Thanks for coming. I appreciate that. My name's Adrian Mendes. I'm the CEO of Perimeter Medical Imaging. It's a Toronto-based company founded here in Toronto, actually, although about two thirds of our employees are now stateside, and we' commercializing in the United States. Okay, so we're making forward-looking statements, standard disclosure. We're an FDA-regulated company, so we have two different products. One is in market already, what we call the S-Series. That's already in market earning revenue. T hen we have a next generation product that is coming through that's being reviewed right now, what we call the B-Series. Okay, so everyone's heard this phrase before, hopefully not personally or with their loved ones, but probably they have because it's so common. W e didn't get it all right. So here's a problem we're trying to solve, making sure that the cancer surgeon can get all the cancer out on the very first operation, and they don't have the situation where they're calling the patient a week, two weeks later and saying, "Sorry, we didn't. We had a failed surgery, and you got to come back in for another one." Happens way too often, so this is what we're working on. Okay, so just a little tiny bit of background. What a cancer surgeon is trying to do when they do the surgery is they're trying to get what they call clean margins, so it's pretty simple to understand. Take the tumor out, make sure it's fully enclosed, encapsulated with some healthy tissue, a few millimeters thick. If you can do that, then you can be pretty certain that what has happened is you got the whole tumor out, and you didn't leave any cancer back in the body. T hat's what the picture on the left hand indicates. If you do have a problematic surgery, what has happened is you've got the picture on the right, a positive margin, what they call it, where some of those cancer cells are right at the edge of the tissue you pulled out, which means you probably cut through the tumor, which means you probably left cancer back in the body, which is what you're trying, obviously, not to do. The trouble right now is you don't find out about that until a couple of weeks after the surgery is done. T he other problem is it happens a lot. So those percentages you see in the bottom right-hand corner are the percentage of time that the surgery fails for different types of cancer, right? So 23% for breast cancer, 12% for thyroid, 21% for prostate. T hose numbers are kind of consistent around that across the board: lung cancer, throat cancer. It's very, very hard to solve this problem. The reason it's hard to solve the problem is because what you're trying to do is you're trying to detect in those margins cellular level, like, is that a cancer cell or a healthy cell? You can't see it. You can't feel it. O n the edges, it's really hard to detect. The tools that surgeons have in the operating room don't allow them to visualize at that level of detail. T hat's the problem they face. So they've got poor tools. They've got poor imaging tools ahead of time. Things change from when the mammogram was done, or whatever was done earlier, till the actual surgery itself. T hen that causes these defect rates, 20% plus. T hen what's the net result of that? So think of three major constituents. The first one's super easy, right? You're a patient, and you then get a call two weeks later. You know, if you're lucky, you live in a big city, and you can actually get back to the hospital without really impacting your life. If you're not lucky, you live out somewhere, and maybe you're a single mom if it's breast cancer, and you got to take time off work again, and you got to get into the city and find childcare and all the rest of it, right? So it's very problematic from that standpoint to have to, like, disrupt your life again to go under the knife, not to mention just the trauma of having to be put under is horrible for anything to have to do it again. Increased rate of complications. So we've done some pretty broad studies. 66% of repeat breast surgeries result in a complication on the second surgery because you're cutting open that not yet healed wound again two weeks later. T hat's very bad. So from a patient standpoint, that's horrible. From a system standpoint, the hospital systems is also horrible. There are constrained resources, ORs, nurses, which you're now utilizing to fix a mistake that should have been done right the first time. You've got surgeons who are trying to build a practice. If you think about the United States, where it's a competitive market, you're trying to attract patients to your practice. You're trying to build your practice, but this doesn't help if you have a high re-excision rate, and then the economic burden, the people who pay for it, the insurance companies, private payers, the, you know, Medicare, Medicaid, whatever, have to pay for that second surgery, which is super expensive also. So across the board, just getting the job done right the first time is a huge goal. What we do is we use a type of imaging technology called OCT, optical coherence tomography. The simplest way to understand it is it's like ultrasound, but uses infrared light waves, and same concept, shines a light on the tissue, reflects back, captures it, creates an image, and that image is very high resolution. So you can see down to 15 microns, which is good enough to see cells. Okay, X-ray, which is the next best out there, is 100 microns. Cells are in the 20-ish microns, 15, 10 to 20 microns range. So if you only see things 100 microns small, you can't see cells, but you have to see cells to see if you have clean margins. So we can do that, and we can do it without touching the patient. We do do it without touching the patient. You know, it sits in the operating room, but off to the side. We don't have to inject anything into the patient, dyes, or anything like that. So, and we can see deep into a few millimeters deep into the tissue. So it doesn't just look at the outside. It looks through that entire several millimeters thick rim. Okay. This is what it looks like. That top left-hand corner is what the surgeon sees in real time. Right? That white sort of circle area is cancer. If they see that, and then the top of like where the black and the white sort of meet, that top is a surface. The entire depth there is two millimeters. If you look at this as a surgeon, you're saying, "Oh, shoot, I got cancer almost right at the edge. That's problematic. I probably left cancer back in the body." I understand now which part of the tissue that is, and the patient's still sitting right there, and I can take a little bit more and like make sure I get those clean margins. The bottom picture is what the pathologist sees two weeks later. So this is super important for a surgeon to be able to know that what they're seeing in the operating room is what that pathologist is going to see two weeks later, and send them to be able to do the work they need to do with confidence that there's a correlation, and you can see that there is that correlation there. That's the same piece of tissue imaged two different ways. Okay, so how does all this technology encapsulate it? There's a cart that number one, which is where the camera's inside. Think of it like a photocopier, or the camera's pointing upwards. You put the tissue on top of it. The tissue touches the machine. So we actually don't. We have a container that you put the tissue into. So think of a razor blade model, right? So the Specimen Immobilizer is a single-use container. So you have to use a new one for every patient, which gives us great data on how often machines are being used. It's a very high margin thing, over 90% gross margin on that consumable container. And so that's how that sort of like operates from a hardware standpoint. T hen we've got a very big library. So we've started with breast cancer, because that's the highest volume cancer out there. There's about 250,000 surgeries a year in the United States every year. So it's a very, unfortunately, it's a very big market. So we have a lot of images that we capture. Some of the images we, when we first started the company, captured it here at Princess Margaret. Since then, we've been expanding into the United States. We've got a whole bunch of images, over 2 million right now with the current device on market. We use it to train human readers, our surgeons, our users. You can learn how to recognize that picture of cancer. O f course, we've also used it to train an algorithm which is going through our FDA process right now, which I'll talk about more in a minute, and this basically kind of shows what standard of care is right now versus if you use our machine, so if you go through standard of care and you have a surgeon that isn't using our machine, they take the tissue out, send it to pathology, send the patient home. A week or two later, the pathologist comes back and says, "Successful surgery or unsuccessful surgery." If unsuccessful, you're then on the phone with that patient, calling them, giving them the bad news, telling them they got to come back in, and you have to do a second surgery. That's two weeks later. With us, take the tissue out, patient still sitting there 10 or 15 min. You're scanning all sides of that piece of tissue. Y ou see the pictures that I showed you before. Okay, I got it. I missed a little piece here. Take a little bit more, put that onto the machine, scan it. Okay, looks good. Close them up, send them home. You can have a high degree of confidence that you did the job right as a surgeon. You do send everything down to pathology. They come back a few weeks later. W ith what we've seen now, the re-operation rates for our surgeons for the device around the field are much, much lower. So we've had about 3,000 patients go through our machine so far in our commercial, like earning revenue off of it. Those surgeons, versus the 20-ish% national average re-operation rate, are operating at a 3-4% re-operation rate. It is pretty big. It is not like shaving 5-10% off. It is almost an order of magnitude better. Next-gen is where the AI comes in. Okay, the picture on the left side is what is available right now with that screen. That is actually the exact same picture that was on the previous slide. That is what they see right now in the S-Series. 10 or 15 min to scan. You do a manual review by the operator inside of the operating room, and they are reviewing, you know, several hundred to a thousand images to look at the whole volume. Okay, what is going through FDA approval right now is the middle image, where what we do is we capture all those pictures. Then we have a GPU inside the machine that runs the algorithm across all the images, and then it highlights the half a dozen or whatever pictures where it's like, "I think I see something here," pops on the screen. The surgeon looks at those half a dozen images and says, "I agree, I disagree," whatever makes their decision, and they're off. Makes it faster in the operating room for the surgeon. Number one. N umber two, the thing I'm most excited about is the learning curve for a brand new surgeon to adopt this technology goes way down. So without the AI, you got to like get pretty good at reading that image on the left with no assistance with the AI. Now the AI is doing all the heavy lifting for you. So now, instead of having to look for that needle in the haystack, you're really just having to look at a couple, you know, half a dozen pictures. T he AI is going to tell you exactly where to look. T hen you can make your decision. So it's way less scary for a surgeon, right? T hen, of course, in the lab, you know, we've got next generation algorithms that are going to be even better, more sophisticated. Put the nice box around the suspicious area. A s soon as we get this current one through the FDA review process the next few months, we'll launch that one in for review. So we conducted a significant trial which wrapped up several hundred patients, wrapped up end of last year, submitted our application early this year. That'll come through in the next few months, probably Q1, just over Christmas, Q1 sometime. We'll get approval on that. What that'll end up giving us then is a few things. One, it gives us the AI showed you before. All the hardware is all the same. So I don't need to build new machines. I just have to load on the AI software. So it gives us that. T hen the other thing it gives us, if you're familiar all with FDA label claims, the ability to say what your product does without selling snake oil is the thing the FDA was created to do. So they really restrict label claims right now. I don't have a label claim on breast cancer re-operation. I have a label claim on the current device on the ability to observe cellular structures. With this, because we're in a huge trial on breast cancer patients, I will get the claim to be able to say that we can help address breast cancer re-excision, re-operation problems, which then allows me to market much more clearly and very distinctly, not only to surgeons and doctors and physicians, but frankly, also to patients. Y ou can imagine, if you have to go through surgery, you know, why wouldn't you want your doctor to use this? Right? So it really supercharges our marketing efforts in the new year. It's not just a breast cancer problem. It's an all cancer problem. So the graph on the left-hand side shows, you know, how many surgeries per year, how many re-operations per year happen across the different types of surgeries, and then how many dollars that costs us every time there's a re-operation. So it's kind of across the board. Breast, as you can see, there's just the most number of surgeries and breasts, but it really stretches across the board. The right-hand graph is maybe more important. So that's a 5-year survival rate. Blue bar is your 5-year survival rate for different types of cancer if you have successful first surgery. So for breast cancer, it's not bad, right? You get a 90% chance of living past 5 years if you have a first-time success. The problem is, if you don't have a first-time success, then your 5-year survival rate drops way down. So you get a 10%-11% hit for breast cancer. But you get massive hits for head and neck, prostate, you know, kidney, liver, lung. So this is a problem that has value for breast cancer, but has even bigger value for other types. Where we are right now is we're either, you know, we've got this big library, 2 million images of breast tissue. We're building up libraries across other. So we're building our libraries on lung right now. We're building our libraries on head and neck cancers, melanoma. T his is kind of how we like grow the markets of this business over time. T hen to markets. So if you take all that global, you know, it's a multi-billion market, billions of dollars, valuable in the operating room, valuable during biopsies, valuable during pathology after the fact. So there's multiple revenue streams we can get on this over time. And then if you kind of like go down to, let's just say, just the U.S. market, just breast cancer surgery market, even that's a very big market from where we are right now. There's a lot of data, but I'm just going to quickly go through. The most important point here is that right now we have a strong amount of demand with a very tiny sales and marketing force, and it's being led by the surgeons that are using our device. So they're telling their colleagues, their colleagues are saying, "Hey, I need to talk to Perimeter about this, because I heard from, you know, Dr. XYZ in whatever city that their operation rates are way down at the 3% range, and they're now marketing in their community to get patients to come to their practice. I'm in a different city, and I want to do the same thing over here. So come and like, you know, help me bring your machine to me. It's this surgeon-led thing, which is kind of the best way you want it to be, right? Where you've got this organic demand creation channel going for you, which is where we are at this point in time. S o the left graph shows like one surgeon who was able to, in the first 72 patients, very first 72 patients, he just got the machine and was able to cut our re-operation rate down, you know, all the way down to around 5%. We talked about the big market, and we talked about the different hospital benefits. This is how we've been growing revenue historically. So there's a bit of a phase change. In the early days of the company, we had a business model where we were placing that machine, the cart in the operating room at no cost to try to get data, right? Just get it out there, get people using it. And then we were charging for the consumable. We started to charge $750 for the consumable last year. Early last year, we started to raise the price at $900, didn't lose any customers, continued to get new customers. Late last year, $950. This year we are up above $1,000, still losing no customers and gaining customers on that consumable. The other thing we've been doing this year is we've been starting to sell the actual cart itself for, you know, about 50% gross margins or a few hundred thousand dollars per cart. So now our revenue stream is starting to look much healthier, right? So we've got cart sales at a nice 50% gross margin. We've got those consumable sales at a 90% gross margin. We've got service contracts. We sell on these things, annual service contracts for $20,000-$30,000 a year. So the business is starting to get robust. We'll bring the AI out next year. We'll add an AI charge as well, which is, you know, software, right? So that's a 100% gross margin business. So the healthy, I mean, it's a very, very healthy for a hardware device company. You know, we're projecting right now, we're sitting a little bit south of 70% gross margin. We'll be over 70% gross margin in the long term as we grow the business out. Let's see. So from a market penetration standpoint, like I said, we're going through the FDA. So we're going through the U.S. market first. From there, we'll expand outside the world. But just to focus on the U.S. for the moment. Typically, the way medical device companies and maybe all healthcare companies started up in the Northeast. You've got Boston. You've got New York, high-density population. You get a small sales force and kind of move your way through the country from the Northeast and Great Lakes region down. We, after Toronto, our next biggest location is Dallas, so our U.S. headquarters is in Dallas. The reason for that is we got state funding to fund the clinical trial I talked about, and the stipulation was you got to build your U.S. headquarters in Texas, and so we chose Dallas, so we've been expanding from the South north. So we've got a pretty strong presence in Dallas right now in Texas. Where the dots are is where we have devices right now with customers, and we're growing out from there. The nice thing about that is that the most, the juiciest markets we haven't even gotten into yet. Although that Rolodex of customers who we know, like who they are and they want us, are all kind of all over the country, all over the country, but a significant amount of them are in that area I was talking about, New York, Boston, New England, Great Lakes region, Chicago, Cleveland. S o this is the next step. Build up the sales force, start to populate, put them in these places, and then start to grow the revenue in these locations, which we haven't even been able to touch yet. From an IP perspective, very well protected. So we've got patents around the way that OCT imaging, you know, machine works. So a lot of patents covering that. It's very technically hard to do as well. So you read the patents. There's a lot of just core engineering work required to make it work, and then on the AI side, the AI is, the algorithms are, they're just, they're not too complicated, but there's some complexity built in the algorithms, but really, the value is the library, the image library we have. You can't duplicate it with the camera. We're the only ones who can make the camera, so we've got a very, very highly protected library around which we can build, build more models off of, and then once you've got that data library, there's ways of monetizing that over time that we haven't yet explored, so that's another huge asset that we have sitting on the books. This is the management team. I'm the CEO, so I joined this company. I was not one of the founders. I joined this company a few years ago. The company was founded about 10 years ago, about two and a half years ago. Previous to this, I was a chief operating officer at a company called Groq, which is an AI hardware company. So we grew that company. A couple of guys came out of Google. I joined them really early on. We grew that company from there until I took this job to about 200 people, next generation, over a $1 billion valuation. The same lead investor in that company is a lead investor in this company. That's how I got introduced to this one. So I joined, did a little bit of a turnaround on it. Andrew Berkeley is the Co-Founder. So he's the guy who's behind the technology based here in Toronto. Sarah Bryan is our Chief Financial Officer, also based down in the United States. Dr. Ted James. So he's a breast surgeon, ran the breast surgeon school. I guess you would say, at Harvard for years. He's a McGill guy, ran it at Harvard for years, just left a few months ago to Chicago, where at another big hospital in Chicago, where his job there is to implement best practices for breast surgery across the entire network. So he's been attached to the company from the early days. We just brought him on as a formal Chief Medical Officer. And he's been wonderful, just being able to be out there to kind of lend his name and his resume to a company like ours. Carl's also here, engineering from the early days. Abbey Goodman is our salesperson. Good experience growing that med device companies from $0 to $10 million-plus revenue per year. This is the phase we're in right now, which if you know about small little startup companies, it's a phase unlike any other. So you need someone who's been able to navigate that well. S o she's here. T hen Paolo Di Pasquale, who's sitting right over there, he's running our capital markets. Comes from Canaccord, Vancouver, born and bred guy, down in Dallas. His wife is a breast surgeon. So he's got the inside scoop with our customers as well. She's a user of our device and participated in our trial as well. Here's our cap structure. So we are on the V. We are an OTC. We have an OTC ticker down stateside. We've got coverage by Leede Jones Gable, Paradigm, Raymond James. So you can read reports out there. Market cap's about $20 million. T hose are our major shareholders. Okay. H ere's a summary. Large market, very significant improvement over what's happening right now to something that everyone understands and probably has had them affected personally with a high gross margin attached to it, which we're just starting to penetrate. No one else can solve this problem anywhere near. I mean, really, no one else can solve this problem. So we're in the pole position. We're growing a current product very well. You can see some of the growth numbers there. Our next gen is almost to the FDA review. So that'll be up in the next couple of months. So we'll hit the market with that AI version of it. And this is the stage we're at right now. Really, we've got the tech, we've got the customer base, and we're growing sales and revenue. That's it. Any questions? Yes. Is that the surgeon who acquires and willingly helps? Any opinions or reports? Yeah. So it's not a radiologist. So it's either the surgeon or, frankly, a lot of times it's just an operator that can review it. So a lot, sometimes we'll have our own people in there doing the reviews. We don't have AI. With AI, it can be a nurse, like a floating nurse, anything like that, an X-ray tech. So hold on. I think there was a question back there and that'll come to you. I don't mind you asking. This happened. So first, I'm going to disclaim. Happened before I joined. The reason they went public was because it was an RTO. So there was a vehicle sitting on the exchange. S o they were trying to raise money. They were struggling to raise money for it based in Canada. There was no real U.S. presence. So, you know, this seemed to be the way to actually raise money. There was a vehicle they were able to merge this way. S o that was, I mean, that was a story to the RTO. Y ou're right. This is more early stage in a different world. This would still be a private company for the next couple of years. T hen it'll get bought out by a, you know. Look, there's another company that's trying to solve the same problem in a different way. Private, $200 million. Public. $20 million. Yeah. The last fundraiser, I put $1 million in because I'm like, this is like a huge dislocation. Got to do it. Yeah. See that thing, number two right there? That's the thing that you have to use one of those for every patient. Yeah. T hen that's the tissue, you know, the tumor goes inside that, and that goes on top of the machine. T hen the scanning takes place. T hen after it's done, you know, you wipe the machine down, you throw that out, and then you open up a new one for the next patient. Yeah. So the reoperation, like if you have to do a second surgery, it's actually much more than $10,000. It's, you know, it can be $20K in a very simple case and up to $100K if the woman says, you know what, I got a lumpectomy, it didn't work, just do a mastectomy. So it ranges quite a bit. The insurance company. It depends. Two different models. If it's in a value-based care system where the insurance company gives a hospital a fixed pot of money and says solve the problem, then the hospital has to eat it. If it's not, if it's in a fee-for-service program, then the insurance company pays for it and So we need to chase large insurance providers to say, yeah. Yeah. Yeah. Right now, our point of entry is with the surgeons, but the data we're collecting is exactly what the insurance companies want to see. It's like, let's see the performance. I'm like, are you really going to be able to reduce? N ow we're getting there, right? Because we've had 3,000 patients go through. We have a broad clinical trial that was successful, so this evidence building is exactly what they're asking for, which we're able to provide. Sales force and Dartmouth, undergrad plus successfully spot on the patient and the basis of all much more than competent than self-value. Yeah. So there's like, yeah, a little bit over $100,000 fixed and then everything else is variable on top of that. No, it doesn't take that long for them to be competent. We can, because we've got that pipeline of qualified leads already. They walk in the door not having to go and garner interest. They just have to go and move those leads to the pipeline. So at this point in time, first day on the job, they should be able to be closing and bringing revenue in within three to six months. That's going to be cash dependent. So if I had sort of the ability for me to absorb over the next, say, 12 months, I would hire about five new salespeople. I have one right there. Yes. So every scan uses one of those. Yep. Every patient uses one of those. Yeah. But the same thing. The the same thing. The scan uses like not 2-20 apps now. No. Well, after each patient, but each patient could have multiple scans on the same consumable. We're over $1,000 on those. Yeah. Yeah. I saw a question back there. They are. No, no. They're still there. They've been very interactive. We have something called Breakthrough Device Designation, which means our questions go to the top of the queue. We have something called Breakthrough Device Designation, which means our questions go to the top of the queue, number one. Number two, they're still working even through the shutdown because this is something that's paid by industry, not paid by the treasury. So any application in the FDA that was already in flight before the shutdown is continuing to move. What's not happening are new applications into the FDA. So there's been zero impact on us. We've already given them everything. Yeah. March, give or take a month. Well, those reductions at 3% rate right now is happening before AI, with no AI. So that's the value that you get right now, number one. Number two, I'm getting hooked off the, is that an, oh, absolutely from 20% to 3%? That's massive. That's huge. So that's the motivation. Plus then from our angles, hey, if you want the AI, you better be a current customer because we're going to service our current customers first. Okay. I'm getting yanked off the stage. I'm here all the rest of the day. Come find me, come find Paolo. Thank you.