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.