On behalf of OTC Markets and our co-host, Zach, Small Cap Research, we are very pleased you have joined us for our quarterly conference. Our next presentation is from Perimeter Medical Imaging AI. Please note you may submit questions for the presenter in the box to the left of the slides. You can also view a company's availability for a one-on-one meeting by clicking "Book Meeting" in the top toolbar. At this point, I'm very pleased to welcome Adrian Mendes, Chief Executive Officer of Perimeter Medical Imaging AI, which trades on the OTCQX Best Market under the symbol PYNKF and on the TSX Venture Exchange under the symbol PINK. Welcome, Adrian. Thank you, Greg. It's lovely to be here. Thank you to everyone else for watching this webcast. I'm excited to share with you what we're doing here at Perimeter. Let's jump right into it. Our goal is we're focused on cancer surgery, and our goal is to transform cancer surgery with advanced visualization and AI. I will be making some forward-looking statements in this presentation, and we'll be talking about two different products, and these are their different indications for use. This is the problem that we're addressing. A cancer patient or a cancer surgeon has to say these words to a patient of, "We didn't get it all." It's a conversation no surgeon wants to have with their patients. It's unfortunately the moment someone hears those words, they know exactly what it means, and it's an extremely traumatic and horrible thing for anyone to have to hear after they've gone through a cancer surgery. This is the problem we're trying to address, and I'll show you how we're approaching it. The goal with a cancer surgery is to get clean margins, what they call clean margins. If you look at the picture on the left side of the screen here, that's a clean margin. The way to visualize this is the best way to visualize this is imagine a hard-boiled egg where the yolk is cancer, and what you want is you want that cancer to be surrounded by a rim several millimeters thick of healthy tissue. If you have that, you have what's called a clean margin, a negative margin, and that is a successful surgery. On the other hand, if that margin is not at least 2 millimeters thick and some of those cancer cells are up against the edge of the tissue removed from the patient, you have a positive margin, and that increases the likelihood that there is some cancer left behind inside the patient. That is where those words, "We didn't get it all," come in. Okay, we are trying to help our surgeons achieve clean margins. This is not an easy problem to solve. It is very hard to feel cancer cells. It is virtually impossible. You cannot detect them by sight or touch. Traditional imaging, X-ray, ultrasound, CT scans do not have the resolution required to be able to see at the cellular level, to be able to differentiate between cancer cells and healthy cells. The end result of all that is that there is a very, very high percentage of surgeries, cancer surgeries, where the patient has to come back in for a repeat operation. You can see these numbers at the bottom here: 23% for breast cancer, 12% for thyroid, 21% for prostate. This is the percentage of patients that have their first surgery, and then they hear those words, "We did not get it all," and they have to come back into the operating room for a second surgery. It is a really big problem. It covers all the different cancer types to varying degrees, but very, very large in all of them. Here's a little bit more data about that. The left-hand graph shows the top eight cancers and how much money is spent each year in just the U.S. on those repeat surgeries. Never mind the first surgery, just the repetition of having that surgery that was unsuccessful the first time. Over $1 billion across, again, just these eight cancer types. The quantity is very large. The bars on that left-hand graph is how many repeat surgeries are done per year, or how many surgeries are done per year. As you can see, breast cancer, which is the biggest bar there, is the largest number of surgeries, but still quite a number of surgeries account for the other types of cancer. We are starting, most of our customers are in the breast space, and that's because that's the highest volume. What's perhaps even worse is the five-year survival rate, which is the right-hand graph, how it decreases quite significantly in some cases if that first operation is not successful. With breast cancer, the five-year survival rate is quite high, naturally, at 90% if the surgery is successful, but it falls all the way to 80% if it is not successful. Other types are even worse. Head and neck cancer, as an example, has a success rate or a five-year survival rate of 55%. If you don't have success for surgery, that falls all the way down to 10%. This is an economic problem, but it's also a health problem, just a fundamental survival rate problem as well. Being able to solve this problem has benefits across the board. Here's an example of some impacts of it. From the patient's perspective, there's a very large increase in post-operative complications after repeated surgeries. The risk of infection goes up from having to come back into the hospital again. That's a large 66% increase in these complications. There's also a delay in treatment. If there's radiation or chemo that has to happen after the surgery, that has to be delayed if there's repeat surgery. One of the reasons why patients get a lumpectomy versus a mastectomy is to preserve cosmesis. That, of course, is compromised if there needs to be a repeat surgery. Then just the emotional trauma of having to realize that you have to go back under the knife again because the first surgery wasn't successful. From the hospital system standpoint, there's a huge resource constraint already in hospitals today: shortage of operating rooms, nurses. Having to bring a patient back in to repeat a job that unfortunately wasn't done right the first time is a big problem. It puts a lot of strain on the system. It goes without saying that there's a big economic cost to bringing those patients back, $20,000-$50,000, depending on the type of surgery that is repeated. Really, across the board, being able to solve this problem has benefits in multiple different dimensions. Okay, let me talk a little bit about how we're attacking this problem. We have an imaging technology called optical coherence tomography, OCT. It's a very high-resolution imaging technology. It's able to see cellular-level detail because the resolution of it is so much better than X-ray, MRI, and other types of imaging. It can also image several millimeters into the tissue, which, of course, is required to be able to inspect that margin down to 2 millimeters of depth. There are no injectables. It's non-invasive. It doesn't come in contact with the patient's body at all. It just comes in contact with the tissue after it's been removed from the patient. It's extremely safe. What's key about this is that the size of cells, both cancers and healthy cells, are much smaller than what can be seen by X-ray, but bigger than what can be seen with OCT. This makes optical coherence tomography really the ideal type of imaging technology to look at to help solve this problem. OCT as an imaging technology is in widespread use. Probably everyone who's watching this has had an OCT image of their retina taken at the ophthalmologist. It's also used in cardiovascular applications. Both of those are a fairly small area that needs to be inspected. If you think about an eye, a retina, it's within 1 centimeter. The traditional OCT is able to take pictures of about that area. What we needed to do at Perimeter was make the imaging field a lot bigger. We expanded it out. We've got something called wide-field OCT, which is ours, which allows us to image a piece of tissue up to 10 cm square. It's actually round, so 10 cm by 10 cm diameter image, which then allows the surgeon, no matter how big the excised tissue is, as long as it's within a 10-centimeter diameter, to image the whole thing in one shot. That is proprietary technology, and that is how we were able to take OCT that really is in widespread use today and repurpose it for this application. What we do with that is we then provide it to the surgeon who can use it in real time in the operating room. Now, what's very important to understand here when using imaging technology like this is how well does it correlate to what the final decision point is in a procedure like this, which is pathology. What we're showing here is that correlation. The top picture, the black and white picture, is what the surgeon sees inside the operating room. The bottom picture, the purple picture, is what the pathologist sees when they look at it under the microscope. What's important here is to see that that big purple spot in the pathology slide is in the same place and in the same position as the white circle in the OCT image at the top. That shows correlation. What that provides is confidence to the surgeon that what they're actually seeing in real time during the operation is what pathology will see 7 to 10 days later when they look at their sample underneath a microscope. This is very important to gain the confidence of the surgeons using the product. This is what the product looks like. There is the cart, which is item number one there on the left-hand side. That sits inside the operating room, just outside the sterile field, but close enough for the surgeon to use and to see. The second component is what we call a specimen immobilizer. That is a container, basically, that affixes to the top of the machine. Inside the container is where the tissue goes. It is then scanned from the bottom, kind of like a photocopier. That is one per patient, one use per patient. Every time the machine is used, the surgeon has to open up a new packet and use a new container. Number three is proprietary imaging atlas. This is an imaging device. It takes pictures. We have a very, very large library, over 2 million images of tissue, both cancerous and healthy tissue. With that, we use today with our FDA-cleared device, the S-Series, we use that to train the readers. We use it to train all our human readers, including the surgeons. We have a next-generation device going through an FDA PMA approval today, which adds an AI algorithm to the system. What that allows is a greater ease of use. We have trained that algorithm on our image library, and it helps provide a sort of look-here guidance system to our surgeons to improve ease of use inside the operating room. To talk about how our solution fits as compared to the current standard of care, we will start with the standard of care, which is the horizontal sort of section there at the bottom of the screen. Of course, the operation starts with a resection, a removal of the tissue, which then gets sent down to pathology, which takes up to 10 days for the pathologist to put it under the microscope and look at it. There is some work that needs to be done to the tissue before that. In the meantime, of course, the patient is closed and sent home and is waiting for a response to find out from the surgeon if that surgery was a success or not. Over those 10 days, of course, there is a lot of stress waiting for that. The patient cannot really do post-operative therapies. As you saw in one of the earlier slides, there's a high percentage of times where the surgeon comes back and says, "Unfortunately, we didn't get it all, and we need to do another operation." Compare that to our solution at the top, where, again, it starts with the surgeon removing tissue, but then in real time, the tissue's scanned inside the operating room while the patient is still on the table. Based off of what the surgeon sees on the screen, they can take further action and take a bit more tissue, put it on the scanner, and look at that tissue. When they're satisfied that they've gotten everything, they can then close the patient and send them home. They do that. They send them home with a high degree of confidence. They got all the cancer out of that patient. With that, the patient has some level of confidence. Ten days later, when they do get the report from pathology, there is a much greater chance that it will turn out to be a positive phone call as opposed to a negative phone call. We have a broad set of surgeons using the device right now. We have over 1,700 at this point. I think it is up to around 2,000 patients who have had a procedure done using our machine. We are seeing some very good success rates. These surgeons are really happy, and they continue to—we see our customer base growing because surgeons will—this is a big problem for them in their practice. They like to understand how their colleagues are using it. As word of mouth gets out there, there is a very, very strong uptake on this tech. Here is a—so what this is, is a study we did with one of our surgeons with the first 72 patients that she used when she first got the machine. She's up to several hundred at this point. What you see is what the national average in the blue bar there is for breast cancer reoperation rates, which is around 20%. With these 72 patients, what this surgeon was able to get to was down to a 5.6%, a 75% reduction in reoperation rates, which is a really, really big difference for her. It gives her much more confidence when she's closing out her, closing the patients and walking out of the room in her conversation with them. That's 15% of her patients that don't have to come back for another operation. This is a white paper. It's on our website. You can take a look at it that shows how we analyze this data. Of course, we've got close to 2,000 patients that have gone through our technology. We're seeing even better numbers as we look at that data internally, which we're preparing to publish over the next little while. This is digging it down one more step deeper into the different types of cancers. There are two main types of breast cancer. One's called DCIS. One's called IDC. I think the main takeaway on this graph is even for both those types of cancers, more advanced and more early-stage cancers, using the OCT machine does have a drastic improvement on the normal re-excision rates. In one case with those 72 patients, there were actually no reoperations with IDC, which is the more advanced stage of cancer. A really, really great result from this surgeon with her patients using the OCT product. Okay. Now let me move from the product that's in market right now to our next-generation product, which is basically the same thing that I've shown you so far, but with that AI layer on top of it. We have our technology. We've created our image library. We've got an AI algorithm, which, of course, we can iterate by adding more images to it, by upgrading the algorithms, by making it better and smarter. All of that results in a much easier tool for surgeons to use, which then results in two things. One is less time in the operating room while the patient is under anesthetic, which is obviously beneficial for that patient to be under less. The second is that it makes it much easier to adopt this technology for new surgeons because they have the look-here guidance. I'll show you a picture of what that looks like on the next screen. As we move through this slide, the leftmost picture is what a user sees today when using our device. It's our S-Series image. There is no A on that, and they see that image. A typical patient has 500, 400, 900 images when it's scanned. The surgeon has to flip through all of those images to identify whether they see something suspicious or if those margins look clean. They can go through fairly quickly. You can do it like a flipbook movie, so you can kind of move things like a movie. It does take manual review in that way. What is undergoing FDA approval today is what you see in the middle of the screen, where the algorithm inspects all the images in real time, and it puts those little red marks above those things it finds to be suspicious. When the surgeon's flipping through the images, they can see those red marks, and they can say, they can make a judgment. Does that look like cancer? Do I believe what the AI is telling me or not? Better than that, what the interface does is it actually puts up thumbnails. Rather than having to scan through all the images, the new software will put up half a dozen, maybe a dozen thumbnails. The surgeon can look at those either in thumbnail form or click into it to inspect more deeply. This is what's going through FDA approval right now, and hopefully, we'll be in market by the end of the year. Of course, we already have future evolutions of this in development. That's what you see on the far right, which is our version 3.0, where we will move to drawing a box around the suspicious features, which then just makes it much, much more easy to use. Of course, through this entire evolution, we're increasing sensitivity, increasing specificity, just making the AI much more accurate as we add more data and as we make the algorithms more sophisticated. We completed a trial, a clinical trial in November of last year, which we successfully hit the endpoints. That is what's feeding into the FDA approval process right now. As you can see, there were a lot of very top cancer centers that were involved in this trial. This is a high-profile device within this is a big problem. Every surgeon understands this problem and is trying to figure out ways to solve it. There is a lot of interest within the community for this trial and to participate in it and to help to see if this thing can be useful for them, which it was. We did pass the primary endpoints, and that is what fed into our PMA submission that we submitted back in March. Here are some of the economics of this business. I have talked about the problem. I have talked about the technology. I have talked about how the technology has gone through clinical trials and is proving its worth and how we have customers out there using this already. If we look at the market as a whole, and if we just say, "Let's just take the United States alone, and let's just take breast cancer and none of the other cancer types," and we say, "If we can reduce the re-excision rate from about 20% to around 5%, what does that do? What kind of savings does that help? What benefits does that bring to the whole system? You can run the numbers out, and you can say, "If it costs an extra $17,000 per reoperation and you can save 28,000 patients, which is a lot, 28,000 patients from having to do a reoperation, there's almost $500 million, almost half a billion dollars in healthcare savings to the system alone just based off of that reoperation." That does not take into account any of the additional benefits to the patients or to the constrained resource within the hospital where those operating rooms and nurses could be using more profitable surgeries. It does not, of course, take into account the fact that you're able to reduce or increase, I guess, that five-year survival rate by it being successful in the very, very first surgery. A massive economic benefit just in this very, very small starting point that we have for the company. Then from there, this can obviously expand, right? Not only is it good for breast cancer, it's good for really any sort of solid-state cancer and then around the world. If we look at just within the United States, we're looking at a $4 billion market just for surgeries and for biopsies. There's application for this in biopsy as well. If we start with that $0.5 billion SAM that we've got right now for biopsy and for surgery, that expands very, very quickly with the tech that we've already got developed with very little additional R&D required. Okay. What's our pathway? If I looked at last year, we were running the trial, which we completed towards the end of the year. We were getting traction on the S-Series, and we continue to do that this year in 2025. We are expanding our customer base on the current device, the S-Series. We are getting early adopters. We are expanding geographically within the United States. We filed our PMA application. We are creating strategic partnerships with big hospital groups across the country. All this is leading up into early next year, late this year, where we will hopefully get PMA approval, FDA approval on the next-generation device. With that very, very strong platform of surgeons that we have on the current device, we will be able to launch the B-Series on a position of strength into much more widespread adoption. With that, we will also be able to really accelerate our advance into different types of cancers beyond breast cancer and further those strategic partnerships. This is the path we've got over the next couple of years. Almost halfway through the year this year, we're well on track with this plan. This is the team. We've got a very well-experienced team, I'll say, across the board from many, many different companies, technology companies, healthcare companies, medical device companies. This is the team that's bringing this to market. A quick note on our IP. We are a technology company. Our IP is very well patented and protected both from a patent standpoint as well as trade secrets, as well as our image library. One thing I'd like to just mention there, the value in AI companies is not so much in the algorithm, but it's in the data library. The nice thing about this company is one of the reasons I joined this company is because the images that we have are owned by us and cannot be replicated because we have the only camera that can take those images. It gives us a very, very strong moat around our AI that has a lot of value within it. Here is a quick look at our cap table. We are listed on the OTCQX under PYNKF and on the TSXV Exchange also under PINK. We are followed by three firms, three analysts at three different firms: Leede Jones Gable, Paradigm Capital, and Raymond James. We have two major shareholders, Social Capital, Master Holdings, and Rocco Schirelli at 31% and 12%, respectively. Okay. In summary, we have a very large market that we're addressing, a very high gross margin, recurring revenue model by selling those one-time usable disposable containers. Very, very good positive reception from our early adopters on the current device, the S-Series. We have our next-gen AI-enabled device that is going through FDA approval at this point. It met its primary endpoints with statistical significance in the trial that wrapped up last year. We're optimistic of getting that through the approval process, hopefully not too far in the future. With that, that allows us to bring AI into market and very much help bring this tech to as many patients as many surgeons are out there in the cancer space. That is the summary of Perimeter. Okay. Let's see the questions that have come in. Okay. Here's the first one. You had a strong Q1. Do you expect that to continue this year? Yes. Yes. We have momentum going into this year. We did have a strong Q1. We were up 88% versus Q4 and around 400% versus the first quarter of last year. There is a little bit of seasonality in our business. As we look at the pipeline, as we look at where we are quarter to date, I do expect that to continue through the rest of this year. When do you expect approval of the PMA for the next-gen AI device? We submitted that application in March, and it is moving through the process. There are multiple interaction points with the FDA through a process like this. It is moving through the FDA process at a fairly good clip. Right now, we're projecting us to get approval sometime around the end of the year, plus or minus a month or two. We're running the business, running towards that goal towards the end of this year to get approval on that device. Okay. What's next here? After breast, what do you think the next cancer targets are for the wide-field OCT technology? That's a great question. Like I said, we're starting with breast cancer. That's our beachhead market. From there, we actually have surgeons that are currently looking at different types of tissues. We have captured with a surgeon that works with head and neck cancers. We've captured images from them, and we're pulling that together into a paper. We have a current surgeon right now who is imaging melanoma samples. That's looking very interesting. Upcoming here in the next few weeks, we're going to announce yet another tissue type that we'll be bringing on board in the next few weeks with a brand new surgeon. We're looking forward to that. Very excited to expand this into different market types. Okay. Perimeter is trading on the TSX Venture and recently moved up to OTCQX. Are there any plans to graduate to the TSX Big Board and/or uplist to NASDAQ? Yeah, the answer is yes. That is in our roadmap, our capital markets roadmap. Timing is always very hard to sort of predict definitively, but we continue to look at that. We continue to grow the business. Our ultimate goal is to be able to uplist to TSX and NASDAQ. All right. That looks to be all the questions. Thank you very much.