Sadeq Ali, AccurKardia - Analyzing ECG Data in Real Time | LSI Europe '22

Transcription

Sadeq Ali  0:03  

Hello, everyone. Good being here today. So one of the major themes that we've seen in this conference already is the coming together of digital with analog and physical. We've seen a proliferation of, of health sensors embedded into everything from watches through to automobiles through to bio fabrics. This is a theme that was touched on this morning by the folks at Medtronic in particular, when they spoke about the coming together of these two worlds, the physical and the digital. This is a theme that AccurKardia very much, in some sense plays into the specific challenge, though, that this creates is while it does solve for some problems, it also creates new problems. If you think about the proliferation of sensors out there, the amount of data that has been created today as a result of the sensors, is exponentially larger than what was available to clinicians, all through up to this point. This data, in some sense having too much data is is potentially as as not useful as having too little data. In fact, in some contexts, you can make the argument that having too much data is perhaps worse than having very little data. And this is the context this is the the the overall larger problem that AccurKardia as a company is trying to solve, we're essentially building a solution that allows the world to take the data take all of the noisy data in particular, and identify the signal from that data to make this data actionable to make this data reliable, and to basically allow this data to enable efficient clinical intervention and save millions of lives. That's essentially our mission. And that's what we're working towards. Let's take the context of where we currently play, which is in the cardiovascular context. This morning, when actually yesterday, when the CEO of Siemens Health was speaking, he specifically called out three major challenges in healthcare. He mentioned the challenge of having very limited resources and the resources being stretched just not enough skilled resources and healthcare. He spoke about the challenge of 3 billion people in the world not having access to quality health care. And he also spoke about the challenge of having the pandemic of, of, of cancer in some sense that we're dealing with. Well, there is another very large, perhaps even larger pandemic than cancer, which is cardiovascular disease. In the US alone, approximately 16 million people suffer from a cardiac arrhythmia, which can be a precursor to heart attack to a stroke. That's about 5% of the US population, about 650,000 people die annually due to a cardiovascular issue. And there are solutions out there that enable better tracking, better monitoring of cardiovascular disease. But nonetheless, we're very much at stage one of the process of tackling this problem. We built a solution called Accur AI, which we think of in some ways, as an operating system for ECG hardware. The device that we work with are everything from ambulatory halters. And event monitors, through to variables through to even bedside monitors, the context in which it can be used is very, very wide ranging. The major advantages of our solution relative to other options out there, potentially the market today or beyond the fact that it's device agnostic, most of the solutions out there vertically integrated into a specific hardware device, or that it's near real time. It has clinical grade accuracy. And it works on a very, very low compute footprint. Now, if you think about it, the people focusing on the in the accuracy side of it on the device agnostic side of it, which is of course important, but I think what's equally important is the ability to process all of this data in near real time and the ability to do it on a low compute footprint, particularly as you think about the use cases in which healthcare is going to move towards and these health sensors are going to be moving towards, you know, think about the context, for example, in an international space station or even further in space exploration in those contexts. You don't have access to AWS and very heavy cloud compute, you need to have the ability to process information at the edge on on on potentially very, very, very low compute footprints. Take the context of autonomous driving, we have vehicle equipment manufacturers like Bosch, for example, embedding ECG sensors into the steering wheels of automobiles. Think of it in the context of, of an autonomous vehicle, where you potentially need to know if somebody is falling asleep. The way that's done today is through computer vision. That's not necessarily the best way of determining that a better way of determining that is to see if someone's heart rate is dropping, of the person is going to go into potential sinus bracket cardio. Now, if you were in that context, the ability to run that software on the car computer. And you know, in contexts where you don't have cloud connectivity at all becomes extremely critical. So these use cases that are not your typical traditional ECG analytics use cases, applying existing solutions for this doesn't work, you need to have a solution that can work in near real time, if not real time, and very, very low compute footprints. So our initial product is Accura ECG, this is more for the event monitors and Holter space. It is a cloud based web application that is going through an FDA approval process. As we speak, the this particular product is going through a 510 K process. It's a Class II medical device, and software as a medical device essentially. And at the point that we do get approval, it will have the broadest arrhythmia coverage of any similar device out there in the market, which is significantly greater than then really anything else out there as far as what they can cover in terms of arrhythmias. In terms of other products that we've been building. We've built a an application that functions alongside variables we've built it in, in the beginning with the Apple Watch, but we've since adapted it to other consumer devices as well. This particular product was built within about three months of Apple making the data available to developers, this device provides a very fast turnaround time and click clinical grade analytics in the context of consumer wearables. And we've developed a workflow along with it, which allows you to potentially make the entire workflow reimbursable which which isn't the case today with with Apple watches and the the reports coming out of the Apple Watch. Where are now in the process of building out an API based software, which will allow any device manufacturer that is building a hardware ECG based hardware device to integrate directly into our workflows, this particular product, we are doing some r&d at the moment, we're working with some some hardware partners that are there right now in the research phase with us. And we will hopefully commercialize with them as we get our regulatory approval in terms of the Apple product, the AccurBeat product that you saw on the previous slide. If anyone wants to see a demo of that product, we can do that after this presentation. Dr. Naveen Razvi who is here who's our chief medical officer and a cardiologist here based out of the UK. He was part of a group including hid lab in New York as well as SUNY Downstate that have done some clinical research on that as well that will be published within the next quarter or so in GIMR formative so we are pursuing the clinical research path even on the on the accurate product. In terms of where our devices currently operate in terms of the market segments that we operate in. We operate in the outpatient ambulatory segment with the accurate ECG device. We operate in the telehealth segment with the AccurAPI device. And we have a roadmap in place to do this on device in the context of a bedside monitor in the inpatient telemetry segment. The AccurBeat and accurate API products are also very relevant to the DTC segment as well as non clinical use cases. The team itself is comprised of entrepreneurs, health, tech entrepreneurs as well as entrepreneurs from from from the technology space in general AI researchers, electrical engineers, Dr. Razvi who is a cardiologist as well as a panel of other cardiologists, including Dr. Rajesh Rostova, from Scripps, Dr. Eduardo Hernandez from from Texas heart, and Dr. Andy Petke, who is both a cardiologist as well as actually a doctor as well as an entrepreneur from UCSF. The company was formed in 2019. Though the research that led to the formation of the company started all the way back in 2015. We did an initial precede raise of about 1.6 million, starting in October of 2019. We didn't raise any money after that until about a month ago, when we started our seed raise, we initiated a seed raise of about 2.5 million of which we've raised about 1.3 already, and not just committed, but that's actually been transferred over to us. We're continuing to do the seed raise. And we hope to close the 2.5 million target within the next two months or so, in terms of where we plan on using these funds, we're going to be using them primarily to continue to build out a product roadmap to continue to commercialize and build partnerships with hardware manufacturers in the ECG space and potentially outside of the ECG space as well. And we're also going to go pursue additional regulatory approvals, as well as additional clinical trials that are clinical validation that Dr. Rosemead will lead up. That's it from my end. Thank you very much for your time and if you have any questions, please feel free to catch me I'd say thank you