I guess we can get started. Thank you. I'm Adrian Mendes. I'm the CEO of Perimeter Medical Imaging AI. I'm going to walk you through the story. I don't know how many of you are familiar with it, but we'll go through. We'll leave some time for questions at the end. Forward-looking statements. We're a FDA-regulated company. There's indication of use for a couple of products that we have on the market right now. All right. This is the problem we're here to solve. We didn't get it all. It's something we don't want to hear as patients. It's something we don't want to hear as a company trying to address it. We've got some technology that I'll talk about that helps surgeons make sure that they can get as much of the cancer out in that very first surgery and avoid having the patient come back in again for a second surgery. Okay? That's the purpose. That's the problem that we're trying to solve as a company, that second surgery problem in cancer surgeries. This deck, you can also get off our website. You don't have to take pictures or anything like that. It's all up there. Just a quick high-level summary. We are the first FDA-approved imaging device with AI on it for breast cancer surgery, which is a big step forward in that there's a lot of breast cancer surgeries, about 300,000 a year that happen. We're the first one to have any sort of AI in an imaging technology in that operating room. We did complete a clinical trial for the product that we just released, that just got approved in March of this year. We're sort of the front end right now of commercialization. Successfully hit our endpoints. There's a few other companies on market, none of them have hit their endpoints. We're the only company addressing this market space that has successfully hit our endpoint. That was great. It's a very large market opportunity. Like I said, 300,000 breast cancer surgeries a year in the U.S. alone. We have a recurring revenue model, where we monetize every procedure through a disposable mechanism. We're in commercial. We've got a very nice, strong competitive moat, which I'll talk a little bit about. Here it is by the numbers. In our previous generation device, our earlier device called the S-Series, we've done over 4,000 procedures commercially over the past few years on that device. We have a very large image library. We've got over 2 million images of healthy tissue and cancerous tissue that then helps us to train our human readers of the imaging system as well as our AI algorithms, of course. That took years to build. That's one of our moats. It's a huge value add for the company. It's one of the reasons I joined the company three years ago is because of that moat. We're in a number of hospitals, a few dozen hospitals throughout the U.S., a few different specialties. We are primarily focused on breast cancer. We do have surgeons that use us, that have investigated us for head and neck cancers, for skin cancer, for lung cancer. The base technology is applicable for pretty much any sort of solid state cancer. We start with breast. It's the largest market, that's where we're starting. We've got patents. We've now launched. I think that's all really to say there. Let me talk about the problem a little bit more, one level down from just the high level. Getting all the cancer out during surgery, what does that mean? How does a surgeon know that they got it all out, clean margins? You've probably heard this phrase if you've ever experienced cancer or had it in your family. The idea is basically when the surgeon takes the tumor out, they want to make sure it's fully encapsulated with a few millimeters thick of healthy tissue. If they do that's considered to be a successful surgery and the patient doesn't need to come back. If they don't, if they look at it after it comes out and there's cancer cells right on the surface or even to the depth of 2 millimeters, there's a presumption that the surgeons cut through the middle of the tumor, and they left some of the tumor back in the body, and then that's what leads to a reoperation, patient having to come back. Okay? It's a high reoperate. You can see some of the numbers there. For breast cancer, it's almost 25%. Like I said before, this is a prolific problem across all types of cancer surgeries. It's very, very hard for a surgeon with the tools they have to be able to see those cancer cells, feel them. That's what makes it so tough. Even the best surgeons have reoperation rates where patients have to come back. If you want to think about our company in the terms of technology, there's two basic technology areas that we have. The first is this. This is the imaging modality called optical coherence tomography. It's been around for a long time. We've all had it. When you go to the optometrist and they do a scan of your retina, that's OCT. The best way to think about it's like ultrasound, except it uses infrared light waves. What that gives is a very, very high-resolution image with a few millimeters of penetration into the tissue. Okay? The resolution is small enough to see individual cells, and that's important if you want to look for cancer cells and be able to distinguish between cancer cells and healthy cells. Okay? No other imaging technology has that type of resolution. We're at around, say, 15 micron. A micron's a thousandth of a millimeter, about 15 micron resolution. X-ray is about 100 micron, tenth of a millimeter, right? Cells are smaller than 100 microns. If you can't have a camera that sees something less than 100 microns, you can't see cells that are less than 100 microns. We're at 15. That's the key thing for us, right? This imaging technology, everything else sits on top of that, the ability to see something that other modalities cannot. Secondly, the ability to see into the tissue. If you remember what I said earlier, a successful surgery is when you take the tumor out, and it's encapsulated by a few millimeters, 2 millimeters for breast cancer, of healthy tissue. You have to be able to inspect all the way through into those 2 millimeters of depth. That's what OCT allows us to do. It allows us to have that high resolution into the tissue type. In conjunction with that, there's an AI layer that sits on top of that, which then helps the surgeon be able to identify those areas after looking through those hundreds and thousands of images for the patient sitting right there, be able to see if there is any cancer in that 2-millimeter margin. If there is, because it's happening in real-time, the surgeon can go back to the patient who's a couple feet away and take a bit more tissue, versus not finding out till when they get home and then having to bring them back to the operating room. Okay. Claire is the name of the new product. The older product had the imaging technology, not the AI technology. What did that mean? That meant that the users, those 4,000 procedures we were talking about and the several dozen hospitals, the surgeon had to look at the screen without an assistant and scan through those hundreds and thousands of images, to find cancer cells, okay. To find something suspicious. Based off of that, they would then act. It works. They can see that. We've proven that you can tell the difference between healthy cells and different types of cancerous and unhealthy cells, it takes training, it takes effort to do, right. That's what we've been running the business, that's what we've been commercializing so far. Last year, a few CAD million of revenue. Earlier this year, we got FDA approval for basically the exact same hardware setup, the exact same imaging system, but with that trained algorithm sitting on top of it. Now instead of the surgeon having to go and scan through all those hundreds and thousands, the algorithm does it inside the body of the machine, then puts up a screen of thumbnails of however many images it sees, like how many different frames it sees that might have something in it. The surgeon can look at those dozen or half a dozen or whatever images. It just makes it a lot easier and gives them more confidence that they've found something in there versus trusting their own searching. I'll flip back here for a moment. The trial was run across dozens of top cancer centers in the country. It concluded at the end of 2024, and that was what fed into the FDA application. What was the endpoint of the trial? The way the trial was designed was the surgeon would do standard of care all the way through, right to the point where they're going to close the patient up without having to use the Perimeter device. Then they would open up an envelope, and the envelope would say either, "Close patient," or, "Use the Perimeter device." So if they use the device, two batches of tissue go down to the pathologist. The pathologist looks at the first batch and say, "Is it clean margin or negative or positive margin?" Then would look at the second batch and say, "Are there a positive or negative margin there?" So the endpoints were tracking how many times, how many patients had a positive margin before using the machine, and how many of those patients, the surgeon was able to clean it up after using the machine. So this is a pictorial, basically standard of care versus how a surgeon would do their procedure with our machine. Typically what happens without a Perimeter device is a surgeon excises the tissue, sends it down to pathology, which takes a few weeks to get an answer back. Of course, the patient's then sent home, then the surgeon calls them back afterwards and says either, "We have successful surgery, clean margins," or, "We have a positive margin. You need to come back in again." Okay. That two-week timeframe is problematic. That's the problem. With us, they take the tissue out and just like I described, scan it inside the operating room, execute follow-on action with the patient, then send the patient home. So with that, they're able to catch more of those positive margins than they would've been able to otherwise. The key is that real-time information feedback. The market. The market is huge, of course. I talked about breast cancer alone at 300,000 cases per year, which is our initial launch. The AI is trained on breast cancer. There's nothing inherent about the imaging modality or the machine that constrains it to breast cancer. Like I said, we do have surgeons using it for other indications as well. The market grows from there, right. Right now we're FDA approved, so U.S.-based markets where we're starting for breast cancer, we expand out after that. We have very supportive surgeons. We're gaining awareness within the patient community as well, which as you can imagine, is a big piece of our go-to-market strategy, to make patients aware. The way I think about this is to date, there has been no solution for this, and you see those really high reoperation rates for cancer surgeries. Because of that, patients, surgeons, everyone assume that that's just the way it is, right. That's just life. You're always going to have a higher-ish risk of having to come back for a second surgery. The fact of the matter is you don't have to have that, right. You don't have to live that way. There is an element of our marketing now that we have the indications from the FDA approval that we just got, where we can say some things like breast cancer and impact on reoperation rates and margin assessment. We can speak language that patients would understand, and we can advertise that. This is going to be a big piece of our marketing campaign going forward to drive demand onto the hospitals and the surgeons. There's value across the continuum of care. Every stakeholder has some positive value from using this. The reduction in reoperation rates, there's clinical value in that, both for the surgeons as well as the patient for not even economic reasons, just like the health reasons. There's operational value in that time delay. While that patient is waiting and wondering and they're full of anxiety, they're trying to figure out what's going on. There's value there. There's huge savings to the healthcare system if you don't have to pay for another operation when it could be done right the first time. Breast cancer is not the most highly profitable procedure either for you to use nurses and anesthesiologists in operating rooms, which are constrained. The hospitals are highly motivated to be able to flip that operating room to some other procedure. They can make more money there. Getting these reoperation rates off the table, so to speak, is very valuable to everyone. Maybe most important is this graph in the right-hand side here. The five-year survival rates on if you differentiate between whether you have a positive margin or don't have a positive margin after that first surgery, and you can see it drops. The survival rate, if you look at breast cancer, for instance, if your first surgery is successful, you have a 90% survival rate after five years. If it isn't, you have to go back in for a reoperation, it falls to 80%. Okay? That's breast cancer, not great, but some of them are even worse. Head and neck cancer, it drops. 55% is not great in the first place, but it drops all the way down to 10% if you don't have successful first surgeries. There's a huge impact, and it comes from many different things. Cancer left behind, infections, longer recovery time. It just adds risk to the patient. If you can get it done in one procedure, so much the better. Okay. Looking at this from the hospital perspective, there is significant economic value to the hospital, and we've got some estimates here of these major buckets. The first is just direct cost avoidance. The cost of having to spin up that operating room, pay for an anesthesiologist, pay for a surgeon, nurses, et cetera. There's that cost. The efficiency, the effect on the operating room, that operating environment when it's being used for that procedure versus some other procedure. The patient pathway. There's savings there as well in terms of the effect on the patient, what other follow-on therapies they have to have after the fact. Then the system-level institutional reputational value, their ability to attract more patients into their system. Their ability to have a deeper relationship with the referring physicians because their reoperation rates are better. There's costs all across the board, and these are things that we talk to the hospitals about. We don't have reimbursements right now for the S-Series product, yet hospitals are still coming to us and saying, "Hey, we need to get this product into our system." One of the nicest things I like about what's happening right now is our surgeons are liking it enough to start to talk about it publicly in the market. If you go to our webpage or look at our social media, just a few days ago, one of our surgeons in Phoenix did an interview with the local TV station in Phoenix talking about our product. Talking about other products, but talking about our product by name, about how it helps her take care of her patients better. These physicians and these hospitals are using it also to market their practice in the communities. This is about the AI. I put this slide here in the commercialization section rather than put it back in the technology section for this reason. The AI gets better continuously. We got FDA approval on that second one there, Claire with AI 2.0. Okay? That was the algorithm that we ran through the trial, and we hit our endpoints with the algorithm. We got that approval in March. Up until that point, our AI was frozen. We couldn't do anything with it. We got approval through something called a Predetermined Change Control Plan, or we got that approved as a part of the FDA approval, which basically is a plan for us to continue to upgrade the AI without having to go back again for another six month or one year round of reviews with the FDA. Super important for algorithms. Algorithms are developing very quickly, obviously, in the world. We all know that. Our database is growing every day from all the procedures that we're currently using our product on, and there's so much room to improve. The trial was successfully completed on an algorithm developed back in early 2023. We're now three years later. We've been able to already release the next generation of that, what we call 2.1. We released that about a month and a half ago. We've got further plans for improvement on that algorithm. This is very important for a few reasons. One is, of course, it helps our product get better. From a commercialization standpoint, the fact that we are the first AI in the operating room is interesting, but unto itself, it's nothing but a bragging right. This allows us to continuously improve the product in ways that are very difficult for anyone else to improve their product, because of the flywheel we have going with more data, better results, more customers, more data, better results, more customers. We're a small company. We've got 35 people. We were founded in Toronto. Most of our employees are now down stateside. Our U.S. headquarters are in Dallas. Those red dots indicate the different geographies we've got our current customers in right now. Those boxes are an indication of the number of procedures in each of those different geographies. What I've highlighted in green are the regions we're focusing on now this year. There's a lot more out there. We're being very focused and targeted for a few reasons. One is for capital management. Two is, I've been running these types of companies, small startup technology companies for a while, and one of the common failure modes is you grow your team too quickly, and you don't really know how to utilize that team and how to make it effective doing whatever they need to do. This allows us to be in three markets where we have customers, where we have loyal customers. We can leverage off that to have sort of an amplifying effect in those markets where we have local teams, and we can continue to develop, so to speak, the commercial playbook in those markets, and from there, start to copy and paste into other geographies. I think the point of this slide is really there's a huge amount of potential even beyond the areas that we are currently in today. There's three angles to our commercial strategy, three pillars, I suppose. One is convert our existing customer base from the older product to the newer product to Claire. The second is new placements. We're very well known within the surgical community, breast cancer surgeons, and some of them have gotten onto our old product, and some of them were waiting for the AI for it to be easier for them to use. They've all been following us very closely. We now have that AI in market, we've got this strong pipeline of customers right behind them, behind our current customers. Then drive system utilization. You put one of those machines in a hospital with the first surgeon, there's usually two or three other surgeons also doing breast surgeries or even other types of surgeries that we can then get onto those machines. This year it's about those three. Those are our three pillars of our commercial strategy. I talked a little bit about this, recurring revenues. We have our platform, the cart that sits in the operating room. We have a disposable that's used one per patient. It touches the tissue, so we have to discard it after the procedure's done. One per patient. Then we have service contracts, then we have software that we also contract with the customers on. There's two nice things about this. One is it does give us a recurring revenue stream. Two, it allows us to monitor very closely if surgeons' volumes are going up or down, then react appropriately if they're going down, proactively as opposed to waiting for three months and realizing they haven't ordered anything. We get this sort of in real time. The market is large. The margins on the disposable-- the top line, it's very, very large for a company our size, for any company really, and it's high margin. Those disposables have over 90% gross margin on them. As this business scales, that's great for just delivering dollars to the bottom line. The moats around this company. One is how do you build the hardware? How to build a camera. Very hard, very difficult. Took us years to develop and figure it out, but it's heavily protected, both from an IP patent perspective in the U.S. and Europe. Also just as in a technical, how to do it. Even if you didn't care about patents, it would take you a long time to rebuild the cameras and rebuild what we've got. That's on the hardware side. Then on the AI side, it's very difficult to recreate the library we have. You can't buy this off the shelf. We're the only ones who have the camera. If you want to have the pictures that train the AI, you have to have our camera, and I just explained how it's hard to recreate our camera. You also need to take those images in an operating room because you need the tissue to be fresh out of the patient to capture images that are going to be useful to the algorithms because the surgeon's using it in real time. It's not like you can go to a tissue bank and get a bunch of tissue and take images and recreate a library of that. You have to do it one by one with patients in an operating room. These are two huge moats for us that becomes very, very hard to replicate or recreate around us. That's the leadership team. Andrew Berkeley is a co-founder, our Chief Innovation Officer. He's still with the company. Company's about 12, 13 years old now. He's still with the company. I joined three years ago. Sara, our CFO, joined three years ago. I was the Chief Operating Officer for a company called Groq, for six to almost seven years, before I took this job. That was my experience of how to build up a company from nothing to something. That was an AI startup company. Carl Gazdzinski is also in Toronto. He's been an engineer, so he leads our engineering effort up there, been with the company for a long, long time. Abbey Goodman's our VP of Sales. She's got experience ramping small startup med device companies, in the breast cancer space from zero to CAD 10+ million of revenue. She's the perfect person for where we are right now in this marketplace. This is our capital structure. You can see the number of shares outstanding. I think there's three key points there I think is important. One is we've got a strong cash position right now. Two is strong insider alignment. Between myself and Social Capital, we have about a third of the company, about 30% of the company, plus a quarter of the warrants that are out there. Strong insider participation. We continue to invest to help drive the company forward. Social Capital is Chamath Palihapitiya's fund. He was also the lead investor, the first investor in Groq, and he introduced me to Groq as well. We did that one over there, and we're doing this one over here now. In the last round that we closed just a few weeks ago, a month ago, there were a lot of healthcare specialists who are starting to move to become more relevant now to smaller institutional investors, folks in the healthcare that understand what we're doing here. That's nice to start to move the cap table, to those folks. This is a summary of everything I just said. I'm going to stop there and see if anyone's got any questions. Yeah. Since the inception of the company 12- 13 years ago. Yeah. What's been your total R&D cost? I think we've spent CAD 70-ish million. Yeah. 7-0? 7-0. Yeah. Okay. Not all in R&D. There's other expense as well, but that's the total capital raised. Yeah. Okay. In terms of developing first generation and, you know. It's probably about CAD 50 of that, I would say. Yeah. In terms what the business model is, are you selling the Claire device to hospitals or leasing it out? Yeah. What kind of price is it? Yeah. Have you given any kind of guidance on how many devices you plan to sell this year? Yeah. Next year? That gives us an idea of how revenues could build up. Our business model is, we have a couple different ways. One is we will place the device at no charge if the volumes are high enough or we will sell it. Okay? I'm not going to talk about pricing, but I will talk about gross margin. When we sell the cart itself, it's about a 50% gross margin. When we sell the disposable, it's about a 90% gross margin. Okay? The margins are very healthy on both of those. If we don't sell the cart and we place it at no charge, we recoup the cost of that within about a year. I've not given guidance. We have not given guidance right now. I've found it's not valuable to give guidance at this point because it's so lumpy and early on, being publicly traded, the moment you miss your guidance, which is based off a very large spectrum of what the possibilities could be, you're dealing with a whole other problem. Maybe the last question I make is on the capital structure there, you have a lot of shares. Yeah. Does it make sense to reverse split or something like that? Yeah. We're right now on the TSXV exchange. We've got a plan to bring us onto NASDAQ over the next year or two. At that point in time, we'll consolidate and we'll do the reverse split, bring the price up. With your existing placements, are the doctors or the hospitals using Claire for all of their breast cancer procedures or are they choosing a certain segment? Yeah, when they start to use it, they start with a certain segment, DCIS and IDC is where they start. That's our foothold. As they start to use it, they realize, "I don't want to not have this visibility anymore," and they start to expand usage. We even have surgeons now starting to use it for some of their mastectomies, for the nipple-sparing mastectomies. Look on the backside of that to make sure there's no cancer there. We see a nice thing where they use it for a high portion of that, and they expand from there. That goes back to my increasing utilization point. Yeah. It's not going to happen. I'm getting-