 00:25:38,409
World website

683
00:25:39,034 --> 00:25:42,095
or at your favorite podcast provider.