--founder. We're based in Toronto. We're a medical device technology company. Been around for a little bit over a dozen years. I'm going to walk through. This deck is also on our website, so you can download it, so you don't have to worry about taking photos. I'll speed up. There's a lot of slides in here, I don't need to bore you to death on all of them, but please ask questions as we go through, if you like. I'd much rather be interactive than not. The problem that we are working on is to eliminate second surgeries, cancer surgeries, right? This idea of the surgeon doesn't get it all, and they have to go back in. You, as a patient, have to go back for a second surgery. Totally horrible. Costs money, traumatic for the patient.
Not that great for the surgeon because they're trying to do a good job also. Very hard problem to solve. Occurs in all types of cancer surgeries. We're starting with breast cancer, although the tech does work across multiple types of different cancers, which we can talk about also. Here's just a quick summary. I'll go through this, and then we'll dive a little bit into this. We do have the first FDA-approved AI imaging device for breast cancer surgery. There's nothing else out there that does this. The problem is big. No one's been able to solve it. We recently got FDA approved. We have a Class III device out there on market with the right indications in breast cancer, with a big clinical trial that does show improved surgical outcomes. That's the product we have on market.
It's a next generation of a previous product that we have. It's a very large market. Just looking at breast cancer surgery alone, over 300,000 a year in the U.S. alone. The reoperation rate, which you will see shortly, is anywhere between 20%-25% of those women have to come back in for a second surgery. You can run the numbers on 20%-25% through it. There's a lot of women out there having to go back again for another operation. We have a recurring revenue model, which is nice for the obvious reasons. I won't harp on it. I'll show you in a moment. Very high gross margin. We're in commercial phase right now since we got FDA approval, so we're in the process of ramping up. We've got a lot of demand for this. Our constraint right now is a sales force constraint.
Building out a sales team, that's going to be the right sales team to take us there. That's really what we're working on within the company. We do have a very good competitive moat. The technology, couple different variation, not variations, couple of different technology pools, let's say, on the imaging system, very well-protected, as well as on the AI library. We have a very big data library. Again, a few statistics I'm just going to. We've had on a previous generation device, we've got over 4,000 procedures on that. We have some of those are on the new generation as well, since we got approval. Several dozen hospitals around the United States. Large image library, over 2 million images in that of healthy tissue and cancerous tissue.
We talk about multiple surgical specialties, so although our focus is on breast cancer, we do have data and some papers and research ongoing right now across different types of cancer, head and neck cancer, bladder cancer, lung cancer. So expansion out of just It is a large market, 300,000 breast cancer surgeries, but you take those other cancers in, it is an even greater market. We first started commercializing our original device in 2022, right at the end of 2022.
So we have got a few years of experience under our belt now selling into this market. Claire is the name of the new product, launched a few months ago, and we have got a number of patents that protect it. If you are unfamiliar with cancer surgeries, if you have any experience with cancer surgery as a patient or other ways, margins, getting clean margins are the most important thing.
The easiest way to think about this is a hard-boiled egg, where you have got yolk as the cancer and the white as healthy tissue. And what you are trying to do is get that boiled egg out and not see any of the yolk. If you can see any of the yolk, or even a few millimeters away from the edge, you have got cancer. That is called a positive margin, and that is what the surgeon is trying to not have happen. So really get a few millimeters of healthy cancer around. It is very hard to do. You cannot see the cancer. A couple cells is all you need to ruin that margin. You cannot feel it. So they really do not have the tools, and you can see it with the numbers here, right?
Breast cancer, 23%, but thyroid, prostate, all kinds of other cancers that are not even listed here, are all up in that teens and 20s range of having to have reoperations. So big problem. I am going to talk about, in your mind, think about the company as having two technology pools. This is the first one. The first one is the imaging. So you have to have some technology that can see really small things, cells, and be able to see that within those 2 mm of margin. Optical coherence tomography, that is the base technology. We have modified it to what is called wide-field optical coherence tomography. There is a bunch of patents around that. Andrew is one of the inventors, main inventor in that. OCT itself has been around for years.
We all have it done off our retina when we go to the eye doctor to take a look at that at very high resolution. But it is small. OCT is small, 1 cm diameter, not big enough to look at an entire tumor after it comes out of the body. Wide-field takes that and expands it out to a 10 cm diameter sort of view. That is what you need. That is what we have. The reason it is so good is for those two reasons at the bottom. One is you get that high level of resolution.
The next closest thing is X-ray, which is 10 times worse resolution. And unfortunately, in this difference between the 10-ish micron resolution that we get and the 100-ish micron resolution X-ray gets That is where cells live. Cells, depending on the cell size, is somewhere from 15 μm, 20 μm up to 80 μm or maybe sometimes more.
But you need to be able to see that. X-ray can't see it. We are able to see a few millimeters deep into tissue, so that's important from inspecting the whole margin. The other important thing from a surgeon and a patient standpoint is that we don't need to touch the patient at all. We don't inject dyes. We don't have to put a device into the body, which has benefits from a safety standpoint. Take that imaging technology, that's IP pool number one, then add to that AI, which we train off that 2 million image library I talked about, and that makes the imaging system much easier to use in the operating room with a surgeon. That's what, put those two things together, and you've got a real good device that's out there that's usable by the surgeons.
AI is the second IP pool that we have. The big library we have is the IP pool, and then the AI is what it uses that to make it more usable. What the FDA cleared for us a few months ago is this product, which we call Claire. It's got a lot of benefits. It's got the benefit of having the AI there to help the surgeon inspect the margins. It's got a high confidence level. Right now, a high accuracy level of 88% right now. The one thing I will put in your mind is, although 88% is very good, with AI, it's the worst it's ever going to be. We have the ability to make it better. We've got certain mechanisms and approvals from the FDA to approve next generation AIs without having to go back through a whole FDA review.
We've already released the next generation version of the algorithm out to market in the last few months after getting approval. A lot of claims around it, a lot of great benefits. The combination of these two things allows us not only to see the cancer, but then to be able for the surgeon to use it much more easily. Just to get a picture in your head of what it does, the first image on the left-hand side is, all of these images are what OCT generates. What you're seeing here is a few millimeters into the tissue that's put on the machine that the surgeon took out of the patient. What you're seeing with those white spots, those sort of white circles, that's a cancerous area. That's what the surgeon doesn't want to see.
But if they do see it, they know that the patient's right there. It's in real time. They walk a few steps over, and they can take a bit more tissue out of the patient. That's a perfect example where that patient would have gone home without our device with cancer in their body. What you see on the left-hand side is what the surgeon would see without the AI. If you think about a typical patient, they go in, they take the tissue out, they put it on the machine, hundreds and thousands of images get generated. The surgeon has to search through all of these images. We've got a few dozen surgeons that do that right now. It's not the easiest thing to convince a surgeon to go do that. Therefore, AI, right?
What AI does, what you see in the next pictures is what our AI is doing. Claire with ImgAssist AI 2.0 is what ran through the clinical trial. Claire with ImgAssist AI 2.1 is what we released to market a few months later, an upgraded version of that. We have more and more of those coming through the pipeline. What that does is it looks through all of the images that are generated in real time, almost instantaneously, and drops those little red bars on top of the areas it sees as suspicious. Now instead of the surgeon having to search through hundreds and thousands of images, they get a half a dozen or a dozen images that they can inspect very quickly, and take whatever action they want to take.
There are future versions of the algorithms which you can see with ImgAssist 3.0. The only thing I will say about here is we had a trial that was across 10 different hospitals, this trial. Brand name hospital. These are world-class cancer institutions, so very high quality trial. Over 300 patients. These are some of the statistics out of the trial. You can dig into this more if you are interested or ask questions. I am happy to talk about it. But very high negative predictive value, very high specificity. Good sensitivity, 61% is actually quite good sensitivity in terms of being able to remove as much cancer as possible. Goes up from there. The margin accuracy at 88% is quite nice as well.
In the trial, out of the 200 patients that the machine was actually used on, we had 26 of those patients where they actually had clinical benefit. The way to think about that is not that 90% of the patients did not have clinical benefit. Most of those patients did not have any cancer after the surgeon did standard of care. The way the trial was designed is the patients and the surgeon went through the entire standard of care procedure, and only when the patient was about to get sewn up and sent home did our machine have the opportunity to do anything. All of these patients would have gone home with more cancer in their body if it were not for our machine. So direct benefit on these patients, 26 of them. It is a huge percentage of patients that had some sort of impact.
Not only that, we were able to do it with taking less tissue. If you think about a woman that comes in, "I want a lumpectomy. I do not want a mastectomy. I want to retain as much of my body as I can." So do not take more tissue than you need to. Now you are sitting in this very precise sort of environment where take all the cancer out, but leave all the healthy tissue behind. So it is important for us to not only do that, but also do that by taking far less tissue than would be under standard of care. We were able to prove that in this trial. Which I will say, when we go into a surgeon and introduce the product to them, some surgeons will say, "Yes, I do have a problem I would like to reduce my reoperation rate and be able to do that better."
Great. Look at the previous slide. We are able to save all these patients. Every surgeon out there is a better than average surgeon. They all have way better reoperation rates than the 20%. Of course you are. Are you concerned about taking too much tissue out of those patients? They generally are. We can flip over to that goal. We have lots of coverage. I think I have talked about this. We can give information to the surgeon in real time in the operating room without having to wait for the answer to come back, which allows them to save the patient from having to come back later. There are a couple of competitors out there. They are all small companies.
They do not do the job anywhere near as well as we do. They basically have very little market penetration. I can talk about this in more detail, but really from a realistic standpoint, the competition we face out there is status quo right now. It is not one of these other companies. It is a large market, 300,000 breast cancer surgeries a year. That is just breast cancer. We do have studies in other cancers. In some cases, the impact is much greater. Head and neck, as an example. If you do not get all the cancer out on the very first surgery and you have to go back in again, your chances of surviving five years after that fall all the way down to 10%. There are different areas, different impacts in different types of surgeries, so volumes are lower, but the impact is much greater.
A huge market for us to play in over time. Testimonials. The only thing I will say here is that there are various constituents, stakeholders in this whole thing, right? The patient is a big one, the surgeon is a big one. The payers who have to pay for the reoperation, that is big, and then the hospital themselves. There are benefits across the board. It is not like it is agnostic, like some people do not care. Some of those constituents do not care about it, and others care a lot.
They all benefit from having to have less operations and be able to get patients clear of cancer quicker. I talked a little bit about the survival rates here. You can see the reoperation rate is high across many, many different types of cancers. The blue bars are if the first surgery is successful, what your five year survival rate is.
The red bar is if your first surgery is unsuccessful, what your five year survival rate is. You can see where things really fall off the map if that first surgery is not successful. Head and neck is huge. Prostate cancer is not great either. There is a pretty big drop-off if they are not successful in the first surgery. Really getting those right is very important. Kidney is a big one, too. Okay, I am going to flip to economics here. The economics, the patient does not really care about the economics. The surgeon, to some extent, does not care as much about the economics, but the payers care a lot about that. Why should I pay for this? Just how much money am I going to save down the line? This kind of articulates that, right?
There are four different buckets that when we do the analysis, this falls into. It adds up to about $20,000. The first one, which is the biggest one, is really just the cost of not having to go through another operation and pay for that. The nurses in the operating room and the surgeon, all that stuff again, right? Most of the savings is that. There are OR efficiency benefits. Breast cancer is not the highest profit driver for hospitals. It is not the one that they actually want to use their resources for. If you could flip that operating room to not have to do another breast cancer surgery and flip it over an ortho or brain or something else, they make way more profit on that. There are some benefits to that as well.
Being able to not have to delay their chemo or radiation, and reputational and quality metrics. All of this adds up to $20,000. If you want to do your sort of financial modeling on this, take the fact that one in every four or five women have to go back in for a reoperation, divide this number by four or five, and then that is the cost that gets amortized across every breast cancer patient out there. That is the saving per. That is sort of like the total market that we could kind of go over. We are not out there from a pricing standpoint trying to say, "Give us all of that savings." We split that with the payers and say, "You take some, we take some," and then that pays for us to develop a product, make it, and then deliver it.
Reimbursements, if you are familiar with healthcare, reimbursements is a big deal. We have built ourselves today, we are sitting here today with a multimillion-dollar, over $2 million of revenue per year with no reimbursements, which means the hospital is paying for this all out of pocket. The insurance companies are generally not paying for our business right now today. With the new product that we have, we are going through. We are aggressively pursuing a reimbursement pathway. We have expedited pathways. If you are familiar with the reimbursement pathway for a business like ours, you may have in your mind this idea that it takes several years to get there, which is true. We have something called Breakthrough Device Designation, which opens up fast path reimbursement pathways.
We expect to get there early next year, which then unlocks the whole. It reduces a lot of the pushback that we get from the CFOs in hospitals. That is a very, very big catalyst for us coming soon. We have got growth, quarter-over-quarter growth, year-over-year growth, even with the existing product. Great momentum. We are in the process of converting our older product, S-Series, over to the newer Claire products. The newer Claire products run at a 2x- 3x ASP, so we get much more revenue per procedure. Momentum is building. The biggest challenge we have right now is not a challenge of demand or interest. It is really we are a small company. We have got about 40 people. It is a challenge of us having a sales force that can actually cover that. Our corporate headquarters is in Canada. We are a Canadian company.
Our U.S. headquarters is in Dallas for reasons of funding that we got some grants from the State of Texas. We're growing, and we're FDA approved. So we're growing out of there. We have a large concentration of customers in Dallas. Some of our biggest customers from a volume standpoint are in Utah, Denver, and Phoenix. So we're growing on that side of the country, and that's where we have our sales team. Right now we have no coverage in some of the most densely populated parts of the United States. Florida is the state that's got the most breast cancer surgeries of all the states. Of course, in Northeast, Boston, New York, the Midwest, high. So this is where we're now expanding our sales team.
We've got a huge amount of opportunities and pipeline sitting out there waiting for us to actually have people locally to drive that. That's our strategy: convert our existing customer base over to the new product at the higher ASPs with the AI, pursue some of that pipeline I was talking about, and then drive utilization on the machines we have out there. Since we do make money on a per-procedure basis, we put a machine out there, the more operations we can get on it, the more money we make. By the way, these are not sequential. We do these in parallel. Our revenue model. We have three revenue streams. The first one is, we don't implement that often, but it would be selling that cart. If we sell the cart, we sell it at a few hundred thousand dollars.
It's about a 50% gross margin. So I like that. The flip side of that is it takes a long time, generally, to get through a hospital buying cycle. So I don't necessarily want to delay getting to the second revenue stream, which is the consumable revenue stream. High gross margin, over 90%. If we can get that money sort of machine flowing, that's better for us, so I don't want to delay that. Then we have a service contract in place. Generally, the way we operate is a 90% gross margin on the consumable, $25,000-ish per year on the service contract. That allows us to get breakeven, even if we place the device there at no charge within a year. I'm going to end the five-minute warning. We've done a lot of things to date. The next milestone we have is, again, that reimbursement.
Then 2027 ends up being really that accelerated growth with reimbursement in hand plus FDA approval. It's a big market. You can do the math yourself. It doesn't take very much market share for us to get to breakeven. We have IP protection, both patents as well as trade secrets, as well as a large image library that protects this. There's really not much fear of a competitor coming in and copying us on this. It takes a long time, and there's a lot of hoops they'd have to jump through. Two of us that you see here are here in this room. We've got a great, strong leadership team, a lot of names from an experience level, so it's a good team. This is our capital structure. I think a couple things. One is we've got a good balance sheet right now.
We've got cash all the way into past the middle of next year. We don't have any real debt on the balance sheet. We've got a debenture, a convertible note that we, a couple of us invested in, for tax reasons, earlier this year. The two largest shareholders are myself and Social Capital, which between the two of us, we own about, well, you can see the numbers, about 30% of the shares and about 20% of the warrants out there. Then the last round we raised, we started to get more institutional investors on board. This is what the cap table looks like. That's a summary of everything I've said. Any questions in the last however much time they give us?
Can you talk a little bit about who you're actually selling to in the hospital? Do you know a lot of the pathology department who's actually buying decision-makers and second parties? Are you suggesting that frozen sections as an example of that buying anymore, which is going to rely on the AI and the research, or this is in addition to sections?
Okay. The first question is, who do we sell to? We sell to the ORs. We sell to the surgeons. They're the ones who make the decision to buy, and then they run it up through their. It sits in the OR.
[inaudible]
All operating room-based, yeah. The standard of care is pathology, so pathology has to inspect all of that tissue at the end of the day. Even with us, they will have to do that also. They collect a lot more information than just margin assessment. So that will still exist. It doesn't displace them, which is kind of good because it reduces the enemies that you get sort of by taking away business. What it does do is it provides that information in the operating room, so the surgeon can react more quickly in real time to that information they would have to wait for otherwise.
Follow-up question?
Yeah.
Yeah. Is your inflection point Sorry.
Go ahead. Noelle's had her hand up.
Is your inflection point then presumably getting reimbursement in 2027 work a little bit easier because I'm thinking right now as an [off-the-chain 12 thing] to me, they're going to perceive that they're not potentially getting paid for things that's hospital.
Yeah
It's a lot easier to navigate between those threes off of that.
It does.
There's no existing codes. You guys have pushed this on that now.
That is correct. That is correct.
[audio distortion]
To be clear, there was a tracking code for the previous generation product. This one does not have one, and the pathways that we. Because it has AI, this has AI plus OCT, everything all bundled together. The pathway we are going down has no code. It allows us then, which is a good thing, because that allows us to employ some. I can go into detail, but some work with CMS to get new codes, get new dollar amounts assigned to it directly, and we are not blocked by the previous codes. Yeah.
My last question is, I am just curious, you are doing breast now. What do you need to show in what leverage this new [12] from breast to head and neck? In cancer, what do you need to show? Then what do you have that allows you to be applicable to those particular needs?
Okay. I will give you the answer along two dimensions. From a technology standpoint, the imaging system we have is being used for other types of indications. There is no blocker in what we have from a technology development standpoint on the imaging system. The AI is trained on breast images. As we collect images of other types of tissue, we have the algorithms we retrain on that. Once we have the images, that is fairly easy to do. That is not a problem. Okay, so that is the technology product standpoint. From a regulatory standpoint, we are allowed to market to breast cancer because the trial we ran on Claire. The first-generation device has a general indication, which means we cannot market specifically, we can solve this problem for this type of cancer.
What we would do to really market hard, which means that we can open up revenue streams on the other types of cancers immediately because we have the product that is out there, and we can talk about what it can do generally. But for us to supercharge it, we will want to have some sort of evidence built about how we can perform in that sort of. So there would be some sort of evidence we would have to generate, put in front of the FDA to do that. So no blockers on revenue right now, an acceleration once we get FDA approval on those other indications. Not necessarily. It is just the claims that we want to make against it. Yeah. Okay, I am getting yanked. If you have questions, feel free to Andrew or myself, we can answer between us any question you would like.
Thank you for your time.