 Hi, everyone. My name is Stefan Malmö. I'm one of the founders of Detectivio, also a doctor and a medical researcher. So what are we working on at Detectivio? We're doing software for measuring health with cameras. It enables remote checkups in telemedicine and it improves triage in hospital emergency departments. It does more than that and I'll tell you more in a bit. So emergency department waiting rooms was the place where it all started and the challenge that we identified and set out to solve. And I'm gonna come back to the emergency department waiting room, but first I want to tell you a little bit about why I became one of the founders of Detectivio and why we're doing this in particular. So when I grew up I was fascinated by electronics and computers and did all kinds of crazy things coding and building networks and had some first jobs without any education in IT. And I believe in this crowd and this place that's not unique at all. I guess almost everyone has that background. But it was quite unusual when I entered medical school and did like software development on the side and it's... It gives me a special perspective that I bring into medicine. So after I had worked in several emergency hospitals and clinics I noticed that we were doing all these important measurements. We were making assessments based on important measurements. But for me the available methods to do these measurements weren't good enough and they were hand operated, cumbersome, spread infections, analogue. We had to enter the information by hand into the systems. So I decided I wanted to look for better solutions and so doctors like me have been looking for better solutions and better ways to assess disease, especially in emergencies. But for me it comes natural to look at it from a technology perspective. So can technology bring a solution? Can we improve the disease severity assessment? Can we improve patient and provider experience? Can we improve accuracy in ease of use and patient safety? And at the same time integrate digitally to the healthcare ecosystem. Those were the challenges that we started out with and I realized early on that I wouldn't be able to do it on my own. I needed a more technical founder to come with me or actually I didn't know we were going to founder company. I just wanted to explore this challenge and I was so lucky to find Taha Khan, my co-founder who came at the same problem from a different angle. So he's a data scientist and engineer who did his PhD on medical applications of AI using sensors. So I was really excited when I heard about him and it turned out that his father and brother are doctors. So we could actually speak medically to each other and communicate in a good way and we started out collaborating doing small project and after a while we decided okay are we going to do this as pure research or do we want to build products and change things and we yeah as you figured out we decided on the latter and started the TQU and since then we've added more great people to the team and continue building this a great team to make this technology available in healthcare and beyond. So back to the emergency department. I'm sure that several of you have been to the emergency department for yourself or for someone else who needed help and it's the same all over the world. Chances are that you had to wait for hours and you're thinking like who needs help first. Maybe you were thinking it has to be me. That's usually the case. Could actually be the person sitting next to you but how's the doctor or nurse going to know? How do they know who needs help first? The problem is that in that setting delay causes harm, suffering and sometimes tragically even death. So it's important to get it right and of course there is an established process. It's called triage and it's the goal is to identify who needs help first and it's based on measuring the vital signs, patient by patients and while the patients stay in the clinic repeated measurements help to detect trends over time. So what are these vital signs that I keep talking about? It's actually just a group term for measurements that you've heard about before. Heart rate or pulse, respiratory rate, oxygen saturation, blood pressure and body temperature and these are like the fundamental metrics of human physiology, you could say. So instead of having someone placing different sensors and gadgets on your body with our software and camera-based measurements, instead it would be a quick scan of the face from a one meter distance. For the temperature measurements, we're using an infrared radio metric special camera but all the other vital signs are measured with the standard RGB sensor optics and it makes the measuring the vital signs as easy as using a camera and of course it's a digital so it goes straight into whatever system is used. So how does it work? Input from a camera and then the software does face detection and regional interest detection. It extracts the digital signal and in that signal we can identify thousands of different features that we then use in the AI algorithms and we get vital signs as output. The three last steps can be done locally or in the cloud. So the core activities what we do is like deep-take AI research. We build software, we measure the performance of that software and then we learn from the data. So when we build software, we use digital signal processing, machine learning, computer vision. Measuring the performance is probably where we're most unique because we need to do clinical trials, clinical data collection to get that range of high and low blood pressure for example and we do medical validation because we need medically educated and trained nurses and doctors to do the reference measurements so we know that it's high quality and we have our digital lab to do tests and so on. We're really grateful for the academic collaborations that we have that makes this possible. Now we take the data, train the AI of course, but we can also see it's like a feedback look where we can see how we need to improve the software and then we go back to building new software and so on. So that's what we do. Clinical validation also needs to publishing medical journals, medical papers and journals. So this is a publication from earlier this year published in the journal Infectious Diseases. I'm not going to say the impossibly long title, but to sum it up, we have some promising and robust results even in a challenging environment. So our vision to lead the development and worldwide adoption of contactless health measurements for life critical decisions speaks to the fact that we're not only using this technology inside of health care but we see huge potential of using the same software in many different situations outside of health care. So for example in a car if this software was there it could detect and prevent accidents if there was a health issue with a driver and the same goes like similarly this could be done for pilots or bus drivers, but also in many industries where there are dangerous equipments and so on. So the first step for us is to bring this from the hospitals in one step, small step out into the world to the telemedicine space enabling remote checkups for patient monitoring of chronic diseases. It increases the utility of telemedicine and improves the remote assessment and diagnostic capability and it means that patients can stay at home while getting a high-quality assessment on the right health that they need. So that's why I'm really proud to launch our API software for health measurements in telemedicine as a partner program where we work with selected partners to integrate these measurements in the telemedicine experience and it's really like we're closing the last mile for telemedicine to be able to deliver true health care and I just want to leave with one final note like the next time you talk to an online doctor or the next time you're here an ambulance speeding towards the hospital. I want you to think about the important measurements that you need to do and hopefully you'll think about the amazing technology that you heard about today. Thank you.