British technology is looking to the future of medicine, in today’s podcast with Mark Hinton, Chief Technology Officer, Lucida Medical. The firm has made great strides into using artificial intelligence (AI) to process prostate cancer scans and provide useful information to medical professionals when diagnosing patients.
TechBritannia Co-conspirator Rose Ross discovers that early detection of cancers can have not only better outcomes for the patient, but also for the hospital, potentially leading to faster care and cost savings as well.
Mark also talks about how this is just the beginning for AI with many more cancers to focus on and the possibility that one day maybe systems will be able to help improve themselves, taking them beyond relying on human training. Watch the full podcast here:
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Rose Ross: Hello everybody and thank you for joining us on the #WeAreTechBritannia show, as a podcast and video. I’m delighted to be joined here today by Mark Hinton who is the Chief Technology Officer for Lucida Medical. Hello Mark, nice of you to join us.
Mark Hinton: Hello, it’s very nice of you to have me here, thank you.
Rose Ross: Fantastic, well perhaps you could give us a bit of an insight into what Lucida is, what it’s doing, what your role is and your background. Could you maybe kick-off with a little high-level view of what Lucida Medical is all about?
Mark Hinton: Absolutely. So, we’re a Cambridge-based startup using artificial intelligence, and our focus is on medical imaging and in particular MRI, so it’s where you get pictures of the inside of your body in 3D. We take those images and we use the latest machine learning models to identify and assess potential cancers in patients. We use that to support radiologists so they are better at detecting the cancers potentially, and it just gives them more information to understand how serious the cancer is and what the next stages should be.
That’s essentially what we do, so we have a team of very smart machine learning engineers, my role as the Chief Technology Officer is apparently to look after them and keep them doing the right things. We are a young company so we’ve grown very fast, we’re now about 10-12 people, at the beginning of the year we were one person.
Rose Ross: Wow!
Mark Hinton: So, we’ve grown very fast, we have got our software approved so it can be used in clinical practice, and we have partnered with GE, they have an accelerator programme helping startup companies, AI companies in the healthcare sector to integrate into hospitals and clinics, so we’re working closely with them and they’re supporting us in how to do the integration, because as you might expect, it’s a hard task to create a very smart piece of software that can do things that a radiologist does and will help a radiologist do some of those things faster or better.
And then it’s a whole other thing to be able to make it useable, so to be able to put it into the hospital, that the radiologist can have access to it, the radiologist knows how to use it, all of these things and that’s where we are partnering with GE to build that part of the technology now we’ve built the core part which does the clever stuff, and then we have to do the other clever stuff.
Rose Ross: Fantastic, well that sounds quite a fantastic challenge for a CTO. So, you’ve been with the company for five months, and obviously your background is in technology in imaging; perhaps you could just give a little bit of an overview of how you’ve got to be at Lucida, and then we can look at how things will be moving forward. And, perhaps a little bit more about how things are going on the accelerator and what you’re picking up from that.
Mark Hinton: Absolutely, yes. It’s fair to say my background is in technology, I’m actually a physicist and a mathematician originally from academic days. I’ve been around a long time so I’ve worked in multiple industries, it’s been the last 10 years or so that I’ve focused on life sciences and in particular on advanced imaging and the use of algorithms and again machine learning to interpret those images and to get more out of them.
So that helped to bring me to Lucida because I’m one of the few CTOs that already understands how MRI works, that understands, well to a certain extent, that has worked with imaging, has worked with machine learning, and has also just worked on general enterprise-level software using the latest technology. So, I’ve applied my skills in the past in investment banking, in transport logistics, in aerospace. I can bring experience from outside which is always useful I think, to understand the way the same sort of problems might have been solved in a different industry in the past and bring that to bear now.
So, that’s what’s brought me here, that’s why I think I can do a good job. I have a background in startups as well, I’ve helped to take startups from conception through to exits in both life sciences and again in other industries. It’s a broad brush that I bring but hopefully also with the people and management skills to build a young team very quickly. We have a very young, diverse team, the only things they have in common really is that we have this passion for technology, machine learning, for clinical advances.
Rose Ross: Well also the challenging time to be building that team as well because I should imagine you’ve not had an opportunity to spend any time together. Diversity is important I think within this kind of environment, particularly because you need those different viewpoints to work on stuff like this and be as diverse as possible.
Mark Hinton: Yes, the current state of technology such as we’re using now has obviously been absolutely essential to do anything. We do miss the… well, I say we miss, we know that we miss the opportunities to be together in one place with flip-charts and post-it-notes to really brainstorm the designs and algorithms. I would say we’ve never actually done that in this team, so there are… I have physically met pretty much everybody but from March when I started until the last couple of weeks, then yes, I’ve got to know people very intimately on Zoom and Teams but not physically.
In fact, I started my position when I was in Brazil, so the first six weeks or so I was working from Rio and from the northeast of Brazil. So, we can do it.
Rose Ross: I noticed that on your LinkedIn profile, so I wasn’t sure whether you were actually based in the UK working on that type of stuff. But that’s the way things are going and lots of people… in reality we all know now that you can still be incredibly effective without physically being anywhere. Although, I think we are about ready. aside from all the trying to gather for football matches, also looking to spend more time together to bring that social interaction back into things. So, you guys must be looking forward to doing that at some stage, to bring people together.
Mark Hinton: Yes, I think every company has to work out, or every organisation is working out how to do that, as are we. I don’t think we will have everybody in the office every day, ever, we always have an element of remote working now. If anything good has come out of these last 15 to 18 months it has been that we’ve demonstrated beyond doubt that you can work remotely, you don’t need to be at your desk in the office to be productive, you can be at your desk in your kitchen, dining room, or spare bedroom and be productive. That’s been proven beyond doubt now, I think.
Rose Ross: And it’s obviously brought some benefits from a work-life balance perspective for a lot of people. Although I think everybody is kind of keen to get back in to the office perhaps. So, a hybrid seems to be the direction.
Mark Hinton: I think a hybrid thing, it depends a bit on what your commute is like. If you live close to the office…
Rose Ross: In Brazil I probably wouldn’t want to come in a couple of times to the office if I was living in Cambridge.
Mark Hinton: Well quite, yes it would be a bit of a trek.
Rose Ross: It would be, yeah you’d be complaining, although you’d catch up on a lot of reading I guess on a plane, but there we go. So, you’re focused on MRI imaging, you’re focused on obviously both the diagnostics and obviously looking at the progress of treatment and hopefully that treatment being successful from an MRI perspective. Are there any particular cancers that you’re focused on, does that make a difference to the AI? How does that all fit together?
Mark Hinton: Our first product is focused on prostate cancer, so it takes prostate images and then obviously processes those. We are working on other cancers, now we have launched prostate our development programme is looking at other cancers, and in particular we’re looking at a thing that’s called whole-body MRI and is actually imaging from about the base of the neck down to the thighs kind of thing, so you get an image of the whole body and then you can look to identify cancers in the lungs, liver, kidneys, pancreas, and also in bones. Quite a lot of cancers spread from the organ to the bone and you get bone metastases as they’re called, and they’re quite hard to pick up in MRI but we’re working on doing that.
So, that’s very exciting for us because that could be a whole-body screening MRI that people could have whatever, once a year or when they get to a certain age, at certain age trigger points just go and have it, it would take 30 minutes, there’s no radiation associated with it or anything. Then you get an analysis of all the organs that are most likely to become cancerous, and the key thing is of course that the earlier that anything like this is detected, the much-much-much greater chance of survival and full remission is.
We haven’t got there yet, I must stress that, but it’s something that we’re working towards and that would be one of the visions of the company long-term would be to be able to offer that kind of a service.
Rose Ross: And that aspiration obviously has to be important. I think one of the things that personally I was involved with was Bytes for Heroes, which was in the early part of the pandemic when we were looking at tech supporting the NHS and doing a bit more than a clap on a Thursday evening. We did some work with Royal Marsden around the whole element of that, and certainly we were seeing that there was – I’m not saying the Royal Marsden was saying this specifically – but I think that the general understanding is anything of the scale of a pandemic is going to impact all other aspects of treatment and diagnosis. So, anything that we can do right now to help speed up that process, from identifying an issue right through to ensuring that you can spot whether things are working, hopefully working, or not, the better.
So, you’re starting with prostate cancer, that’s a big theme in tech industry on a personal note because obviously we still are, even though you’ve talked about diverse teams, there is shall we say a bit of a slant towards gentlemen being part of the tech environment, so obviously it’s important on that level as well. I personally have been impacted, my father died of pancreatic cancer and unfortunately by the time they were aware it was way too late for him; it’s a long time ago. But anything that people can see that will have a positive impact, and basically what you’re saying is AI is not here to replace what’s happening in the Radiology Department right now but is to augment and stand alongside what’s happening there.
Mark Hinton: Absolutely. I think it’s very much a bit of an old phrase, but it’s a balanced scorecard assessment I think in the way that the technology impacts. There are some hard financial things, if you can choose cheaper treatments then you can save money, that they’re still going to be affective because you’re using AI. But there’s also the general quality of the outputs from the radiologist, if they can be improved or if you can use AI perhaps to help the less specialist radiologist.
So, we’ll have hospitals where the radiologist may only do a handful of prostate examinations per year even, and then there’ll be other radiologists in more specialist hospitals might do 1,000 or even 2,000 in a year. So, obviously the one that does 2,000 a year is very good, they just get better the more of these things you do. The one who only does a small number they might not be quite so good, then if technology like ours can help them to raise the quality of the reports that they produce, and the findings that they get, then that’s a very cool part of how technology can help.
But definitely, we’re a decision-support system rather than a decision-making system. It’s going to be a long time before you go for an MRI scan, either with our software or whether you go by a competitor’s, automatically without a radiologist looking at it, or without an oncologist looking at it, you’ll get a message saying you’re due for this treatment because of the results of your scan. That’s a long-long way away, but hopefully it might actually never happen. So, it’s definitely supporting the radiologist making their life easier.
It is about productivity and if today a radiologist takes 20 minutes to report on an MRI and using our software they could do it in five minutes, that obviously sounds good and it actually is good, but from the poor radiologist point of view he may just be asked to do four times as many reports as he currently does. Ultimately you try to reduce the number of radiologists, which might not be a good thing if we actually have a big shortage of it, but simply churning out more reports without improving the quality, without having good quality control checks, I don’t think is actually a great help. But producing better reports in the same time period or a bit less, I think that starts to add up to a lot of savings, or if it frees the radiologist up to do other things.
Rose Ross: Looking at other aspects, if you talk about potentially automation as being something in this area, because what I understand is, and I think this is an interesting concept, because the radiologist will look at the bigger picture whereas anything that you’re producing at the moment, from what I understand, and please correct me if I’m totally wrong with this; is that it will look for one thing, so it will look for the prostate, yeah?
Mark Hinton: That’s true.
Rose Ross: For example, and it will pull all the things together and go, that’s an issue – that needs to be looked at, because effectively it’s like alerting people. But all of these things as you say, particularly I would have thought with the complexity of this sort of ‘from neck to thigh’, there’s a lot of information that you’re going to be looking for there, and particularly with something like that there is going to be a need for the human element very clearly, to look at all aspects in the diagnosis and the wider landscape.
Yes, I’m afraid I’m a bit of a fan of House, and we all know that a part of the problem with diagnosis is you always assume that it’s one thing that’s wrong.
Mark Hinton: That is true, yes
Rose Ross: As soon as there’s another thing wrong, he was always very good at looking at the bigger picture and going, ‘Yes, you’ve got all these weird symptoms because you’ve actually got two different things that are causing the problem’. And that’s life, it isn’t just ‘Please take a ticket for your illness’, and that is all you will get.
Mark Hinton: Exactly, yes.
Rose Ross: … whatever you get, and that hopefully is the minimum amount and nothing ideally, but obviously we want to ensure that that type of engagement with the medical profession gives the medical profession the best chance of diagnosing you as a human being, not just as one particular illness or condition.
Mark Hinton: Exactly yes, yes. And there’s a whole discipline out there called health economics which tries to look at all of those, takes lots of factors into account to understand what the overall economic impact of introducing new technology, new processes or new drugs, whatever it might be, not just how much does it cost per patient, but everything that comes out of that, both in saving and societal.
Rose Ross: Yes exactly, if we have healthy happy individuals we should be a more productive nation, or at least happy… they say if you’re happier then you’re a bit more productive. So, I don’t know, I like to think that’s the truth, I don’t want to be miserable and very unproductive! Yes, absolutely, please send me the statistics that hopefully say that’s correct.
Mark Hinton: I can imagine there might have been a bit of lack of productivity earlier today after people had a few more beers after the football match last night.
Rose Ross: Well, I’m alcohol free at the moment so it definitely wasn’t me, but I was in a pub last night and I have to say yes, there was a lot of happiness, shall we say, sloshing around in beer glasses at the time, and a lot of running around, yeah. Although I was very happy, I did predict the score, I did say that Denmark would score twice and England once, of course. But I didn’t realise Denmark was…
Mark Hinton: You got it the wrong way round, you got it exactly the wrong way round. But very good.
Rose Ross: No I didn’t because Denmark did score our first goal.
Mark Hinton: Oh, that is true. Alright, okay it was an own goal, yes.
Rose Ross: And Sterling did do a good impression of making it look like it was him! In fairness, but great job lads keep up the good work. So, we’ve just time-stamped ourselves to when we’re recording this! I’m not sure we’ll turn it around. Hopefully, we’re still smiling when we’re thinking about football when this (Euro 2020) is over.
So, we’ve looked obviously at the medical side of things, and you mentioned very briefly earlier that you guys, first of all that you’ve come onboard and the team has grown hugely, exponentially from 1 to 12 in around six months, that’s huge. Obviously, getting onto an accelerator programme, I believe it’s the Edison Accelerator programme run by GE?
Mark Hinton: Yes. That’s right, yes.
Rose Ross: And obviously, GE are big on imaging, so hence they’re being a great fit. So, tell us a little bit about the support that you’re getting, because I did go and have a little snoop and you’ve talked about the technical element of it. But obviously it’s exposure to GE’s client base as well which is going to be invaluable.
Mark Hinton: Hopefully so, yes. So, in terms of practical support they have programmes we can join and are part of, around defining the market, whole marketing processes and sales, so that’s useful, I have some experience in this but you can never know too much. So yes, we will be trialing the software, demonstrating the integration with hospital systems through them, working with them, so they provide the plumbing, as it were, into the hospital systems and we provide the AI solution and generate the reports to be sent back.
So, that goes on during the programme and we will demonstrate the technical feasibility of that part, but then that also puts us in touch with the hospitals that we’re working with, that GE are working with. So, we’re talking to radiologists there, we’re talking to their project managers, so we’ve got that introduction to a very significant NHS trust, and it will be a showcase for us, so hospitals can see how it will integrate, what they need to do, what they get out of it.
Hopefully, at the end of that we’ve got a case study that we can demonstrate to people, they can see how well that’s performing. That supports us with the other work we’re doing around further validation of the software. So, actually getting more and more real-world cases that we can process with the historic data in effect, so we can compare with what the actual outcome was.
We’re basically trying to predict the cancer and at what stage it is, just from the images rather than having to do a biopsy. The imaging is not intrusive, the biopsy obviously a needle goes into you and some tissue is removed, so that’s much better for the patient not to have the biopsy, and the biopsy is expensive. So, it all dovetails together.
Rose Ross: Well you’ve got to send it to a lab, somebody’s then got to look at it in the lab; whereas this can be effectively instantaneous couldn’t it?
Mark Hinton: Pretty much yes, at least in a few seconds which is pretty much instantaneous.
Rose Ross: Oh it will do for me, that’s instant. That’s faster than I can make a cup of tea, so that’s instant in my book.
Mark Hinton: Exactly, exactly so. And it gets us exposure as well, we’re introduced to people, people are hearing about it, yes it’s raising our profile and gives us very positive awareness.
Rose Ross: Obviously, GE is a global brand, is that going to give you exposure beyond the UK as well, through their network?
Mark Hinton: Definitely. Yes definitely. GE is particularly strong in the United States, the United States at the moment is the biggest single market for what we’re doing, and GE has a lot of presence there. So, there are lots of information systems in a hospital, there’s the systems that store the medical images and we need to be connected to that. There’s a system that has the individual patient records and we need to be able to get some information out of that, the results of blood tests and that sort of thing. And there’s a system that manages the imaging process, manage the appointments and where the report will go from the radiologist, and we have to have integration to that.
But in many hospitals, in the US in particular and quite a few in the UK as well, all of that will be supplied by GE, GE have solutions for all of those things. Then we have, in a sense, easy access to those hospitals, and GE already there, there’s going to be more customers. So, if they promote us as an add-on for their things then that can be a good and effective route to market, potentially.
Rose Ross: Definitely a very good foot into the lab, I suppose, or foot into the radiology room.
Mark Hinton: Yes, it’s a very good link in, this. And we’re also learning. yes it’s generally how you have to do that. so for hospitals that are not GE.
Rose Ross: So, how long will you be on the programme for, how long does it run for?
Mark Hinton: It runs until about the end of the year. There’s an onboarding process, we’re now underway, we’re fully functional; it’s about six months.
Rose Ross: So, it’s early days but you’re obviously hopeful that this is going to be a very positive…
Mark Hinton: Oh yeah, it’s already positive, yeah. It’s already doing good things for us.
Rose Ross: What else can we see coming out of Cambridge, or where everybody is throughout the UK right now, what kind of stuff? Do you want to say anything more with regards to the AI and machine learning aspect of it, because obviously that’s a very interesting area. I know that this can be a bit of a contentious subject from an ethical perspective, and obviously the medical profession keeps a very close eye on such things. I think that you’ve obviously mentioned that this is not designed to replace the human view of information, but to provide additional eyes, effectively, even if they’re not actually human eyes, to identify things and speed things up, and maybe spot things that don’t get spotted by the human eye.
Mark Hinton: We have a vast amount of data being collected and has been historically collected, and the process has been gone through to really understand that data to make it accessible to research companies, to companies like ourselves but in a correct way; so, to comply with ethical considerations, to comply with GDPR, to have the data with informed consent, and of course we know there are some political things going on at the moment around GP’s data, and whether you have to opt out of that data being potentially used or not. That’s a short-term moral-political challenge, obviously we don’t really have anything to do with it specifically.
But those will be overcome, then we’re going to see more and more data available, and then the use of machine learning to look across that data. There’s a phrase, multiomics, to look across all of the data, the genomic data, the radiomic data, so data taken from images, there’s other ‘omics. There’s proteomics, what proteins your body produces, that kind of stuff, and this becomes a vast amount of diverse data, different dimensions of the data that you can glean different pieces, you can get different conclusions from. It’s been used a lot now to combine definitely genetic data, genomics, with imaging data in all sorts of diseases, not just the cancers that we’re looking at but across neurodegenerative diseases, dementia, MS, across arthritis for instance and some of the rare terminal diseases like muscular dystrophy.
So, the potential is there to have this much-much bigger picture and understanding of the disease mechanism and the precursors to the disease becoming apparent, so that we can screen people very-very early across a whole range of diseases. Also, perhaps you could address those diseases without expensive treatments simply by lifestyle, potentially. If you’re prone to arthritis then maybe you just make sure that you have a healthier lifestyle, maybe part of it is to make sure that you have a healthier diet with lots of olive oil, oily fish and lots of fresh vegetables, and really more than normal people should, avoid processed foods, high salt and sugar content foods which might well help.
So, there’s a whole way which we can help society to understand where everybody is at and what’s the best thing to do, and of course that comes with great risk as well, one thing in particular of course if you’re in a health-insurance-led country then knowledge about your genetic disposition, or what the imaging says about your likelihood of contracting certain diseases going forward, kind of smashes your health insurance premium if you’re not careful, if that’s allowed. So, there’s a lot of moral stuff that needs to be worked on in…
Rose Ross: Well I don’t like that last bit; I think that there will probably be some things stepping in to say, that’s just an indicator for me and if I’m addressing it then it’s not really relevant from a health insurer’s perspective. Not that I’m concerned myself but, goodness knows what comes up on my early warning system.
Mark Hinton: Yeah, well we have to see. But it needs to be thought about as we go through these things.
Rose Ross: Yes exactly, exactly. But the whole AI and machine learning element could be really very instrumental into accelerating the preventative, versus the having to deal with once you have those diseases. Which obviously would be great for the individuals involved to make good choices, informed choices I think is probably a better way of doing it and saying, ‘You’re about to hit your threshold for this, you might want to steer things in this particular direction to remove that issue somewhat from the likelihood of you being impacted severely by it’.
Mark Hinton: Yes, and I think even just being able to put people onto some kind of surveillance, so already we have programmes where certain people, often because of family history will have MRIs or will have mammograms on a regular basis. But with machine learning this kind of data, you can target those people much better. So, you would not have to have so many people on some kind of active screening programme, which would save money, while those ones that were on the programme you would catch early and have much better options for treatment and much cheaper treatment.
Rose Ross: And much better success rate, basically.
Mark Hinton: Basically it’s a better success rate, yes.
Rose Ross: Yes, which I’m sure from most people’s perspective if that’s going to be unfortunately the way that the dice land, they would prefer to know that that’s actually an issue.
Mark Hinton: I guess so, yes.
Rose Ross: Yes, ultimately I guess. Anything else that you’d like to share about the overall – not just what you guys are doing but perhaps the bigger picture with AI and machine learning in the medical profession, or just generally in life? While I’ve got an expert in this area I’m going to pick your brains!
Mark Hinton: We’re seeing some trends and I think there’s a couple of breakthroughs that will happen at some point in the not too distant future. The trend is that we are now a data-driven economy, data-driven society, so there’s more and more data and it’s going to be applied in more and more ways, different machine learning models will emerge to deal with different types of data. A whole bunch of stuff has emerged with image processing, so medical imaging has driven a lot, driverless vehicles have driven a lot in terms of image processing, image recognition, image tracking, there’s a big overlap in terms of the core algorithms.
We are still largely at a point where we have a bunch of underlying algorithms and we apply those algorithms to different scenarios; to image processing, to speech recognition, to object tracking, to classifying people by their browsing behaviour, it’s like algorithms are tuned directly to those things. While each application is basically unique, we create a host of one-trick ponies; in terms of it being AI we don’t really have AI, we have a whole bunch of very well-tuned algorithms to wrap and input to an output. We haven’t got a generalised model yet, but I think that is something which will come along at some point, or more generalised model and that will be much more powerful and much more generic, possibly much more scary, but it will work more like our brains work and less like how a calculator works.
I saw an interesting thing with Jeffery Hinton recently who was one of the very early pioneers, father of machine learning, and …
Rose Ross: And I think we’re supposed to say that he’s not a relative, so you’re not name-dropping.
Mark Hinton: I’m not name-dropping, he’s not a relative.
Rose Ross: Specifically like your brother or your dad or something!
Mark Hinton: No, no. But he did recently say he thought the current approach isn’t quite right and he said he hopes there’s some PhD student somewhere who’s looking at the work that I did, realising it’s not the right way to do it, and is going to come up with a whole new way and then that will revolutionise everything. I look forward to that day, it will be great. I love what we’re doing at the moment but that generalisation to solve multiple different problems isn’t really there yet as far as I’m concerned.
Rose Ross: We need some new thinking, perhaps it might be one of your… can we machine learn that though? That’s the question, could we set a computer the task of working out how to make itself smarter?
Mark Hinton: It’s an interesting question. Well, sort of, sort of, there are models.
Rose Ross: Or would it implode thinking about that, because it will be like, ‘Oh no, that’s just too much to think about. Sorry I can’t do it’?
Mark Hinton: Well Google did a thing, Google Go where they trained a machine to play Go, the game with the squares and the counters, against, they used lots of human matches, the outcomes of lots of human matches to train it, and then it beats the No.1 Go player in the world over a series of matches. But then Google went one step further, they said, ‘That’s all very interesting but we’ll have a try at a different approach’, and they created two machines.
They encoded those machines with the rules of Go which are quite simple, then they got the machines to play against each other, both optimising themselves to achieve the goal of winning the game. So they let them go, played each other a few million times or whatever, and then the best one of those two, which presumably was just random that one got to be better than the other one, played the original machine learning algorithm that had beaten the human player, and basically beat it every single time. So, yes.
Rose Ross: There we go. So, I think they are possibly the solution to their own problem, we shall see, maybe we’ll chat about that once things have progressed a little bit more. So, Cambridge we’re going to be hearing a lot more about what you’re doing and hearing a lot more about some of the progress that’s being made in a very important area of the medical profession.
So, I’d like to say thank you very much to Mark Hinton who is the CTO at Lucida Medical. Thank you for joining us.
Mark Hinton: It’s been an absolute pleasure, thank you so much.
Rose Ross: No problem and thank you all for listening and watching to ‘We are Tech Britannia’, the podcast that looks at what’s happening in technology and biotech in the UK.
You can follow us on Twitter @techbritannia and also find us on LinkedIn, and online we’re at www.techbritannia.co.uk.
Thank you very much, we look forward to your feedback and hopefully you’ll join us again soon. Thank you.