If you'd like a copy of today's deck, simply email me at Glenn Green at bristolir.com. We'll break for questions at the end of management's presentation. When they do break, we encourage questions. As a reminder, we're only going to take questions through the web portal. If you're listening over the phone, please access the web link that I would have sent earlier today to ask that question. You can submit a question anytime. I'll ask the questions on the air for everyone to hear, and then Adrian, Andrew, or Sarah will answer. I'm not going to reference any names, but simply read the questions asked. And as we have a fairly large audience today, if I can't get to your question today and it has not yet been addressed during the call and can be, I'll get back to you by email.
I'm not going to read the forward-looking statements, but I do state that they apply, and I reference them on page two of this presentation. With that said, once again, thank you for joining us. Remember, this is fairly informal, and we do encourage questions to help you better understand the business and its growth path. And now I'll turn the call over to Adrian to start his part of the discussion and presentation.
Thanks, Glenn. Good morning or afternoon to everyone. I thank you for joining us. I'm Adrian Mendes. I'm the CEO of Perimeter. And with me, as Glenn mentioned, we've got Andrew Berkeley, who is the co-founder of the company and our Chief Innovation Officer, and Sarah Burnell, our CFO. We have forward-looking statements in here, so we won't go through them specifically. And then because our company and our products are regulated by the FDA, we do have indications for use. We'll be talking about two key products today. One is the S-Series, which has a 510(k) clearance and is available for commercial use in the United States. And then we're also talking about our B-Series product, which is right now authorized for investigational use and not for sale in the U.S., but is going through a clinical trial right now.
So we'll get into both of these in this presentation. A quick summary, just a snapshot of the company for those of you who are not familiar with us, is on this slide. We do have an experienced management team. Andrew, of course, has been with the company since the founding about 10 years ago. Sarah and I joined just over a year ago to the company. We bring a few decades' worth of experience between, well, each, I suppose, in technology management and healthcare management. Of course, we've got a very strong executive team and management team across AI, medtech, technology, kind of the entire gamut of what's required to run a business like this. We participate in a very large market. The oncological surgery market in the U.S. alone is over 1 million surgeries per year.
For better or for worse, it is growing over time, and that's the market that we play in. One of the things that excited me when I joined this company a year ago and continues to excite me is the fact that we do have a very differentiated technology that is very useful and helpful in the applications that we're applying it to. One of those key technology areas is called OCT, or optical coherence tomography, which we'll talk about a little bit more. That differentiation is important because it helps us solve a very big problem in a way that's very difficult for others to solve using different methodologies. Sort of extending on that, our tech is protected. It's quite well protected between issued patents, patents we've got pending, provisional patents.
There's a significant amount of trade secrets and just sort of know-how that we have to create the solution that creates a fairly strong moat between us and any other competitors we've got. As I referred to before, we do have a product that is FDA cleared and in the commercial setting right now, with which we're earning revenue on and surgeons are using in the field with their patients. Then we've got a next-generation product that's going through a clinical trial at the moment that we hope to bring onto markets after we get clearance, hopefully next year. With that, I'm going to turn it over to Andrew Berkeley to take us through a little bit of the technology and the problem that we solve.
Thank you, Adrian. Okay, so let's start first of all with the problem. That is during any surgical oncology procedure, the main goal is to remove all of the cancer, leaving no cancer cells behind. In the case of where you have a positive margin, this is where there is still cancer at the surface of removed specimens, which is a proxy for cancer being left in the patient. That's called a positive margin. Negative margins are where the surgeon has been successful in encapsulating the cancer in a barrier of healthy tissue. That's what we want for every cancer patient who's going through surgery. Today, it's very difficult for surgeons to be able to verify that they have achieved negative margins or to identify when they do have positive margins in real time because there's no existing technology that can achieve that.
The tissue gets sent off to a pathology lab, and it gets analyzed under a microscope. Approximately a week later, they get a report back to say, "You had a positive margin," which nine times out of 10 will signal for to bring a patient back for a second surgery. Next slide. Cancer, as everybody knows, is an extremely challenging problem in everybody's lives. There's 4 in 10 people in their lifetime who are at risk for getting cancer. When you look at positive margin rates across different cancer types, in breast cancer, it's 23%. In prostate cancer, it's 21%. In other cancers like thyroid, it's 12%. This is not unique to just these three cancer types. Liver, kidney, pancreas all suffer from having positive margin rates, which lead to unforeseen and unwanted outcomes for patients.
So when you talk about the problem of cancer, it's not just a very physical or very emotional problem. And when our customers who are surgeons have to face these patients on a daily basis, they say it's an extremely difficult time to discuss treating the patient and the treatment path. But what's even more difficult is to pick up the phone to have to call them to say, "I didn't do my job properly. You still have cancer, and you need to come back for a second surgery." That's a really bad scenario for the surgeon, for the patient, for their family. And that's something that we're driven to transform. So our technology, as Adrian pointed out, leverages OCT. OCT has been used in ophthalmology and cardiovascular imaging for the last 20 years. That's about 30 million three-zero eye exams done with OCT every year.
It creates a super high-resolution image. It's a very shallow imaging depth, but it gives us enough resolution to be able to see microscopic disease within the margin. Then that allows the surgeon to take corrective action if possible. So on the bottom, you will see bottom left, you'll see an image that the pathologist looks at under the microscope. This can take up to a week, maybe even longer, for them to get the slides under the microscope and then generate a report. Then within 10-15 minutes on the top image, this is an OCT image of the same tissue area. And this is a very small microscopic area of an early breast cancer that is right at the surface.
By having OCT available, if this was identified during the operation, it would allow the surgeon to take more tissue while the patient is on the table rather than having to bring them back for a second surgery. The platform itself has four key components. We have the imaging system. We have a single-use container that is used on every patient. We have an imaging atlas where we have correlations to pathology, so OCT matched to the ground truth. And we use that to both train human readers, but we also use that ground truth correlation to train our AI algorithms. And our AI algorithms are in a pretty advanced state. We are just about to wrap up in the next few months recruitment on our multi-site clinical trial to evaluate the performance of our AI in helping to address unwanted positive margins.
What will happen is the device will be used as normal, but the AI will do a first pass over all of the images, and it will highlight specific areas to the surgeon and allow the process to be more streamlined and potentially more accurate. The current standard of care today is to remove tissue, send it off to pathology, and wait 2-7 days to learn whether or not they have removed all the cancer successfully. Then you can go on to the next stage of treatment. With the Perimeter solution, our device sits in the operating room. We visualize the margins in real time, 10-15 minutes. If the surgeon wants to take more action, they can do that there. Then all of the tissue continues on to post-operative pathology.
The goal is that we achieve a much higher rate of negative margins compared to when you don't have the technology in the above standard of care. In the last few months, we started to really try to correlate the data that we've had from the S-Series over the last 12 to 18 months. We have a number of papers in the process, but here's just the first one that's come out. It's from a doctor out of the state of Texas, Dr. Gunter. We looked at 72 of her patients using OCT, and we compared her statistics to the national average, which was a comprehensive study done by MD Anderson and the University of Texas Health Science Center, where they looked at 24,000 patients. The national average came out at 19.9, so a 20% reoperation rate for breast cancer patients. When we analyzed Dr.
Gunter's data, she was down at 5.6%. So almost a 4x improvement on reducing reoperation rates for Dr. Gunter in her first 12 months of proper use of the device. When we break that data down into kind of different subcomponents, there's two real types of breast cancer that cause issue. The first is DCIS, which is an earlier stage cancer. And then we have invasive ductal carcinoma, which is IDC. So when we looked at the DCIS patient group, the national average is 31% for below the age of 65, 24% for Medicare patients, so above 65. And then with the Perimeter device, we were able to get that down to 13%. When we looked at the invasive cancer, which is 5% of all of the cases, the national average is 18% for below 65, 12.7% for patients over the age of 65. And Dr.
Gunter had no reoperations using OCT for this subgroup. Again, that was 75% of the national cases for breast cancer falls into this IDC case. So some pretty good outstanding results there in the IDC category. We are continuing to build a body of evidence. As I mentioned, we have the Dr. Gunter paper that was just released. We also have two other investigator-led studies with people who are using the S-Series and the impact that it's had on their practice. And we also have a more clinical research paper in the works, which is going to highlight our work in head and neck cancer. So just some testimonials from our current users. You can read through some of these. The theme is consistent that you have a much better chance of getting clear margins the first time.
I have seen its benefits firsthand, and it has impacted positively on my patients. I think what's interesting here is we have a very advanced technology, but a lot of these surgeons who have adopted it are very mature in their careers. This is not just a young person's technology. This technology can be adopted by anybody who is practicing surgery. You don't have to be a young, up-and-coming, tech-savvy surgeon to adopt the technology. Back to our AI trial. This is just a list of some of the sites that we've worked with over the years in both generating the data to train our AI algorithms and to run the clinical trial.
So we have Mayo Clinic, Baptist MD Anderson Cancer Center in Jacksonville, Holy Redeemer in Philadelphia, Moffitt, which is one of the big cancer centers of the country, Baylor College of Medicine, and the University of Wisconsin. So some really big names in the academic cancer center with some community hospitals built in there as well.
Okay, thanks, Andrew.
Yeah, Adrian.
Yeah, so taking what we've talked about so far, then looking at the economic opportunity. So it's quite large, actually. This chart has a lot of numbers on it, but basically, on the left-hand side is the opportunity. On the right-hand side is what we think we can do within that opportunity. So just sort of extrapolating some of the numbers we've seen already, we've talked about for breast surgery for lumpectomy, about a 20% re-excision rate. Out of that same paper that Andrew referenced earlier by MD Anderson and UT Health, they've got some math in there off their study that shows that there's about a $17,000 average cost per re-excision. And when you extrapolate that out across the 200,000 lumpectomies per year in the United States, you get to almost $700 million per year today and growing off what I call waste.
Money spent on surgeries that, if they were successful their first time, would not have to be done. So there's that $700 million. Now, with our technology, what we've got on the right-hand side takes some of the data we've seen from that first paper that we published that shows surgeons getting their re-excision rate down to the 5.5% range. With that level of improvement, we can save the system almost $500 million per year. Again, just with breast cancer, just in the United States, and that's just today. So a significant amount of savings based off what we've seen and what we've got evidence put together today. Now, extracting that out further, we talked a little bit earlier, Andrew referred to other cancer types that we have done some studies on. We continue to do studies, and we know the technology works for.
So if you just look at the top eight cancers, there's about over $1 billion per year spent on re-excisions across just the top eight. And again, just in the United States. So the economic opportunity just from that, from the cost reductions, is quite large. And our tech, as we know, can expand out beyond breast cancer. Perhaps even above and beyond the dollarized impact is the graph on the right-hand side, which is very hard to quantify this from a dollar perspective, but you know there's a ton of value there. And what this shows is the difference in the five-year survival rates for a patient if their first surgery is successful versus if not. And you can see that there's a relatively significant drop-off in that five-year survival rate if the surgeon isn't able to get all of the cancer in that first operation.
Anywhere from 10% drop-off, actually 11% drop-off for breast cancer, down to a very, very large drop-off for head and neck. So it's really, really important, not just from an economic standpoint, but more importantly from a survival standpoint, to make sure that the surgeon's able to get all the cancer that first time. So this is kind of the opportunity that we look at that drives the work that we do, both economically and then also from the social aspect of it. So turning a little bit to the commercial strategy, this is a graphic that kind of describes where we have gotten to so far and then where we see ourselves going over the next few years just on the commercial dimension of the company. So since founding in 2014, we really classify that as just getting ourselves to the starting line. There's a lot of product development.
We need to get a 510(k) clearance from the FDA for the S-Series. We had to develop our AI. So a lot of R&D and regulatory work we had just to get our product to be able to get it into the commercial setting were there. So this year is about gaining traction on the S-Series. Really, what we're doing here is we're focusing on what we call the ideal surgeon, the early adopters. We're focused on a few key geographies in the United States: Texas, Georgia, Florida, and Ohio, and really building traction there. We're exercising our commercialization efforts, building our sales force, building our best practices, all with the intent that as we move into next year, we go into expansion mode. So we're starting to expand that S-Series through new geographies.
We start to move from the early, early adopters into some of the middle adopters from a surgeon standpoint. We've built reputation at this point, and then even stronger in 2025 off our technology so that our existing surgeons are starting to tell their colleagues about the success they've had. That first paper that Andrew talked about, we'll get more of those out into the market. So we're really into expansion mode in S-Series next year. As we also talked about, the B series clinical trial, we'll have finished recruiting all the patients we need to recruit to close out that trial this year. And then we'll be in the FDA approval cycle next year. And what happens then is that we hopefully get approval in late 2025, early 2026. And as we look into 2026, now what we've got is we've got our B series on market.
What the B-Series brings is essentially the same core hardware technology as is in the S-Series, but with that AI layer on top. And what that allows us to do is to expand the customer base. So it allows us to move from the early adopter surgeons out to what we call the middle adopter and late adopter surgeons, which then expands our customer base from a few hundred breast surgeons out into the multiple hundreds and thousands of breast surgeons in the United States. Also, what that allows us to do is to really further our strategic partnerships on some of the things I'll talk about as we flip forward in this deck, and then also expand onto tissue types.
Once we get to 2025, 2026, we'll have what I call our breast surgeon machine sort of up and running, and we'll be able to expand into different tissue types, head and neck cancers, and others. So this is how we're going to grow the business commercially over the next few years. We're starting to see signs of this already this year. And as the year progresses and we announce more of our quarterly close, I think you'll be pleased with how that progress is happening this year. So we talked a little bit about our core technologies, one of which being the OCT or the imaging technology, and then the other is being our AI technology. Putting those two things together, we're starting right now in the surgical suite, which is the little bubble on the left side of this graphic here, the tumor margin assessment markets.
And we've talked a lot about today about how we can apply those two core IPs in that market to help surgeons make sure they've got clean margins when the patient exits the operating room and seeing good success there. But if you just think about that core technology and then how can it be extrapolated, really what we've got is the ability to visualize tissue, visualize cellular structures down at the microscopic level. Today, that IP exists. We've done the R&D for that, and we've built out our data library already for the breast cancer. So it's very easy to see how that can be expanded into the biopsy markets where doctors are taking biopsy samples from patients to assess whether they do need to go into surgery or not. And that's a large market, 2 million breast biopsies a year in the US.
Then also pathology, where the pathologist is looking at that excised tissue under the microscope and trying to understand if there is cancer in the margins. So that's a very, very large market as well. Both markets are targetable with the core IP that we have right now. We don't have to invent new things. It turns into an engineering exercise at that point in time, which is much less risky. We see great opportunity to expand out from the current market we're in into those markets and also great potential to engage strategic partnerships with some of the big players that are in those markets today. As we look into our roadmap, not only do we have a really big market in the tumor margin assessment space, but also great TAM expansion opportunities throughout other markets.
Of course, key to that is our ability to protect our IP. I touched on this earlier. We've got a number of patents already granted as well as filed and pending. And we've got a significant amount of AI expertise and then a very significant image library. Which if you're following the AI markets at all, you can understand that the algorithms are important to be able to use the raw data you feed in. But what really is the IP protection, what adds value to a company is the image library or the data library. And we've got a very large library for breast cancer. And even better, it's proprietary. And you really need our OCT technology to build that out.
So this provides a nice moat for us because we don't have to be worried about someone else building a library sort of in the background and then surprising the market by bringing out this brand new library of OCT images for breast cancer or any cancer. It's something that we've got very well patented. And so they'd have to go through us to be able to build that library. Moving to the capital structure, here's a summary of the current state of the company. So we've got three analysts that cover us. We've got 65 million shares outstanding right now. We're listed on the TSXV and OTC markets both in the U.S. and in Frankfurt. We have one major shareholder, Social Capital Master Holdings, which owns just over 20%.
And we've got north of $10 million in our bank account as of the end of Q1, which is our last announced number. This is a quick snapshot of our executive team. We've talked about we've got three of us on this call right now. But of course, the team extends much beyond just the three of us. We've got top leaders across sales and marketing in our clinical affairs department, head of engineering manufacturing, Carl, who sits up in Toronto with Andrew, and then Tom Boone is our Chief Operating Officer. So a ton of experience in medical devices and engineering and sales and commercialization. And then the depth just extends down into the organization below this team as well. So to just summarize what we've talked about before we open up for questions, it's a very large market. Cancer, unfortunately, is a large market.
It's a high gross margin business model that we've created here. We've seen, obviously, with some of the data that we showed earlier, we've seen that we can have a huge impact in this market. So we're very excited about our ability to take a significant part of this market and have a very positive impact for patients and healthcare providers over time. We've got positive reception from them, as we've seen, as you've seen some of the endorsements from our KOLs, our key opinion leaders. And then taking that core IP that we've developed over the years, significant market expansion opportunities, not just geographically, but also into different tissue types using the key IP that we have, and then into different areas within the entire treatment sort of stack from biopsy to pathology as well as in the surgical suite where we are today.
And that's all very well protected, as we talked about, with not only our IP, but our trade secrets that we've got. So a very strong company with strong technology underpinnings in a very big market and showing a significant improvement on sort of the standard of care that is out there today. And so with that, I think we can wrap up the formal part of the presentation and move to questions.
Super. Thanks, Adrian. And again, to our audience, if you have a question, please use the Q&A text feature within the Zoom webinar. We do have quite a few questions in the queue already, and I know that some of these have been addressed post asking the question, but maybe we'll revisit some of these topics. First question for you, Adrian, is how does Perimeter technology compare with other imaging solutions currently in the market? I guess a competitive review of the marketplace and where different products fit.
Yeah. Maybe I'll turn that over to Andrew to answer.
Yeah. I think the question is specific to imaging from what I can gather, Glenn. Is that correct?
Yes.
Okay. So OCT, as I mentioned, is an established imaging modality used in ophthalmology and used in cardiology. Its main benefit is the resolution that it produces, which is about 10 or 15 microns. When you think of ultrasound, it's very similar to OCT, except we use light waves instead of sound waves. You get a much higher resolution image. The trade-off is that you don't get a lot of imaging depth. Ultrasound can see further into tissue. But for margin visualization, we only care about the surface down to 2 millimeters. Once that is cleared, that's considered a successful surgery. When you think of X-ray, ultrasound, or MRI, or any of these standard CT, even imaging modalities that people are very familiar with, they just do not have the resolution to be able to provide the information that the surgeon needs in the operating room.
The features that we see in OCT are too small to be identified in X-ray or ultrasound or MRI. Now, as I said, there is a trade-off. We cannot see all the way through the specimen, which other technologies can, and that has its own value as well. But we are just focused on solving the issue of positive and negative margins. And in order to do that, you need to have the resolution that can identify both negative and positive margins.
Okay. Super. Thank you, Andrew. Next question, Adrian, can you talk about how many units are in the field today and how you see the scaling? I know we had the timeline, I guess, page up there, but maybe expand on that a little bit.
Yeah. So we've got close to 30 or more units in the field total. And I'll describe how that breaks out. We've got about 12 out there right now earning revenue, S-Series out there earning revenue with commercial surgeons using it on patients. We have another handful that are demoing for upcoming customers. So we go through a phase in our sales cycle where we'll demo a unit. We'll place it in the operating room after we get permission from the hospital with the surgeon, and then we'll help train them and show them the value of it. So this is part of our sales pipeline. So we've got a handful or so in that phase. And then we've got about 12 out there. From a hardware standpoint, it's the same device in the clinical trial.
From a software standpoint, it, of course, has the AI layer on top of it. And then we've got another few, two or three, that we have in the field where we're gathering data and doing studies. So that's kind of the fleet that we have out there. A couple of things to just kind of say to that, as we think about how we grow the business, it's super important for us to make sure we're a device company. When you put device in the field, you need to make sure that it's going to be reliable.
And so having all of these devices in the field, not just commercial earning revenue, not just demo, but also the studies and both the studies, both the BCR study as well as the studies we're doing on the S-Series are useful for us to gather data, build up the reliability of the machine, as well as see how it operates in the real world. So that's the state of affairs right now. As we move forward, what we'll end up seeing is on the commercial side, that's going to continue to grow, and that growth rate's going to accelerate as we continue to build out our sales force and what we have word of mouth, building up our sales pipeline.
As the trial wraps up, what will happen is we'll end up pulling some of those machines back as we push the B-Series through FDA approval, and then they'll get redeployed as we get approval.
Okay. Thank you. Based on experience, what would you say is the biggest hurdle that you've faced in terms of getting surgeon adoption, and how do you see this changing?
I think the biggest hurdle right now is probably the training aspect. There's a couple of uses, things that the surgeon needs to do or someone in the operating needs to do when using our device. One is they need to orient the sample onto the scanner. So when you're looking at margins, you need to scan all sides of the tissue sample. So there's a little bit of work needed to put it down on the scanner, take the image, rotate it, take the image. So there's that. That's work number one. And then work number two is interpreting the images, being able to look through the images and identify, learn how to read them, and identify what they're seeing. And so there's a training aspect that needs to happen with both of those.
It isn't a lot, but to really get a surgeon up and running and capable, a user up and running and capable, they do need to put some initial effort in. And this is important because without that, they're not going to have success with the machine. So one of the things that we do, I use the phrase early adopter. I think both Andrew and I might have used the phrase early adopter earlier in the presentation. What's key for us when we are ramping our commercial efforts is to focus on those early adopters, focus on those surgeons that are willing to take the time and effort to do that, put that effort in, and bring that into their practice. And with that, they're the ones who are seeing the success that we kind of talked about a little bit earlier.
As we progress further, both with the B-Series and some other products that we're bringing out to market shortly, what you'll see is that effort required to become proficient on the machine goes down. This is what really opens the market up larger and larger. I think that's the biggest barrier. We don't really see barriers in terms of once we talk to a surgeon, whether they believe that re-excision rates are a big problem, they all see it as a big problem. We don't really see whether they admit it or not. We don't really see, at this point in time, challenges in terms of, at least with the surgeons we're participating with, in terms of them being able to justify this to their management within the operating, within the hospital groups.
We're very careful in the ones we select to make sure that they have influence. So it's really about making that ease of use better for the surgeons. And that's where we focus a lot of our time.
Super. Thank you. On the same topic, is there an opportunity or an option to have a trained technician or some kind of professional along with the surgeon, helping the surgeon in the room related to your device?
Absolutely. Yeah. And we do that in some cases right now through training, like when we're training the surgeons. But there's a lot of people in the operating room. There's nurses. There's physician assistants. And so the opportunities do exist. The clinical trial that we're running on the B-Series, we're not allowed actually to have any Perimeter staff in the operating room for those procedures. So there, we've got a fairly robust training program to help non-Perimeter employees learn how to use the device and help the surgeon with it. So it really comes down to surgeon preference. Some surgeons like to have full control over everything, and some surgeons would prefer to delegate that to someone else in the operating room.
Okay. Super. You had that slide in the deck related to, I guess, cancer missed during surgeries. Can you comment on what makes some cancers more or less challenging than others in achieving negative margin?
Yeah. I'll let Andrew answer that. I think he's probably the expert.
Yeah. For sure. There's different reasons. It's the stage. If it's an early stage cancer and it's in a dense breast, it's very difficult to see that in mammography. It's very difficult for the surgeon to plan and know exactly the extent of the disease. We've released a white paper a few months ago. I think about 50% of the time in breast cancer, the size of the lesion is underestimated. So the surgeon goes in thinking they're taking out a 2-centimeter extent of cancer, where it gets analyzed by pathology after it's removed. It actually turned out to be 3 centimeters. So you have that as an example of an early stage cancer that's hard to visualize. I think with invasive cancers, particularly if they're treated with chemotherapy, they respond differently. Sometimes the chemo obliterates the cancer. So there's really nothing there to visualize.
Sometimes it leaves it kind of looking like Swiss cheese. Then other times, it doesn't have much of a response at all. All of these things look different. You have to be able to identify them uniquely differently at the margin. So it kind of depends on the stage of the cancer and the type of pretreatment that has been applied to the cancer that can impact the ability of any technology to be able to identify it at the margin.
That's a big part of our data collection is to look at all of these examples, find out which ones are truly causing all of the issues for surgeons, and then focus in collecting the data there, not just to train humans, but so that we can train our algorithms to be the best at finding those kind of outlier cases which are causing the headaches for surgeons.
Okay. Thank you. Next question. You're currently using, obviously, the scan in the operating room. Is there an opportunity to use the OCT device in pathology as well?
Yeah. Andrew.
There definitely is. We can identify microscopic disease or visualize it in real time. That's very useful information to the pathologist because the pathologist receives a large specimen, and then they have to process it. They actually only look at about less than 1% of the margin because when they make a histology slide, it's very, very thin. They make about 20-50 on average. If you were to make histology slides for the entire specimen, it would be close to about 10,000. There's lots of times where we see things that pathology might have missed. We can use our images and our orientation guide to help pathologists being able to more accurately sample on the specimens that they receive. That's just transferring the information from the operating room to the pathology department.
You could also imagine having a standalone device in the pathology lab that, when any new specimen comes in, it quickly gets imaged with OCT, apply the algorithm, it flags specific regions. The pathologist can then look at detail at the OCT images, but then proceed to sample in those areas to be much more efficient and more accurate potentially with their analysis. That could be done across a multitude of different tissue types.
Okay. Thank you. Can you give more clarity of the 5.6 re-operation rate in Dr. Gunther's paper? In other words, I guess, how does the OCT miss certain cancers? Can you get it from 5% down to a number closer to 0?
Yeah. Again, it's not microscopic disease where sometimes it's the presence of a couple of individual cells at a margin that have been left behind after chemotherapy. It could be a very small duct that has disease in it that the difference between what a normal duct looks like and what a diseased duct looks like is even hard for pathologists to identify. And it's some of those kind of outlying cases, which is where we need to continue to build our data and to build our training libraries for our AI technology. So for the most part, when disease is there at the margin, we are able to visualize it. But there are some outliers where we're still trying to build our knowledge base. And we do hope to take that 5% down as low as possible.
Given that we start at around 20%, we still believe that reduction is significantly important.
Okay. Thank you. I guess some business-type questions for you, Adrian. And I'm sorry, I can't recall if we covered this in the formal presentation. Can you just provide some color on your revenue model? Do you sell the product to customers as capital equipment, or is it some sort of razor blade model?
Yeah. It's both, actually. So we will place, so first order, we'll place the device for free inside the operating room, and then we will charge for the immobilizer. That's the container that was pictured on a previous slide. And the way we are building the business model is those immobilizers are very high margin. And so we are targeting surgeons or multiple surgeons using the same machine such that we get payback on the machine after about a year. The machine lasts 5+ years. So that's the basic business model that we've been employing to date.
Moving forward, and just to talk about that a moment, a part of the strategy behind employing that type of business model today is to really be able to seed the market, build some of the data that we're now able to show in some of the papers we're writing, and start to build that word of mouth with our surgeons for their colleagues so that we are building out the demand. As that continues to grow, we will start to evolve the business model. In fact, we've started to do it already where we'll charge for the device that we place inside the operating room and then have a different pricing model on the consumables. What's important to us is to always have a recurring revenue model on the consumables.
And as the demand for it starts to increase and we start to really build out the evidence, we'll start to charge for the device itself. On top of that, we do have other revenue streams. We charge for service. We charge for maintenance. So there are other ways that we are bringing top-line revenue into the company.
Okay. Thank you. You said that the B-Series and other products that will be introduced shortly will reduce the amount of work and training to do the test. Can you please elaborate on that?
So with the B-Series, what the B-Series does that's different fundamentally from the S-Series is it adds an AI layer on top. So what the surgeon does is they scan the specimen, and then they review the images on a screen. And there are lots of images. They kind of scroll through it like a movie on the screen today with the S-Series. What the B-Series will do is it will, with the AI algorithm, highlight, let's say, the top dozen or half a dozen frames out of all those images that have the highest likelihood of having cancer in the frame and put those on the screen.
And then the surgeon can zoom right into those areas and then make a judgment for themselves whether they think that is something that they need to go back into the patient and take another shave or not. So now, rather than the surgeon scanning through everything, every single image in the volume, they can review the top dozen or so images, which, of course, naturally will then speed up their efforts. That's the biggest. So what we see is for those surgeons that we have on the S-Series right now, that'll be maybe one of the more significant time savers that they'll have from using the device.
But the other benefit of B-Series is that it sort of extends into those surgeons that are not yet using the device where it allows them to bring the tech into their operating room and have more confidence that they aren't missing things. It's sort of that co-pilot standing beside them. And so what it does, it does two things for us, the B-Series. It helps speed up this time in this operating room for our S-Series customers. And then it really greatly expands the user base for the technology with those later adopters. That's, I think, the most significant improvement we'll have.
Okay. Thank you. I think this question is for Andrew. Can you theorize why the five-year survival rates are lower if a patient doesn't get clean margins on the first surgery but gets clean margins on a re-op? And why would utilizing your technology in real time help survival rates if after first removal, there were not clean margins?
Interesting question. Let me get this right. Why is there a difference between survival rate of negative margins and having positive margins? Is that, in essence, what the question is?
I'm just reading. I think that's the case, but I'll let the individual sort of do a follow-up question if we don't get it right.
Sure. Sometimes having a negative margin or, sorry, having a positive margin is where there's actually no more tissue to take. And if you actually think about head and neck cancers where they go in and they try to remove the cancer with sparing as much tissue as possible. But unlike the breast, there's not a lot of extra tissue to take. If you're doing breast surgery and you identify a positive margin real-time and there is more tissue to take, it's quite a simple approach. They just go and they take a shave, and then they can analyze the piece of tissue that's come out. And for example, in the head and neck cancer where you do see the impact of unaddressed positive margins, sometimes there's just no more tissue to take or there's no more margin to take. So the cancer is there.
The only way to continue to treat it is not surgically. It's with neoadjuvant or post-adjuvant treatment, which would be some type of radiation or chemotherapy. By that stage, the cancer may have evolved and may have spread. I hope I'm answering the question correctly.
Okay.
I think, in essence, sometimes there's no more tissue to take. So even if you do have a positive margin, it doesn't always mean that you have the ability to go back and take more tissue. In prostate cancer, usually for positive margins, they don't go back and do a second surgery. They administer radiation therapy. So it just depends on the cancer type, really.
And I think there's, in addition to that, sometimes there are reasons why the surgeon will choose not to do a second surgery because they hope to get, even if there is the ability to take more tissue. And that's where our product can come in handy. The risk trade-off between bringing a patient back into the operating room versus taking a bit more tissue while the patient's already in the operating room can be different for different patients. And so if the patient's already gone home, there may be a choice made to not bring the patient back in for reoperation, even though they do have positive margins because the other risks associated with that are greater.
And so I think this is an area where having a technology like ours in the operating room when the surgeon has the ability to take more tissue without the other risks can actually be very beneficial to that five-year survival rate.
Okay. Thank you. Can you talk about, I guess, the reimbursement model that you may have? Is there coverage through or potential coverage through Medicare or insurance payers for the product? I guess just in general, overall statement on reimbursement.
Do you want me to talk, Adrian?
No, go ahead. Go ahead.
Okay. So there are CPT tracking codes for the use of OCT during breast cancer surgery that we do leverage. One is a hospital code, and the other one is a physician code. The reason why these are put in place is in advance of getting to a full CPT code that is both covered and paid, you need to prove to the CMS, to the authorities, that, A, your technology works, and secondly, that it's being used all over the country. So every time somebody uses our device, we encourage them to bill for our codes, even though they are only tracking codes, and they generally will come with a $0 amount. And that does two things. It gets us into the chargemaster where some of the insurance companies we have seen have provided a small amount of payment to the physicians.
But the main purpose is that the CMS can actually track the utility of the device. Because when we go to get our codes transferred from being just tracking codes to fully covered and paid codes, they need to see that there is utilization happening throughout the country and that this device is being used by multiple hospitals in different regions rather than just by two or three doctors in a university setting. So that's where we're at in terms of reimbursement. We do have tracking codes. They are being billed. We're building up our utility. And for the physician code, we are starting to see some small measures of payment. It's very infrequent, but at least it's something to get us up and running to say that some of these codes are getting reimbursed.
Okay. Super. Adrian, if you could just go over some of the timelines regarding this question again. With patient enrollment in clinical trial completed by Q4, when do you think the study will be completed and analyzed by you? And then from that point in time, how long would you expect it to take to get FDA approval?
Yeah. So based off the current schedule, which is ahead of our expectations, actually, it looks like we should have our data analyzed by the end of this year or very early next year. And then approval through FDA can be anywhere from 9-12 months. So the way we're planning internally is to assume that we get the approval from FDA towards the end of 2025.
Okay. Super. Thank you. We've got quite a few questions that are sort of finance-related, so I'll just sort of characterize them all into one and let you address it. So you've obviously got a cash burn. How do you currently think about your balance sheet and then as it relates to your future cash needs and potential grants and revenue coming down the road?
Yeah. Sarah, would you like to take that?
Yeah. So Adrian mentioned that at the end of Q1, we had $10.6 million on the balance sheet. We burn about $1.2-$1.3 million a month. That projection gets us to the end of the year cash balance. I think we do have an ongoing CPRIT grant that covers about 67% of all of our ongoing RCT costs. That will carry us through the end of our trial. Andrew and his team are working on looking at other opportunities for grant applications to fund future innovation. So we are looking for sources of non-dilutive funding to help us on the R&D front. I think that answered all the questions.
Okay. Can you talk about investments into the company to date to get it to this current level, I guess, size and timelines?
Sure. So since the inception of the company, there's been about CAD 75 million invested in the company over a 10-year timeframe. The majority of that money came in in January of 2022 with a private placement that was CAD 49 million.
Okay. Are there other imaging companies that have started using AI for their databases?
Yeah. Almost all imaging companies will be using AI, but we don't have any of them that are directly competitive to margin visualization or margin assessment. There are other approaches using fluorescence and using RF spectroscopy, but they're not imaging-based. There's a company called, I think, Koios, which has a breast ultrasound device that they use in the clinic that has an AI overlay. Mammography images now are leveraging AI to help with a second read. So AI is in medical imaging, and it will be here. It's the future of medical imaging. But we just don't have anybody in our direct space who is leveraging AI and imaging at a competitive level.
Okay. Thank you. Can you talk a little bit about your manufacturing strategy and the costs surrounding production of your machines and consumables?
Yeah, sure. So there's two things that get manufactured. One is the device, and the other is the container. We manufacture both of them with a manufacturing partner in Minneapolis, and of course, in close connection with our engineers and our technical folks. Right now, I'm not going to talk about costs specifically, but I will say that as we start to ramp the business, there's a significant effort that we have to reduce cost. The cost right now, the cost right now are actually on the immobilizer, the costs are quite good. So even at the prices that we're selling, which is we list the product for about $1,500, our ASP is just around $1,000. Even at that level, we've got gross margins on that approaching 90%.
As we ramp the volume, we've got our business model out where between the cost reduction as well as the selling of more devices, we'll end up with a blended gross margin of around 75% over time.
Okay. Super. What would you consider your biggest barrier to expansion?
At this point in time, our biggest barrier to expansion is really our sales organization, scaling up our sales organization. We've got a very deep sales pipeline. We have, actually, frankly, right now, more options than we can handle. There's an annual breast cancer surgery conference once a year that is in the April timeframe. So Andrew and myself were at that along with our commercial team a few months ago. And the interest that we were getting from surgeons who we had never met before, our sales team had never met, was incredible. We had a booth set up. We were doing demonstrations there.
We would have surgeons walking up to our booth and say, "Hey, I'd really like to understand what you guys are doing, and can you show me how your tech works?" And we would ask them, "How did you hear about us?" And every single one of them said, "Well, I've got a colleague that uses your technology that told me that I need to come check it out," or, "I've got a colleague right now in my hospital that's using the technology, and I kind of want to see what it's all about because they would like me to start using the device that they've got." So our pipeline is extremely strong and extremely deep. At this point, it's a matter of scaling up our sales force to be able to service it.
Okay. Thanks. I'm just doing a time check. We're just shy of 3:00 P.M. So I think most of the questions have been answered. I've got, I guess, one more that we can address, and then I'll just leave it to you, Adrian, to give some closing remarks. So last question is, and this is a series of questions. It was said that the biopsy and pathology markets can be expanded without large, sorry, without large cost. Can you just elaborate as to why and on the trial approval path needed to do so?
Yeah. Maybe I'll start with the first question about the cost, and I'll hand to Andrew to talk about the approval process. But what I meant with that comment was that a lot of the core R&D effort required to address those markets has been developed. So we have our OCT imaging technology. We know how to capture those images. We know how to manage those images. We know how to use that to put it onto a screen for a user to look at. There's a lot of development that went into that. That already exists for the surgical suite, and that exact same core technology can be used for the other applications. The form factors will change. We won't have the same device that we have in the surgical suite necessarily for pathology and for biopsy, but the core imaging technology has been developed.
And likewise, the image library that we have, the AI algorithms that can identify different structures within the images that was created for the purpose of using the surgical suite can be reused for those other applications as well. Now, the form factors are going to change. The go-to-market will change. The specifics around what that solution looks like will change. And that is some development work that needs to be done. But as I mentioned, it's not what I would call research work. It's more engineering work. And so there's a much lesser cost and much lesser risk to that. So that's the first part of the question. And Andrew, maybe can you speak to what that may look like in terms of how we would get through regulatory to get that into market?
Yeah. Anything that you want to put out into the market in the med device world, you're trying to establish claims, some marketing claims of what your technology can do. So we could easily get a device into radiology and pathology using similar claims to what we have today, which is as a general tissue imaging device for the S-Series. So the regulatory burden or hurdles will be really low. The clinical validation will be very simple, and we could use our own existing technology as a predicate.
As you get established then in both radiology and in pathology, and you get more specific to maybe different cancer types or if you wanted to add AI, again, we would have predicates in place based on the B-Series and based on the S-Series that we can point to with the FDA to say, "You've already approved this technology or something that is substantially equivalent to this. These are the modifications that we're making, and that's a well-understood path through the FDA." So I would see both the clinical validation and the regulatory hurdles continuing to drop as we put our first technologies out and establish them as the equivalents that we will point to.
Okay. Super. Thank you. We did have one last question pop in that I think is important for you to address, and then I'll let you have some closing remarks. Are there any licensing opportunities beyond tissue margin application?
Yeah, potentially. As we talk about those other application biopsy and pathology, one of the strategic questions that sits in front of us is how do we address those markets? I talked about us having core IP that can be valuable in those markets, but that doesn't necessarily mean that we need to build, manufacture, market, sell, whatever that solution looks like. That's an opportunity perhaps with a partner which we could work with, whom we could work with, where our participation in that might be a licensing fee where we take that IP and we would license it to one of those partners. That's to be explored. I think there are licensing opportunities definitely with what we've developed. We'll have to sort of see how that evolves as we explore.
Yeah. Maybe I can just add on to that. And it's not actually covered in the main body of our presentation, but I think most people, when they are thinking about Perimeter in terms of AI, they think of this AI technology that they have finds cancer in their images. We also have several other AI initiatives to speed up the OCT imaging to make the images look better with higher contrast, and then also to start to do some generative work where we're actually generating our own OCT images synthetically, and we can then use them to train. The first AI approach to making it faster and making the images better could potentially be licensed to all of the big ophthalmology and cardiovascular imaging companies that use OCT.
And then also our approach to generating OCT images synthetically is very novel, and that could also be leveraged by other companies outside of our competitive domain who would use OCT.
Super. Thank you very much, Adrian. Maybe some closing remarks, and then we'll end the call.
Yeah. Thanks, Glenn. Thanks for everyone for joining. Hopefully, we were able to explain a little bit of what the tech does and what our strategy is for bringing it to market. I can tell you within the company, we're super excited with some of the evidence we've started to see and we've been able to share. We're, I think, at this point in time, sort of at a cusp of where the business, from a commercial standpoint, starts to accelerate. We're seeing some of that data internally, and it's exciting to us. I talked about our very deep sales pipeline, and the building out of our sales force is really the only thing standing between us and really ramping that revenue. So we've got a big focus on that. We're super excited about the business.
I think we're at a point over the next year or two where we're really going to be able to see it grow. And hopefully, we've been able to share some of the excitement with you today.
Super. Thanks, Adrian. Thanks, Sarah. Thanks, Andrew. And most importantly, thanks to our audience. This concludes this presentation.
Thanks, Kris.
Thank you.
Thank you.